Introduction

The artificial intelligence (AI) consulting landscape is experiencing significant growth, reflecting the broader expansion of the global AI market for AI consulting services. The AI consulting services market is expected to grow from $11.07 billion in 2025 to $90.99 billion by 2035, exhibiting a CAGR of 26.2% during this period. This analysis presents leading AI consulting companies of 2025, highlighting their unique capabilities and value propositions for businesses seeking expert AI guidance.

The Evolving Landscape of AI Consulting

The artificial intelligence consulting ecosystem has undergone remarkable transformation in recent years, evolving from theoretical advisors to comprehensive implementation partners. Modern AI consultancies now deliver end-to-end services spanning strategy development, solution architecture, implementation, and ongoing optimization. This evolution has been driven by growing enterprise demand for practical AI applications that deliver measurable business impact rather than experimental capabilities.

The most significant shift has been toward industry-specialized AI expertise, with leading consultancies developing deep domain knowledge in specific sectors. This specialization enables them to create contextually relevant solutions addressing unique industry challenges rather than generic implementations requiring extensive customization. The resulting solutions integrate seamlessly with established workflows while leveraging industry-specific data patterns and compliance frameworks.

Another critical development has been the growing emphasis on responsible AI practices addressing concerns around ethics, transparency, bias mitigation, and governance. Elite consultancies now incorporate these considerations throughout their implementation methodologies, ensuring solutions meet both performance objectives and ethical standards. This balanced approach has become particularly important as AI systems increasingly influence consequential decisions across business and society.

Strategic Considerations for AI Consulting Partner Selection

Selecting the optimal AI consulting partner represents a critical decision with far-reaching implications for implementation success and long-term value realization. Beyond technical capabilities, several essential factors determine compatibility and ultimate outcomes, including the evaluation criteria outlined in strategic AI consulting and custom development.

Evaluating Technical Depth and Business Understanding

Effective AI implementations require partners who combine technical sophistication with practical business understanding. Elite consultancies demonstrate not just theoretical knowledge but pragmatic implementation expertise addressing the complete solution lifecycle from initial concept through ongoing optimization. Their technical teams maintain proficiency not just in algorithms but in the entire development ecosystem including data engineering, deployment architecture, and operational monitoring.

The most valuable partners balance technical innovation with practical reliability, incorporating appropriate advances without pursuing novelty for its own sake. This balanced methodology maximizes value while minimizing implementation risk.

Assessing Industry Knowledge and Implementation Experience

Domain-specific knowledge provides essential context for effective AI implementations, with the most valuable partners demonstrating deep understanding of industry-specific challenges, workflows, and success criteria. This specialized expertise allows them to anticipate common pitfalls, leverage established patterns, and align solutions with regulatory requirements and standard practices.

Elite consultancies maintain comprehensive knowledge not just of technical factors but of the complete business ecosystem in which solutions will operate. They understand stakeholder priorities, organizational dynamics, and adoption considerations that determine ultimate success beyond technical functionality. This holistic perspective enables them to design solutions that address the complete implementation context.

TOP AI Consulting Companies

After extensive research and analysis of market, emerging innovators, and specialized boutiques, we present notable AI consulting companies for 2025. Each organization brings distinctive capabilities and specialized expertise addressing specific business needs and implementation scenarios.

1. 8allocate

Founded: 2015
Location: Estonia, Poland, Ukraine, Romania
Employees: 100+

​8allocate stands out as an AI solutions development company committed to delivering practical business outcomes through advanced AI technologies. Founded in 2015 and headquartered in Tallinn, Estonia, with R&D centers across Central and Eastern Europe and Latin America, 8allocate combines technical expertise with pragmatic implementation to create AI solutions that provide immediate value and support continuous evolution.​

The company’s comprehensive approach encompasses strategy development, implementation, and continuous optimization. This methodology begins with thorough business analysis to identify specific opportunities where AI can maximize value, ensuring solutions address genuine business needs rather than implementing technology for its own sake.​

By aligning AI initiatives with business objectives, 8allocate ensures that AI strategies are both goal-driven and technically viable, providing clients with a strategic advantage in their respective markets.

2. Markovate

Founded: 2015
Location: India
Employees: 50 – 250

Markovate helps businesses harness AI technology advancements to enhance productivity and operational efficiency. With over a decade of experience serving organizations across multiple sectors, they deliver transformative solutions spanning diverse industry contexts.

What distinguishes Markovate is their strategic partnerships with major technology platforms including AWS Partner Network, Google Cloud, Microsoft, and Databricks. These collaborations enable them to leverage industry-leading technologies while maintaining focus on business outcomes rather than specific technical approaches.

Their technical expertise spans generative AI, chatbot development, LLM implementation, computer vision, and MLOps, creating comprehensive capabilities for guiding businesses through complex digital transformation journeys.

3. Azati

Founded: 2002
Location: Poland
Employees: 250 – 999

Founded in 2001 with headquarters in the USA, Azati has evolved into a trusted AI consulting firm with a dedicated R&D center focusing on continuous innovation. They specialize in delivering cutting-edge AI solutions tailored to the specific requirements of both startups and enterprise clients.

Azati’s expertise spans the complete solution development lifecycle from design through AI development, data science, and machine learning. Their end-to-end consulting and development services enable clients to navigate the complex AI landscape with confidence, accessing specialized expertise without requiring extensive internal capabilities.

4. Faculty.ai

Founded: 2014
Location: UK
Employees: 201-500

Faculty.ai delivers comprehensive AI services spanning strategy development, solution implementation, and governance frameworks for diverse industry verticals. Their service offerings include machine learning implementation, AI strategy development, and data science solutions across healthcare, financial services, and public sector organizations.

The company distinguishes itself through its approach to solving complex problems using specialized AI technology subsets, proprietary platforms, and strategic partnerships with leading technology providers. This multi-faceted methodology enables them to create tailored solutions optimized for specific business requirements rather than generic implementations.

5. Cambridge Consultants

Founded: 1960
Locations: USA, UK, Singapore, Japan
Employees: 800+

Founded in 1960, Cambridge Consultants has established itself as a pioneer in AI consulting through its unique approach blending innovation with practical applications. The company delivers tailored solutions across diverse industries including healthcare, retail, and manufacturing, maintaining a global presence serving organizations worldwide.

With over 800 specialized experts and offices across the USA, UK, Singapore, and Japan, Cambridge Consultants maintains the global capabilities necessary to support multinational implementations while providing specialized expertise for specific regional contexts.

6. Fractal Analytics

Founded: 2000
Location: USA
Employees: 600

Fractal Analytics has established itself as a leader in AI-powered business solutions through over two decades of focused expertise in data science and artificial intelligence. Their comprehensive capabilities span strategy through implementation and ongoing optimization, providing clients with end-to-end support throughout their AI transformation journey.

What distinguishes Fractal is their specialized industry expertise in sectors including consumer packaged goods, insurance, healthcare, and financial services. This vertical focus enables them to develop deep contextual understanding of sector-specific challenges and opportunities, creating more relevant and impactful solutions than generalist approaches could achieve.

7. Quantiphi

Founded: 2013
Location: India
Employees: 1000+

Quantiphi has emerged as a distinguished AI consultancy through their unique approach combining deep technical expertise with industry specialization. Their comprehensive capabilities span machine learning, computer vision, natural language processing, and conversational AI, enabling them to address diverse business challenges through appropriate technological approaches.

What distinguishes Quantiphi is their focused vertical expertise in industries including healthcare, insurance, media, and education. This specialization enables them to develop deep contextual understanding of sector-specific challenges and opportunities, creating more relevant and impactful solutions than generalist approaches could achieve.

8. InData Labs

Founded: 2014
Locations: USA, Cyprus, Lithuania
Employees: 80+

InData Labs is a data science firm and AI-powered solutions provider dedicated to addressing complex challenges in artificial intelligence. Founded in 2014, the company operates with a team of over 80 professionals and maintains its own R&D center. Their services enable clients worldwide to gain valuable insights from data, automate repetitive tasks, enhance performance, incorporate AI-driven features, and prevent cost overruns. By leveraging their expertise in big data analytics and AI, InData Labs assists businesses in transforming raw data into actionable strategies, ensuring solutions are both innovative and aligned with specific business objectives.

9. LeewayHertz

Founded: 2007
Location: India
Employees: 50 – 249

LeewayHertz is a software development company specializing in innovative AI solutions for startups and enterprises, facilitating their success in the rapidly evolving digital landscape. Established in 2007, LeewayHertz has a significant presence in the technology sector, offering services that encompass AI development, blockchain consulting, data engineering, and digital transformation. The company has been recognized for its ability to integrate advanced technologies seamlessly into business processes, thereby enhancing operational efficiency and customer engagement. Their portfolio includes the development of platforms like ZBrain.ai, which provides seamless integration and processing of applications, catering to various industries such as healthcare, manufacturing, finance, and education.

10. Deeper Insights

Founded: 2014
Location: UK, Portugal
Employees: 50

Deeper Insights is a UK-based data science and AI consultancy that specializes in transforming unstructured data into actionable intelligence. The company focuses on helping organizations harness the power of AI to solve complex problems, improve decision-making, and drive innovation. Their expertise includes natural language processing, machine learning, and advanced analytics, enabling businesses to extract meaningful insights from vast amounts of data. Deeper Insights works across various sectors, providing tailored AI solutions that align with specific industry needs and challenges.

Emerging Trends in AI Consulting

The AI consulting landscape is evolving rapidly as technology and business needs change. Here are some key trends in 2024-2025 that top AI consulting companies are focusing on:

Generative AI and LLMs

The explosion of generative AI (such as GPT-4 and similar large language models) has consultancies very busy. Many firms are now building services around Generative AI integration, helping clients deploy AI for content creation, code generation, design, and more. Several leading consultancies shifted focus toward generative AI solutions, including creating custom AI chatbots, AI-driven design tools, and domain-specific generative models for clients.

Responsible & Ethical AI

With AI systems becoming more powerful, there’s a strong emphasis on ethical AI and governance. Top consulting firms are helping organizations implement AI with proper oversight – addressing issues like bias, transparency, and regulatory compliance. This trend includes developing AI ethics frameworks and risk mitigation strategies so that AI solutions are fair and safe.

Scaling AI Across the Enterprise

Early AI projects often start as pilots; a current trend is moving beyond pilots to scale AI enterprise-wide. Businesses want to embed AI in multiple departments and workflows (from operations to customer service), and consulting firms are instrumental in this scaling process. They bring methodologies to integrate AI into legacy systems and to manage change within organizations.

Industry-Specific AI Solutions

Another trend is the demand for tailored AI solutions by industry. Rather than one-size-fits-all, companies want AI that understands their domain (be it banking, retail, manufacturing, etc.). AI consultancies are responding by developing deep industry expertise – for example, offering AI models pre-trained on healthcare data or solutions specialized for supply chain optimization in manufacturing.

Conclusion

The AI consulting landscape is rapidly evolving, offering substantial opportunities for businesses ready to embrace innovative technologies. Selecting the right consulting partner is essential—not only to harness AI’s immediate benefits but to build long-term competitive advantages through strategic implementation. As you evaluate potential AI partners, consider their ability to align technical capabilities with your specific business objectives and their commitment to responsible, ethical AI practices.

To successfully navigate this rapidly changing environment, organizations should prioritize consultancies that demonstrate deep domain expertise, practical implementation experience, and the ability to scale AI effectively across their operations.

Interested in discovering how your organization can benefit from advanced AI solutions? Reach out today to discuss your specific challenges and opportunities and start shaping the future of your business with AI.

Frequently Asked Questions About AI Consulting

Quick Guide to Common Questions

What is AI consulting, and why does it matter?

AI consulting is the specialized practice of helping organizations conceive, develop, and deploy artificial intelligence strategies that align with business objectives. In today’s rapidly evolving market, AI-driven transformations can sharpen competitive advantage, streamline operations, and open new revenue streams. By partnering with expert advisors, companies ensure that these powerful technologies address real challenges and deliver tangible results rather than remain theoretical promises.

How can AI consulting services benefit businesses?

Engaging AI consultants reduces implementation risks, accelerates time-to-value, and provides access to the latest innovations without the need for extensive in-house expertise. An experienced consulting team collaborates with various departments—from data engineering to executive leadership—to orchestrate AI initiatives that simultaneously address operational efficiency, customer experience, and compliance requirements.

Why is domain specialization increasingly important in AI consulting?

Successful AI projects go far beyond coding algorithms; they also demand nuanced understanding of industry regulations, customer behavior, and data patterns. A consulting partner with domain-specific expertise can design AI solutions that account for real-world workflows and limitations, ensuring the technology fits seamlessly into existing processes. This specialization yields faster results, more effective risk management, and greater buy-in from stakeholders who see that the AI aligns with their unique industry needs.

What trends are shaping AI consulting in 2025?

Generative AI and large language models remain center stage, driving revolutionary applications in content creation, design, and customer interaction. Meanwhile, Agentic AI—advanced systems capable of autonomous decision-making and self-guided adaptation—gains momentum, prompting consultancies to explore new governance frameworks and ethical considerations for more independent machine intelligence. Responsible and ethical AI practices continue to be a core priority, as businesses seek methods for bias detection, transparent decision-making, and regulatory compliance. Consultants are also increasingly focused on helping organizations scale AI enterprise-wide, ensuring that deployments expand beyond small pilots into multi-departmental rollouts. Finally, industry-specific AI solutions are more in demand than ever, with specialized models tailored to the unique challenges of sectors such as healthcare, finance, and manufacturing.

How does responsible AI factor into consulting engagements?

Today’s AI initiatives face heightened scrutiny around fairness, accountability, and compliance. Consultants address these concerns by incorporating established ethical frameworks, rigorous bias detection protocols, and transparent model governance. This approach fosters trust with consumers, regulators, and business leaders alike—making AI not only a source of efficiency but also a tool that operates within ethical boundaries.

What is the difference between pilot projects and enterprise-scale AI solutions?

A pilot project typically explores AI’s potential on a smaller scale, focusing on proof-of-concept demonstrations and immediate insights. In contrast, enterprise-scale solutions embed AI across multiple functions and departments, touching customer service, supply chain, marketing, and beyond. The latter requires robust infrastructure, comprehensive change management, and strategic leadership to ensure AI becomes an integral, sustainable component of daily operations.

How do you choose the right AI consulting partner?

Many factors come into play. First, look for a firm with both technical prowess and the ability to translate AI’s capabilities into meaningful business strategies. Second, confirm that they have proven experience in your industry or a track record of solving similar challenges. Third, inquire into their commitment to ethical AI—whether they have frameworks to prevent bias, ensure compliance, and maintain transparent model development. Finally, strong project management and post-deployment support can make the difference between a one-off success and a sustained competitive advantage.

Companies are pressed to release new software faster to navigate the markets’ ebbs and flows. However, time pressure can adversely affect software quality and create major operational risks. So can development teams be more productive and deliver better software at the same time? The new paradigm of platform engineering aims to make that happen. 

Platform engineering is one of the hottest software engineering trends this year focused on one precise goal: helping developers build better code. In this post, you’ll learn what exactly platform engineering is and why 80% of larger software development teams plan to adopt it by 2026. 

What is Platform Engineering? 

Platform engineering is an operational practice of creating and maintaining standardized workflows and toolchains to improve developer experience and productivity. It borrows DevOps principles — automation, collaboration, and continuous improvements — and aims to scale it across the entire organization. 

The best software engineering happens in the flow state — a streak of deep work. But it’s hard to dive into work when bombarded with ancillary tasks. Developers spend only 32% of their time writing or improving existing code and roughly 70% on maintenance, testing, management, and operational tasks. 

The goal of platform engineering is to eliminate blockers and interruptions in development through a combination of cultural, operational, and tech practices.

Source: 2024 State of DevOps Report.  

Platform engineering teams aim to create a better developer experience (DX) — an environment where everyone is empowered to do their best work with minimal disruption and maximum efficiency. 

Responsibilities of a platform engineering team: 

• Create and maintain internal development portals (IDPs)
• Build and support developer toolchains (custom scripts, reusable APIs, etc.) 
• Handle container runtimes and deployment 
• Maintain continuous integration (CI) and continuous deployment (CD) pipelines 
• Configure and monitor infrastructure as code (IaC) platforms 
• Create Golden Paths for performing standard coding tasks 
• Produce and maintain engineering documentation 
• Help establish secure engineering practices 
• Evaluate and experiment with new technologies 

Fundamentally, product engineering teams focus on creating better guardrails and tenets for software engineering teams, constantly seeking ways to improve team velocity, productivity, and happiness. Numerous studies prove that happy teams are more productive, work harder, and deliver more value for business. 

Platform Engineering vs DevOps: The Differences 

Platform engineering and DevOps are connected at the hip. DevOps practices like CI/CD, IaC, test automation, and observability are the baseline frameworks for platform engineering. 

Platform engineering aims to scale DevOps by providing developers with self-service access to approved toolchains, homogenizing workflows across teams, and bringing a greater degree of efficiency and automation to existing practices.  

Perhaps one of your development teams already ranks high in DevOps maturity, but you struggle to replicate their setup to other teams due to skills gaps and talent shortages. Or you have several engineering teams at different stages of DevOps adoption, each using a slightly different CI/CD setup, custom scripts for infrastructure configuration and deployment, and a personalized toolkit. This results in technology sprawl, poor handover processes, and extra complexity in IT infrastructure monitoring, observability, and security, as there are no unified policies. 

Effectively, platform engineering aims to scale and standardize DevOps practices and deliver systems engineering as an internal product capability.

Problems Solved with Platform Engineering 

Fundamentally, platform engineering teams aim to accelerate software development productivity by resolving the common technological and organizational blockers that lead to long release times, compliance and security hurdles, low talent morale, and, ultimately, high attrition. 

Long Cycles for New Projects

To get ahead, you need to get started, and that’s often one of the hardest tasks. Platform engineering reduces the cognitive load software engineers experience when picking a new user story from the backlog by providing Golden Paths. 

Spotify, which coined the concept back in 2020, defines Golden Paths as an “opinionated and supported’ path to ‘build something’ (for example, build a backend service, put up a website, create a data pipeline). The Golden Path tutorial is a step-by-step tutorial that walks you through this opinionated and supported path.”

A Golden Path is a template, plus a set of recommended tools and technologies, for completing a coding task stored in the internal developer portal. The template can include:

• Skeleton source code  
• CI/CD pipeline template 
• Recommended Cloud IaC configurations 
• Kubernetes YAML files 
• Security policy guardrails
• Logging and monitoring tools 
• Reference technical documentation  

Thanks to Golden Paths and mature CI/CD practices, Spotify improved the average time required to setup a framework for building a website from 14 days to less than 5 minutes. The music service also onboards new developers 50% faster.  

Apart from faster task execution, Golden Paths also promotes code uniformity with existing quality, security, and compliance standards, leading to faster deployment times and fewer errors in production.  As  Samantha Coffman, senior product manager, Engineering Experience at HelloFresh, explains: 

Engineers look to Platform like they look at the technology radar. They look to Platform for what they should do next, and they really respect and admire the platform engineers as the gold standard for how they should be doing things.

Tech Stack Sprawl 

Developer tools are plentiful, but haphazard adoption magnifies operating costs and creates extra operational complexities. Instead of shipping new features, 40% of software engineers spend a quarter to half of their time on toolchain integration. 

Tool fragmentation across teams also creates integration issues and limits your ability to re-assign people from one project to another without spending extra time showing them the ropes. 

On the other hand, long approvals for new tool purchases or lax security policies promote shadow IT — usage of unauthorized development tools or non-approved open-source frameworks in projects. Left out of the monitoring perimeter, shadow IT components often become security liabilities. In 2022 over 5 billion malicious requests were targeted at unknown, unmanaged, and unprotected corporate APIs (and many of these were likely successful). 

Platform engineering aims to streamline access to technologies via self-service portals: Pre-approved, pre-made, and readily available scripts, frameworks, toolchains, and OSS components engineers can use in all projects. 

Gaps in System Security  

Cyber actors try to leverage every loophole to break into the corporate perimeter. According to the Data Theorem, 91% of organizations experienced a software supply chain incident last year. Specifically, 41% were targeted by zero-day exploits of vulnerabilities within third-party code, 40% faced an issue due to misconfigured cloud services, and 40% were targeted via vulnerabilities in open-source software and container images. 

These issues arise due to lax security policies, lack of monitoring, and security testing in the developer process. Platform engineering teams help improve built-in security and compliance in the software development lifecycle (SDLC) by introducing automated security as code policy enforcement, automated security testing, and pre-vetted code components, available via developer portals.

Source: 2024 State of DevOps Report.  

Frequent Deployment Failures

Frequent code deployments are almost synonymous with high product competitiveness. Users want frequent updates, fast bug fixes, and excellent application performance. Yet, software engineers struggle to keep up, especially if each code deployment requires extensive manual oversight and tinkering with infrastructure configuration.

Mature DevOps teams maintain a change failure rate of below 15% and need less than a day to recover from a failed deployment. While low-performing teams experience change failure in 64 out of 100 deployments.

Platform engineers help automate deployment and configuration management processes like compute runtime management and container orchestration. Many also handle infrastructure monitoring and maintenance tasks like load balancing, cloud resources provisioning, caching, and horizontal scaling to ensure high infrastructure availability and resilient system operations.  

Combining “shift left” practices (towards more secure, standardized coding) with automatic infrastructure provisioning, platform engineering teams substantially reduce the odds of failed deployments. To minimize failed deployment rates, Microsoft integrated policy fundamentals in its continuous integration pipelines. With this setup, all production releases, including bug fixes, are built from the main branch of source code and have consistent compliance steps. Effectively, teams must ensure that all pull requests have a successful build to keep the main branch always production-ready. Although this approach slows the time-to-production, it minimizes build failure rates, leading to greater end-product quality.  

Communication Silos 

Communication silo is inevitable in larger organizations with sprawling engineering team structures. Several operation units responsible for API management, network operations, and cloud infrastructure provisioning may only interact with one another through ticketing systems and manual processes. 

Multiple cross-functional teams run their version of DevOps — using different practices, security policies, and toolchains to power their CI/CDs. Although there are occasional experience exchanges, each team effectively runs as a “DevOps island,” causing friction in collaboration. 

Platform engineering aims to bring different teams into a shared ‘headspace,’ promoting a greater exchange of knowledge, standards, documentation, and operational policies, curated and disseminated via an internal developer portal.

Source: Christian Posta 

The best-performing platform teams don’t just standardize IT infrastructure but aim to create a stronger alignment between all units — engineering, infosec, DevOps, DevSecOps, networking, API teams, etc — to improve developer workflows and software delivery speeds. 

High Talent Attrition 

Over a quarter of developers quit their jobs due to poor engineering culture. Many are frustrated by toil —  manual, repetitive, devoid of enduring value work, and that scales linearly as a service grows. Others are disappointed by poor collaboration, lack of knowledge exchanges, and minimal support from the management. 

No matter the reasons, the outcome stays the same: Your tech talent grows disengaged, becomes less productive, and eventually throws the hat. 

The end goal of platform engineering is to improve developer experience, aka make it easier and faster to perform regular tasks, access new knowledge, and collaborate with colleagues. Over half of internal developer portal adopters say it eliminates redundant work. Other frequently cited benefits include reduced cognitive load and better cross-functional collaboration. 

How to Establish Platform Engineering Practices 

Platform engineering aims to productize your approach to building and maintaining engineering infrastructure. Effectively, it meshes the principles of product engineering — building with the end user in mind — with DevOps practices of continuous automation and toil elimination. 

To start building your platform engineering capabilities, we recommend the following practices: 

Develop a Developer Journey Map

Platform engineering builds and maintains products for developers — internal developer portal, effective CI/CD process, and ancillary workflows for infrastructure provisioning and management. 

The best products fulfill specific needs and address common frustrations. So your first goal is to create a developer journey map to understand better which bottlenecks they experience. Generally, developers want to see the following features in an IDP:

Source: Cortex 

Talk to different teams and managers about common frustrations. Document these as user stories and evaluate the best ways to address these (e.g., with Golden Paths, via targeted upgrades to CI/CD, etc).  Appoint a platform owner (an equivalent of a product owner) to set the vision and manage the roadmap.  

Start With an MVP Version 

Great products aren’t built with the first interaction, and neither are developer platforms. Prioritize a minimal viable set of features to launch an MVP internal portal and then progressively populate it with extra goodies based on the users’ feedback. 

To release faster, identify and leverage existing solutions (tools, scripts, reference architectures, etc) teams are already familiar with and enjoy using. Opt for flexible technologies that can be adapted to different development processes and automation scenarios to avoid unnecessary sprawl. Review well-known, open-source components with a good degree of customization and look into more purpose-built tools during later interactions. 

This way, you can start the initiative with a lean budget and demonstrate value early to secure user buy-in and justify a larger initiative budget. 

Analyze and Scale Your Platform 

Collect user feedback after the MVP release to understand what’s working and what’s not. Ask open-ended questions to learn how engineers are using the platform, what features they like, and what they are missing the most.

Build an internal public roadmap and solicit feature submissions and upvotes to prioritize new features for development. Platform engineering adopters who practice user-centrism have 40% higher organizational performance and 20% higher job satisfaction rates. Measure the early impact and benefits by tracking metrics like sprint burnout rates, velocity, change failure rates, and failed deployment rates to demonstrate the value of the initiative. 

Final Thoughts 

Platform engineering aims to reduce project cycle times and deployment failure rates by addressing common stressors in the software engineering processes. Rather than directly contributing to new software development, platform engineers focus on creating an environment where others can do their best work. They use feedback from developers to add new features and toolkits to self-service portals, improve CI/CD pipelines, optimize IaC platforms, and ensure proper application performance in production. 

Scale DevOps and establish platform engineering best practices with 8allocate. As part of our technology consulting service, we’re helping our clients improve their engineering practices, make progress on the maturity scale, and get in the green on tracked software development productivity metrics. Contact us to learn more. 

Frequently Asked Questions

Quick Guide to Common Questions

What is platform engineering, and how does it differ from DevOps?

Platform engineering is the practice of creating standardized workflows, toolchains, and internal platforms to improve developer experience and productivity. Unlike DevOps, which focuses on CI/CD, automation, and operations, platform engineering aims to scale these practices across teams, providing self-service tools and reducing operational friction.

Why are companies adopting platform engineering?

Organizations are embracing platform engineering to streamline development, reduce bottlenecks, and improve collaboration. It minimizes redundant work, eliminates tool fragmentation, enhances security, and boosts deployment success rates, enabling teams to ship software faster and more efficiently.

What common problems does platform engineering solve?

Platform engineering addresses several recurring challenges:

• Long project setup times: Standardized templates (Golden Paths) help developers start projects in minutes instead of days.
• Tech stack sprawl: Self-service portals ensure developers use pre-approved tools, reducing shadow IT risks.
• Security gaps: Automated policy enforcement and security monitoring prevent vulnerabilities in code and infrastructure.
• Deployment failures: Standardized CI/CD pipelines and infrastructure automation improve release success rates.
• Communication silos: Internal developer portals centralize workflows, documentation, and cross-team collaboration.
• High attrition rates: A better developer experience reduces frustration and improves retention.

How does platform engineering improve deployment efficiency?

By integrating automation into software delivery pipelines, platform engineering eliminates manual configurations, reduces human errors, and enforces security and compliance standards. This results in fewer failed deployments, lower downtime, and faster product iteration cycles.

How do Golden Paths help development teams?

Golden Paths provide predefined workflows, toolchains, and templates for common engineering tasks. Developers can quickly set up projects with standardized infrastructure, security policies, and best practices, reducing time spent on repetitive setup tasks.

How does platform engineering enhance security?

Security is built into the development lifecycle through automated security testing, pre-vetted code components, and policy enforcement at every stage. This minimizes software supply chain risks, prevents misconfigurations, and ensures compliance with industry regulations.

What role do internal developer portals (IDPs) play in platform engineering?

IDPs act as a one-stop shop for developers, offering self-service access to approved tools, reusable scripts, infrastructure templates, and documentation. They improve workflow consistency, enable faster onboarding, and provide visibility into best practices.

How can companies start adopting platform engineering?

Successful implementation begins with:

• Mapping the developer journey: Identifying bottlenecks and prioritizing improvements.
• Launching an MVP platform: Starting with a minimal version and expanding based on feedback.
• Scaling gradually: Monitoring adoption, refining processes, and automating repetitive tasks.

How does platform engineering impact developer productivity?

By reducing cognitive load and operational distractions, platform engineering enables developers to focus on building high-quality software. Companies using internal developer platforms report faster onboarding, fewer errors, and higher job satisfaction.

In 1776, Scottish economist and philosopher Adam Smith defined two types of labor: productive and unproductive.

There is one sort of labor which adds to the value of the subject upon which it is bestowed; there is another which has no such effect. The former, as it produces a value, may be called productive; the latter, unproductive labor.

Productivity is straightforward in manufacturing (i.e., more goods in less time = more productive), but it’s harder to apply to the knowledge economy and software engineering.

What should you measure as a proxy for software development productivity? Can you optimize individuals’ inputs and outputs? This post will answer these questions.

What is Software Development Productivity?

Development productivity measures an engineering team’s efficiency against selected metric(s) and identifies elements that impede or accelerate their work and creativity.

By measuring development productivity, managers want to understand better how much work the team can handle to improve planning, plus identify common points of friction (e.g., due to unclear requirements or poor handover processes). 

Information and technology silos are the biggest constraints to high software development productivity. A Mattermost survey found that 86% of developers lose 1-8 hours weekly on information lookup. Another 45% are frustrated with tool and stack integration, leading to context switching and troubleshooting, while 25% mentioned that collaboration tools don’t fit into developer workflows.

Interruptions are another problem. Almost a third of an engineer’s time is spent in meetings. This lack of focus time for core work leads to lower completion velocity and revenues for your business.

The best software engineering work happens in the flow state — a state of deep concentration and intrinsic enjoyment. But getting into and staying in the flow is hard. A recent study found that 43% of software engineers experience flow only sometimes and 30% often.

Software engineers cite a variety of common barriers to productivity, including external factors (e.g., how work is planned, organized, and performed) and internal factors like emotional and physical well-being.

The bottom line is that to establish high software development productivity organizations need to improve both ‘soft’ factors like collaboration, team alignment, and leadership, as well as establish proper organizational workflows for project planning, knowledge management, and toolkit integration.

How to Measure Software Development Productivity: Frameworks & Principles 

Can software engineering productivity be measured using metrics like ‘line of code’ or ‘story points’?

In 2003, Martin Fowler, co-author of the Agile manifesto, argued that the software industry couldn’t reasonably “measure productivity”. But people still tried, successfully or not.

In 2014, a group of computer scientists invented the DevOps Research and Assessment (DORA) framework. It measures software delivery productivity through four key metrics:

• Deployment frequency: Time required to release new code successfully. 
• Lead time for change: Time required to commit new code into production
• Change failure rate: Percentage of deployments causing issues in production
• Time to restore service: Time required to recover from a failure in production successfully

The SPACE framework is another popular methodology developed by GitHub, the University of Victoria, and Microsoft to measure individual developer productivity through five dimensions.

• Satisfaction: Levels of developer satisfaction, efficacy, and burnout.
• Performance, measured by the quality and impact of the delivered software.
• Activity, measured via quantifiable developer activities at different phases of the software development life cycle (e.g., count of build, test, deployment, or on-call participation).
• Communication and collaboration, measured by onboarding time or work integration speed.
• Efficiency measures interruptions or delays that affect the flow state (e.g., the number of handoffs in a process) and the impact of interruptions (e.g., meetings or training).

The SPACE framework recommends choosing a limited set of metrics that span all three levels and capture various aspects of productivity rather than using them all at once.

A recent McKinsey article proposed a new approach: adding ‘opportunities focus’ metrics to those offered by the DORA and SPACE frameworks.

Source: McKinsey 

However, technology leaders also vocally criticized the framework. Extreme programming creator Kent Beck and veteran software engineer Gergely Orosz countered that the McKinsey framework is overly focused on software engineering output, not outcomes or impact, and effectively discards half of the software developer lifecycle.

Dan Terhorst-North, an organizational transformation consultant, argued that the framework also effectively discards the supporting work senior engineers often do. “The whole push of the [McKinsey] article is that the best developers should be ‘doing’ rather than thinking or supporting,” he wrote.

Arguably, the best response to the ongoing debate about measuring developer productivity came from Andrew Boyagi, Senior Technical Evangelist at Atlassian:

There’s an unhealthy obsession with measuring developer productivity. Imagine the possibilities if the same amount of time and energy went into helping developers be more productive. Give them what they need, and they will be more productive.

I’m not saying don’t measure, but measuring isn’t going to get you closer to the outcome you’re after. Balance where you put your energy and time.

So, how can you empower people to do their best work? At 8allocate, we practice the following principles. 

Measure Outcomes, Not Output

Many software development metrics focus on quantity: the number of code lines, the number of new commits, etc. These metrics are not inherently wrong but don’t always tell the whole story.

A low defect rate looks good on the dashboard, but high-quality code for an unnecessary feature lacks business value. Senior software engineers may spend more time pair-programming with junior staff to help them solve more impactful problems, which boosts the junior worker’s metrics.

Measure outcomes, not output. Quantify the teams’ contribution through dimensions like product quality (e.g., service health, change failure rate, etc.) and business impact (e.g., feature usage, customer retention, user satisfaction, etc). 

Measure Teams, Not Individuals

Software is built by teams, not individuals. Agile metrics like sprint burnout rate or velocity can give you a good sense of team performance, allowing you to track progress and forecast the team’s speed in working through the backlog. Both give managers an idea of the team’s pace through iterations and help identify and investigate reasons for friction.

Measuring individual contributions is challenging. Some people may get higher-impact tasks, warranting more story points, while others do important ‘glue work.’ 

Tanya Reilly, principal engineer at Squarespace and former Googler, pointed out that senior engineers often have to deal with unglamorous non-technical work like “onboarding the junior engineers, updating the roadmap, talking to the users, noticing the things that got dropped, asking questions on design documents, and making sure that everyone’s going roughly in the same direction.”

Focusing on rewarding individual contributions instead of teamwork can make people avoid ‘glue work’ and not develop technical leadership skills. Without strong leadership, no software engineering team can be successful.

​​Don’t Blindly Optimize Around the Metrics 

Metrics provide direction and are important to understand progress, output, and impacts. But as Goodhart’s Law says: “When a measure becomes a target, it ceases to be a good measure.”

By optimizing for one or several metrics, you’ll get unhelpful results. For instance, maximizing successful builds incentivizes developers to focus on relentlessly shipping new features rather than ensuring their high performance in production.

We modify our behavior based on the standards we’re held against. Any metric you try to optimize will compel the person to optimize their score against it, which doesn’t always translate to the best business impacts.

6 Recommended Software Development Productivity Metrics 

With the right metrics in place, leaders can identify points of common friction — poor planning, subpar processes, or overly optimistic deadlines — and nip this at the bud. Use the following software development productivity metrics to understand your team’s performance and make smart operational decisions.

1. Cycle Time

What it tells: Time from the first commit to production release. Indicates the speed of shipping a new feature through deployment pipelines.

Cycle time gives a good idea of how long different software releases take, but it doesn’t fully reflect the scope of work done. Pushing four smaller commits per day suggests a faster cycle time. But if breaking down work for developers leads to more toil, you’re not really getting better outcomes. 

How to use it: Knowing cycle time leads to better Spring planning and project timeline estimation. A sudden drop in cycle time for a reasonably regular commit likely means your team is blocked or experiences another issue worth investigating.  

2. Lead Time

What it tells: Time from when a customer places a request (e.g., for a feature) to when it’s delivered in production. 

Unlike cycle time, which gets tracked when the team begins development work, lead time includes all the ‘grunt work’ such as creating software requirements specifications and user stories. 

Source: Agile Academy 

How to use it: Lead time shows the time needed to go from an idea to a completed feature. When compared to the cycle time, it helps managers understand the time spent on preparatory work — customer interviews, data collection, business analysis, stakeholder management, etc.

Long lead time may signify operational inefficiencies, delaying software engineers from starting development work.

3. Sprint Burndown

What it tells: How much work gets done per sprint in story points or hours.

Ideally, all planned work must be completed by the end of the sprint. Full sprint burnout rates signify an A+ software engineering team and a capable project lead. But it can also mean you’re not committing enough work for each Sprint, especially if your team finishes it early.

Source: Microsoft 

How to use it: Sprint burnout charts track progress and detect issues. It’s normal for software engineering teams not to burn down all items every time. But if the team often misses the mark, it can indicate planning issues (e.g., the lead plans too much work or changes the scope). A sudden, rather than gradual, drop in the burnout rate means you’re not sufficiently breaking down work.

4. Velocity 

What it tells: Average amount of work completed per sprint, measured in story points or hours. 

Velocity gives a good outlook on the team’s performance and productivity. But it’s tricky to get right. Inexperienced managers may push for higher velocity, forcing more work, not better. Others may misuse story points to represent time. Neither is optimal.

Each team’s velocity is unique and reflects their work type. Some have lower velocity but produce complex, impactful features, while others have higher velocity by doing simpler, operational work.

How to use it: Velocity helps estimate a team’s speed through the backlog and helps plan work. It also monitors team dynamics. New teams increase velocity as they improve relationships and processes. Established teams have stable velocity, which allows you to test whether a particular improvement to the software development lifecycle made any impact. Bottlenecks and inefficiencies in development processes can cause a sudden drop in velocity. 

5. Feedback Loop 

What it tells: The time developers need to get work done vs. the time they spend waiting for feedback.

Software engineers can be blocked by technology (e.g., automated test latency or CI pipeline failures) or people (e.g., code reviewer approvals). Fast feedback loops — instant response from systems and people — help developers work efficiently. Slower feedback loops create interruptions, forcing developers to switch tasks to review notes or troubleshoot issues instead of staying in the flow.

How to use it: Ask your teams where they experience the most friction to shorten the feedback loops. A quick survey can help you identify areas for improving peer review or hand-off processes and accelerating development with technology (e.g., by improving the development environment setup).

6. Mean Time to Recovery (MTTR)

What it tells: Average time the team needs to recover a system after an incident or an outage. 

Unplanned downtime is costly. The sooner the system is back to normal, the less damage your organization will experience. MTTR helps assess the teams’ performance during critical events — unplanned outage, critical system bug, or another type of failure. 

How to use it: Low MTTR indicates issues with your recovery workflows, but it doesn’t point to the exact cause(s), which can be plenty: Alert fatigue, lack of automated playbooks, staff shortages, etc. 

To diagnose the root cause, you’ll need to dig deeper into available data to identify areas for improvement. 

How to Improve Software Development Productivity

Metrics are great for diagnosing problems, but you also need to administer solutions. 

High software development productivity is a sum of investments, made into operational processes, enabling technologies, and cultural practices. 

At 8allocate, we’ve been looking into several areas to make our software engineering teams happier and more productive. 

Introduce Developer Portals 

Developer portals offer a standard catalog of applications, tools, and APIs for developers to kick-start work. This ‘out-of-the-box’ experience reduces the cognitive load and tool fragmentation. Instead of figuring out which open-source framework to use or test case configurations, developers can start new work with minimal friction. To improve developer experience, 59% of companies plan to introduce application deployment automation, 52% — to add easy access to trusted open-source software, and 46% — to introduce application templates, a Forrester study found. 

Some software development teams include Golden Paths in their development portals. Popularized by Spotify, Golden Paths offers tutorials and recommended tools for standard engineering tasks (e.g., building a backend service or creating a data pipeline). By introducing Golden Paths, the Spotify team reduced the time needed to build a simple website from 14 days to less than five minutes. 

Establish CI/CD Pipelines 

One key DevOps principle is to “Automate as much of the software development lifecycle as possible.” In practice, this means the adoption of continuous integration (CI) and continuous delivery (CD) pipelines — a semi- or fully-automated path that allows for new code to be accepted for release. CI/CD guardrails and checkpoints codify quality requirements, focusing attention on creating quality code.

Smart automation reduces developer time spent on testing and debugging. Software supply chain automation removes constant monitoring, enabling continuous deployments and notifications for irregularities.

Experiment with Coding Copilots

AI developer tools are a hot software engineering trend this year. A GitHub survey found that 87% of developers say AI coding tools help save mental effort on repetitive tasks, and 41% believe they can help prevent burnout.

Another study found that developers complete tasks 55.8% faster when using coding copilots. Early Copilot adopters said developers’ productivity increased by about 10% after a small team trial.

Our software engineers tested several testing copilots. Many noted that AI coding tools helped them maintain a deeper flow state and avoid context switching. We observed a 2X increase in productivity among software engineering teams using AI tools and are working on further integrating AI-powered automation into our workflows.

Final Thoughts 

High software development productivity requires changes in workflows, toolkits, and management practices. Discuss barriers with your team. Examine the standard operating procedures and tooling they use to identify areas of improvement. Brainstorm which combination of software engineering metrics could give you a good sense of the teams’ pace, delivery capabilities, and satisfaction with work. Use all of this data to guide your subsequent investments in developer experience and workflow optimization. 

Need further guidance in these areas? 8allocate offers strategic advisory in process engineering. With guidance from our senior leaders, you can implement better workflows and improve developer experience. Contact us to learn how you can set up your teams for greater success. 

Frequently Asked Questions

Quick Guide to Common Questions

What is software development productivity, and why does it matter?

Software development productivity measures how efficiently engineering teams deliver high-quality software while minimizing bottlenecks. It helps organizations optimize workflows, improve planning, and reduce unnecessary overhead in development processes.

What are the key principles for measuring software development productivity?

Effective measurement should focus on:

• Outcomes, not just output – Prioritizing impact over raw numbers (e.g., lines of code).
• Teams, not individuals – Software is built collaboratively, so team performance matters more than individual contribution.
• Balanced metric use – Avoiding over-optimization around a single KPI that may distort behavior.

What are the most valuable productivity metrics for development teams?

The best metrics for evaluating team productivity include:

• Cycle Time – Measures how quickly code moves from development to production.
• Lead Time – Tracks how long it takes from a feature request to delivery.
• Sprint Burndown – Visualizes progress within an agile sprint.
• Velocity – Assesses the team’s capacity to complete work over time.
• Feedback Loop – Identifies delays in reviews, testing, or deployment.
• Mean Time to Recovery (MTTR) – Measures how quickly teams resolve incidents.

How can organizations improve software development productivity?

Boosting productivity requires:

• Developer portals – Centralized platforms to reduce friction and standardize tool use.
• CI/CD pipelines – Automated workflows that streamline testing and deployment.
• AI-powered coding copilots – Tools that reduce manual coding effort and improve efficiency.

How can organizations prevent productivity loss due to interruptions and inefficiencies?

• Minimize unnecessary meetings to preserve focus time.
• Streamline knowledge sharing with better documentation and developer platforms.
• Automate routine processes to reduce manual overhead.
• Use shorter feedback loops to accelerate decision-making.

According to Yahoo Finance, ​​the EdTech market is anticipated to reach a value of around USD 549.6 Billion by 2033, reflecting significant growth from USD 146.0 Billion in 2023, with a projected Compound Annual Growth Rate (CAGR) of 14.2% during the forecast period.

With the steady growth of the sector, we will witness how the EdTech data volume will grow exponentially. If managed effectively, this data can be the source of valuable insights into the learning process, factors, outcomes, learning trends, and more. 

However, managing and analyzing the growing volumes of EdTech data and finding the best application of it can be challenging. The purpose of this article is to give readers a better understanding of data management in the EdTech realm. 

Let’s together explore what types of data are valuable for EdTech firms, how data is utilized in this sector, what challenges may arise in the process, and how to overcome them. 

The further implications of this article will be of interest to educators, EdTech firms, students, and tech-driven organizations looking for effective EdTech initiatives. Keep reading to look at data management from the perspective of education and technology. 

Understanding Data in EdTech

In EdTech, data is one of the main drivers of growth. Various types of data are collected, stored, analyzed, and utilized to make the learning process more effective and insightful for both educators and learners. Let’s have a look at the major data types utilized in this sector:

Learning Outcomes Data: Learning outcomes data provides insights into student performance. Examples of this data type include test scores, grades,  progress reports, etc. Having such data at hand, educators can better understand the outcomes of the learning process for each particular learner. This, in turn, enables teachers to detect performance gaps and use effective teaching strategies to help learners eliminate them. 

Engagement Data: Engagement data explains how learners interact with learning material. For example, session duration, bounce rate, pages visited, and others. Engagement data is collected to help educators detect engagement gaps and patterns, improve the learning materials people use, and offer help or support when necessary. 

Usability Data: Usability data provides insights into the technical side of the learning process and focuses on user experience with EdTech solutions. Namely, EdTech firms collect data on user navigation on a learning platform, bugs or issues users encounter, interaction with specific interface design elements, etc. EdTech software vendors use usability data to continuously improve the solutions, making them intuitive and error-free. 

Collectively, these data types provide a holistic view of the learning process, enabling educators and EdTech companies to create more effective, personalized, and engaging learning experiences.

The Role of Data Management in EdTech

Data should not be collected just for the sake of being collected. It can be the source of insights when making strategic decisions. If managed effectively, data can help EdTech companies improve academic progress, personalize the learning process, and increase user satisfaction with educational solutions. 

Let’s dive deeper into the impact data management makes in EdTech:

Data-Driven Decision-Making

In education, effective data management fuels the decision-making process. The decisions made by educators can vary from minor choices on curriculum or teaching tactics to business decisions that shape the company’s growth. 

Effective data management enables decision-makers to know what data they can refer to to rationalize their strategy. Instead of relying on gut or assumptions, educators leverage the power of data and get evidence confirming their choices.

The recent case study discusses the application of data-driven decision-making in the context of higher education (HE) assessment. The researchers make use of the performance data to make an informed decision for moving from exam-based assessments to non-exam assessment methods. The authors analyze data before, during, and after the COVID-19 pandemic using different learning metrics such as failure rates and mean marks. 

This study leveraging a data-driven decision-making approach supports innovation in teaching and assessment redesign in response to the many challenges that HE providers face as a result of adopting blended learning. It also demonstrates how data is leveraged in EdTech to fuel decisions and strategies that, in turn, are expected to increase the flexibility of learning. 

Thus, incorporating data management practices into your decision-making processes empowers you to make informed choices, stay proactive, and drive value for your organization.

Improving Learning Outcomes

Data in EdTech plays a crucial role in improving learning outcomes in several ways, according to the UNESCO IIEP Learning Portal. As highlighted in their recent brief, data can reveal gaps in student achievement and service provision, helping to identify those groups that are underserved or underperforming. Once identified, such inequities can be addressed. 

For example, if a learner is having a tough time with a certain subject, an educator can detect this by analyzing their performance data and comparing academic performance in various subjects. The teacher can then give that learner some extra resources or a bit more one-on-one time. 

Data management can be used to hold the system accountable for the use of resources by showing whether increased investment in education has resulted in improved student performance. Besides, additional information on an individual learner’s background allows the teacher to diagnose possible causes of poor performance and apply remedies.

In a nutshell, when educators use data effectively, they get a 360-degree view of how their audience is doing. With data accessible 24/7, educators can track learner progress, spot situations when learners might need extra help, and step in with support.

Personalization of Learning

One of the big wins of using data in EdTech is that it allows firms to customize education. By digging into data about how a learner is performing, their preferred way of learning, and their level of engagement, they can adjust the educational content to match each student’s specific needs. This customization can facilitate the learning experience, making it more engaging and effective for each learner.

One of the effective strategies for implementing personalized learning is the use of learner profiles. These profiles keep a detailed and current record of a student’s strengths, needs, goals, and progress. Profile-based data management involves analyzing big volumes of data about each student’s learning journey, which is then used to personalize their learning path.

Data management is crucial in this context as it involves collecting, analyzing, and using data to inform these practices. This suggests that effective data management, which allows for the continuous monitoring and adjustment of personalized learning strategies based on student performance data, is key to the success of PL.

Common Challenges in EdTech Data Management

As the use of technology in education continues to grow, so does the need for effective data management. However, managing data in educational technology is not without challenges. Here, we discuss the common issues that educators and administrators face when dealing with EdTech data management.

Data Breaches in EdTech

One of the most pressing challenges in EdTech data management is the risk of data breaches. Educational institutions store a wealth of sensitive information, from personal learner details to academic records. This makes them attractive targets for cybercriminals.

According to Comparitech, 2021 has been recorded as the year with the highest number of data breaches in the education sector, affecting 771 educational institutions and close to 2.6 million records. A data breach can lead to unauthorized access to this sensitive information, potentially causing severe damage to the institution’s reputation and even legal repercussions. 

Recently, the Federal Trade Commission (FTC) of the United States has taken action against the education technology provider Chegg Inc. for its lax data security practices that exposed sensitive information about millions of its customers and employees. The FTC alleges that Chegg failed to fix problems with its data security despite experiencing four security breaches since 2017. 

Chegg, which sells educational products and services directly to high school and college students, collected a variety of personal information about its users. However, the FTC alleged that Chegg failed to protect this information, leading to four data breaches. These breaches exposed personal information, including names, email addresses, passwords, and, for certain users, sensitive scholarship data. 

The FTC’s proposed order requires the company to bolster its data security, limit the data the company can collect and retain, offer users multifactor authentication to secure their accounts, and allow users to access and delete their data.

As seen from this example, EdTech providers must prioritize robust security measures to protect user data, maintain trust, uphold privacy, and ensure a safe digital learning environment. 

Data Fragmentation

Data fragmentation is another significant challenge in EdTech. In many cases, educational data is scattered across various platforms and systems, making it difficult to gather and analyze. This fragmentation can hinder the ability to gain a comprehensive view of student performance and learning outcomes, which is crucial for improving educational practices and strategies.

As revealed by Education Week, fragmented data systems can lead to inefficiencies in schools. When data is scattered across different platforms and systems, it becomes challenging to gather, analyze, and use effectively. 

Being a major problem in educational data management, data fragmentation can hinder the ability to get a consistent view of learner performance and learning outcomes, which is crucial for improving educational practices and strategies. 

Lack of Interoperability

The lack of system interoperability is another common issue in EdTech. Many educational tools and platforms do not seamlessly integrate with each other, making it challenging to transfer and synchronize data across different systems. This lack of interoperability can lead to inefficiencies and inconsistencies in data management strategy, negatively impacting the effectiveness of educational strategies.

The problem of data interoperability is a significant issue in the development of software collaboration environments. Data interoperability becomes a problem when consumers need to use data owned by producers, and the syntax and/or semantics of such data at both endpoints are not already aligned. This misalignment can lead to difficulties in data integration, hindering effective collaboration among data stakeholders.

At 8allocate, technology is a driving force of modern collaborative education. For years, we have helped companies build elearning solutions and seamlessly integrate them into their EdTech infrastructures. Contact us if you need help with EdTech development, finetuning EdTech systems, or configuring system interoperability via APIs.

In conclusion, while EdTech offers immense potential for enhancing education, it also presents unique challenges in data management. Addressing these challenges is crucial for unlocking the full potential of EdTech and ensuring the security and effectiveness of educational practices. By understanding and addressing these challenges, educators can effectively use data to improve education.

3 Pillars of Effective EdTech Data Management

Effective data management is crucial for the successful implementation of educational technology. It ensures that data is accurate, accessible, and secure, thereby enabling informed decision-making and personalized learning. Here, we discuss the three pillars of data management in EdTech that help EdTech firms achieve positive outcomes.

Data Security and Compliance

Data security and compliance is the first pillar of EdTech data management. Ensuring the security of data is paramount to protect against data breaches and comply with data protection regulations. This involves implementing robust security measures, such as encryption and access controls, and regularly auditing data to ensure compliance with relevant laws and standards.

Keeping data secure involves EdTech companies, schools, and even regulatory bodies all working together and following privacy laws like FERPA and GDPR. However, many EdTech companies are going the extra mile. They’re sticking to strict privacy laws and getting certifications like ISO/IEC 27001. 

So, EdTech companies must navigate the opportunities and challenges of protecting student data, adhering to privacy laws, and implementing robust cybersecurity measures. By doing this, these companies are showing they’re serious about managing and protecting data, using it fairly and ethically. 

Centralized Data Management

Centralized data management is the second pillar. With data often scattered across various platforms and systems, centralizing data management can help overcome the challenge of data fragmentation. 

A centralized system provides a unified view of data, making it easier to gather, analyze, and use effectively. This can enhance the ability to gain a deep understanding of learner performance and learning outcomes, which is crucial for improving educational practices and strategies. Here is an example of what a centralized data repository can look like:

SaaS Viya, a product of SAS, is an example of a robust data management system. It provides an integrated development environment and modern data catalog that allows developers to load, import, profile, cleanse, prepare, and transform data. 

SaaS Viya also offers functions to manage and provide governance for data assets and their relationships. The centralized approach to student data management the company cultivates enables a unified view of data, enhancing the ability to understand student performance and learning outcomes. 

Informatica, another key player in the field, offers master data management (MDM) solutions that unleash the value in data with a trusted, single source of truth. It provides integration, quality, enrichment, and governance in one cloud-native solution.

Informatica’s MDM solutions are built on a high-level architecture that includes Data Explorer and Data Studio for managing data and building and executing data collection and transformation. This helps in loading data from a variety of sources, including files, databases, social media sources, etc.

The tools like SAS Viya or Informatica play big roles in centralizing EdTech data. While the first focuses on analytics, performance, and cloud-native capabilities, the latter excels in data integration, quality, and governance. Together, they empower educational institutions to make informed decisions and enhance learner experiences.

EdTech System Integrity

The third pillar is the integrity of EdTech systems, with a particular focus on the role of APIs (Application Programming Interfaces) in system interoperability. APIs allow different systems to communicate and exchange data, overcoming the problem of system interoperability. 

APIs supported by Learning Management Systems (LMS) allow data sharing with external systems, enriching the LMS with additional data and providing third-party tools with valuable LMS records. 

This data exchange enables educators to personalize programs based on students’ past and current achievements. Learning decisions are informed by additional information about each learner, educational plans, process tracking features, course materials, etc.

The use of APIs effectively addresses major challenges in educational technology interoperability. They facilitate quick, secure, and cost-effective integration. The return on investment for such solutions often exceeds the expenses, making them a valuable asset in the EdTech landscape.

In conclusion, data security and compliance, centralized data management, and integrated EdTech systems form the three pillars of data management in EdTech. By focusing on these aspects, educational institutions can leverage the full potential of EdTech, ensuring the security, accessibility, and effective use of data to enhance educational outcomes.

The Future of Data Management Systems in EdTech

The landscape of EdTech has changed due to the spread of advanced technologies, including AI and ML. These technologies are streamlining the way data is managed in the sector, making the process more efficient and less susceptible to errors. 

AI in EdTech, for instance, is automating the collection and analysis of data. AI algorithms collect and track students’ academic progress and alert instructors when there’s a risk of course failure. By analyzing data, the algorithm assesses students’ academic standing and presents a consolidated report to instructors. Additionally, AI technology is leveraged in education to ensure high-level personalization of learning, automate routine tasks, and streamline student-teacher interactions.

On the other hand, ML is proving to be a game-changer in analyzing vast amounts of data to uncover patterns and trends that can shape educational strategies. For example, ML algorithms can forecast a student’s performance based on past data, enabling timely interventions.

Let’s look at some real-world examples of how novel technologies are leveraged in EdTech solutions. Century Tech, an AI-based teaching and learning platform, is used by educational institutions ranging from schools to universities. It offers personalized learning journeys for students and provides educators with data-driven and AI-powered insights. 

The platform not only enhances learner engagement and understanding through intelligent personalization but also reduces the workload of teachers by automating tasks like marking and data analysis. Moreover, it enhances teaching by providing data insights that support timely and targeted interventions.

Another example is Content Technologies, Inc. (CTI), an AI firm that leverages deep learning to create tailor-made textbooks. Their platform can take a set of guidelines and churn out a textbook that aligns with those specifications, thereby simplifying the task of providing customized learning materials for educators. Both CTI and Century Tech are revolutionizing the education industry, cultivating a data-driven approach to designing learning strategies and tactics. 

The examples show that we can anticipate a future where learning experiences are more personalized and adaptive, thanks to data analytics and management. Powered by AI and ML, data management will remain a key player in decision-making, curriculum development, and student assessment. 

Final Thoughts

In this paper, we explored data management in EdTech, the types of data collected in this field, and the challenges one may face when dealing with EdTech data management. Besides, we looked at the role of emerging technologies, including AI and ML, in transforming the educational sector and shaping the future of EdTech. 

The main takeaway from this article is that data management is crucial for EdTech success. By leveraging the right data, EdTech vendors can make informed decisions on their strategy, personalize learning, increase engagement, improve learner outcomes, and more. 

Yet, working with data presumes several challenges that often are hard to overcome. Eliminating these issues requires EdTech vendors to apply a consistent approach that involves effective data security measures, quality data management, and the integrity of EdTech’s data infrastructure. 

If you’re an EdTech company looking for help overcoming data management challenges, our team is ready to help. We have extensive experience developing robust, secure, and user-friendly EdTech solutions. Feel free to reach out to us for a consultation. 

Frequently Asked Questions

Quick Guide to Common Questions

Why is data management crucial in EdTech?

Effective data management enables personalized learning, improved student outcomes, and data-driven decision-making for educators and EdTech providers. As the industry expands, managing vast amounts of student performance, engagement, and usability data becomes essential to delivering scalable, secure, and high-impact learning experiences.

What are the key types of data collected in EdTech?

EdTech platforms gather:

• Learning Outcomes Data – Test scores, grades, and progress reports to assess student performance.
• Engagement Data – Session duration, content interactions, and behavioral insights to optimize learning materials.
• Usability Data – User navigation, interface interactions, and technical performance to enhance EdTech platform design.

How does data improve learning outcomes?

By analyzing performance trends and engagement patterns, educators can identify struggling students, personalize learning paths, and improve instructional methods. Data also helps schools and institutions track the impact of curriculum changes and optimize teaching strategies.

What challenges do EdTech companies face in data management?

• Data Breaches – Cybersecurity risks threaten student privacy and institutional trust.
• Data Fragmentation – Information is often spread across multiple platforms, making analysis difficult.
• Lack of Interoperability – Many EdTech tools do not seamlessly integrate, reducing efficiency in data sharing.

How can EdTech companies strengthen data security and compliance?

Ensuring regulatory compliance with FERPA, GDPR, and local education laws is essential. Strong encryption, access controls, multi-factor authentication, and regular security audits help protect sensitive student and institutional data.

How can centralized data management benefit EdTech platforms?

A centralized data infrastructure eliminates silos and enables real-time analytics, efficient data governance, and seamless integration across learning management systems (LMS) and other EdTech tools. This ensures better tracking of student progress and data-driven curriculum improvements.

What role does AI play in EdTech data management?

AI-powered tools analyze student data in real time, automate routine administrative tasks, and personalize learning experiences. Machine learning models predict learning patterns, recommend tailored resources, and enhance early intervention strategies for at-risk students.

How does 8allocate support EdTech companies in optimizing data management?

8allocate provides secure, scalable, and AI-driven data management solutions to help EdTech platforms integrate, analyze, and utilize educational data effectively. Our expertise ensures seamless interoperability, compliance, and enhanced learning experiences through data-driven insights.

Data-Driven Ambitions Meet Unstructured Reality

Today every enterprise aspires to be “data-driven,” yet 80–90% of new enterprise data is unstructured – think documents, emails, images, chat logs – and much of it remains underutilized. This paradox poses a critical question for CTOs and innovation leaders: how can we foster a truly data-driven culture when the majority of our data is messy, unstructured, and siloed? The answer lies in combining cultural transformation with technical strategy. Leaders must champion a mindset where decisions are grounded in data while deploying modern data management and AI tools to wrangle unstructured information into insights. It’s a dual challenge of people and technology. Organizational change, not technology, is the primary barrier to a data-driven culture. This guide offers expert advice on overcoming those barriers by aligning stakeholders, upgrading data practices, and leveraging AI analytics to turn hidden data into business value.

The Imperative of a Data-Driven Culture

A “data-driven culture” is an organizational ethos where decisions at all levels are guided by data evidence. In a true data-driven company, all stakeholders recognize that data can answer critical business questions and are willing to invest time and money into data processes. This means leadership actively promotes using data for decisions (“let’s look at the numbers” becomes a mantra), teams trust data insights, and employees are equipped to understand and act on data. Such a culture doesn’t happen by accident; it requires strong top-down vision. As our experts at 8allocate note, it’s much easier to instill data-driven practices when founders and executives visibly champion the value of analytics. Without executive buy-in, even the best data projects risk being starved of support or canceled when quick ROI isn’t immediately seen.

Data culture starts at the top. Research by NewVantage Partners found over *91% of executives cite cultural obstacles – like lack of alignment and poor data literacy – as the greatest barrier to becoming data-driven. In practical terms, this means technical solutions alone can’t create a data-driven organization. Leadership needs to set the tone by treating data as a strategic asset, aligning it with business goals, and incentivizing data-driven decision-making across departments.

The Unstructured Data Challenge

Even with strong leadership support, companies face a technical hurdle: their burgeoning troves of unstructured data. By many estimates, unstructured data makes up about 80–90% of all enterprise data, and it’s growing 3× faster than structured data. Yet only a fraction is ever analyzed. Less than half of unstructured content gets analyzed for insights, and only ~58% is ever reused after its initial capture. This unused “dark data” represents massive untapped value – from customer emails and support chats to product design documents and social media feedback.

Why is unstructured data so under-leveraged? First, it’s inherently harder to organize and analyze. Unlike tidy rows in a database, unstructured files have no predefined schema. They exist in myriad formats across disparate systems – making integration a nightmare. Many organizations suffer from data silos, where each department stores its own documents and media in isolation. This leads to a fragmented view of information; for example, customer-facing teams might have call transcripts that never reach the analytics team. In FinTech, an industry we serve, 54% of financial institutions say data silos are a top obstacle to innovation, as silos prevent a 360° view of the business. Secondly, ensuring quality and trust in unstructured data is difficult – errors or biases in text data, for instance, can mislead models and people alike. Without careful governance, a repository of unstructured data can turn into a “data swamp” of dubious value.

However, ignoring unstructured data is no longer an option. It often contains the “voice of the customer” and other qualitative signals that structured data misses. Modern AI, especially generative AI (think large language models), thrives on unstructured inputs and can unlock insights at scale from these sources. But to capitalize on this, companies must invest in tools and processes to manage unstructured data. This includes data lakes for flexible storage of raw files, natural language processing (NLP) and computer vision algorithms to interpret text and images, and metadata catalogs to tag and classify content. Organizations embracing these solutions are turning what was once a liability – mountains of messy data – into a strategic asset. The rest of this article will outline how to tie these technical efforts into a broader cultural transformation.

Pillars of Building a Data-Driven Culture (with Unstructured Data in Mind)

To successfully create a data-driven culture that deals effectively with unstructured data, enterprise leaders should focus on several key pillars. These span people, process, and technology considerations:

1. Align Data Initiatives with Business Goals

One of the biggest mistakes is treating data projects as academic exercises or pure tech upgrades. The data journey should be business-driven. This means any analytics or AI initiative – whether setting up a data lake or deploying a text-mining tool – must trace back to a concrete business objective. Do you want to improve customer experience? Reduce risk? Identify new market opportunities? Start there. By tying data initiatives (especially complex ones dealing with unstructured data) to business KPIs, you ensure relevance and gain stronger executive sponsorship. For example, instead of “we need a new data warehouse,” frame it as “we need to integrate customer emails and transaction logs to improve churn prediction by 20%.” This keeps efforts outcome-focused and helps non-data stakeholders understand why the investment matters. Data leaders with a technology-first approach often feel like they’re “pushing a boulder up a hill”; a clear business alignment removes that friction.

On a practical level, aligning with business goals also helps prioritize which data to tackle. Not all data is equally valuable. You might have terabytes of logs, but if parsing product review texts will immediately boost marketing strategy, focus there first. This approach prevents boiling the ocean. It’s easy to get overwhelmed by the sheer volume of data available – especially unstructured content. Instead, start with high-impact use cases and datasets that demonstrate value quickly. This creates a proof of concept for broader cultural change.

2. Secure Executive Sponsorship and Data Leadership

No cultural change succeeds without top-level buy-in. Data-driven transformation is no exception. CTOs and Chief Data Officers should cultivate a coalition of executive sponsors who are committed to using data in their domains. Ideally, the CEO and C-suite should actively discuss data in strategic meetings. If data is only talked about in the IT department, cultural adoption will stall. Leaders like the COO or business unit heads should champion data initiatives, sending a message that “data is everyone’s job.” 

Executive support isn’t just symbolic; it affects resource allocation and middle-manager attitudes. When leadership insists on data-backed proposals and reports, teams will adapt by seeking out the data. We advise establishing a Chief Data Officer (CDO) or similar role if one doesn’t exist – someone who can bridge business strategy and data strategy. This role can lead the creation of a data roadmap aligned with corporate goals, and it underscores that data is a strategic priority, not an afterthought.

3. Develop a Strong Data Foundation – Quality, Integration, Governance

Culture will crumble if the data itself is unreliable or inaccessible. An honest assessment of your data foundation is crucial: Do we know what data we have? Is it high quality and well-governed? Can people get to it easily? If the answer is no, start here. “Be honest about the foundation you have, and work to get it right”– if you have low-quality or fragmented data, it’s hard to ask employees to trust it. Many organizations find they must first invest in data architecture and governance improvements as a precursor to cultural change.

Break down data silos: Audit where data lives across the organization. Often, customer data, operational data, and third-party data sit in separate systems. A data-driven culture requires a unified, 360° view of information, so plan to integrate these sources. Modern data lakes and cloud data platforms are invaluable here. For example, an enterprise data lake can ingest diverse formats – structured databases, JSON logs, documents, images – into one repository with appropriate metadata tags. This doesn’t mean all data is immediately perfect, but it becomes discoverable and usable by those who need it. At 8allocate, our Data Management & Analytics services help companies design such integration solutions, from setting up scalable cloud storage to implementing ETL pipelines that consolidate data silos into a single source of truth.

Ensure data quality: Garbage data will breed mistrust and derail your culture. Invest in data cleaning and validation processes. It’s often worth establishing a data governance framework with roles like data stewards who take ownership of data quality in each domain. Automated tools can profile data for errors or inconsistencies, but human oversight is key to interpret and fix issues. In finance, poor data quality is a common pain point – many firms lack standardized quality rules and end up with backlogs of manual data cleansing. Don’t let that be your story. Define what “good data” means for your organization (accuracy, completeness, timeliness metrics) and monitor those. When stakeholders see that data is trustworthy, they’ll be far more willing to rely on it for decisions.

Data governance and security: Hand-in-hand with quality is governance. Set policies on who can access what data, especially sensitive unstructured data like customer communications or legal documents. This is crucial in regulated industries (finance, healthcare, etc.) and to comply with privacy laws. Establish role-based access controls and auditing – not to create bureaucracy, but to create confidence that data is handled properly. Knowing that there’s a clear lineage and control for data builds trust at the leadership level to use data widely. One of 8allocate’s core offerings is helping clients implement robust data governance – ensuring data is “available, usable, secure, and coherent” across the enterprise. With proper governance, teams can collaborate on data without fear of compliance or security mishaps.

4. Embrace Advanced Analytics and AI for Unstructured Data

Once your data house is in order, empower your teams with tools to actually use the data – especially the unstructured kind. Traditional BI tools and SQL queries work well for structured data, but extracting insights from unstructured data requires advanced analytics: machine learning, NLP, image recognition, and more. Investing in these capabilities demonstrates to the organization that no valuable data will be left on the table. It also helps close the analysis gap between structured and unstructured information.

A few strategies to consider:

• Deploy AI and ML solutions to automate unstructured data processing. This can range from simple classification (e.g., routing support tickets by topic) to complex predictive models (e.g., analyzing legal documents or social media for emerging risks). Modern AI can interpret text, sentiment, audio, and video at scale. For instance, generative AI language models can summarize thousands of customer feedback entries or pinpoint mentions of your brand in the news. As Box’s CIO notes, introducing AI to unstructured data can reveal a treasure trove of insights and streamline how work gets done. Use cases like faster content creation, automated idea generation, and better customer chatbots are already delivering value. The key is to identify which AI use cases align with your business goals and pilot them.
  
• Provide self-service analytics tools that support unstructured data. This might include data visualization tools that can display text analysis or geospatial data, or search tools that let business users search documents and emails for trends. When more employees can easily tap into unstructured data, it ingrains the habit of using all available information for decision-making.
  
• Augment team skills through AI consulting or partnerships. If your in-house team lacks experience in, say, NLP or computer vision, consider bringing in experts. As a solution provider, 8allocate often partners with enterprises to develop custom AI solutions for their unique unstructured data challenges – whether it’s building a recommendation engine from user behavior logs or an AI model to scan financial filings for compliance issues. The right expertise can fast-track your ability to derive value from complex data sources.
  

Remember that adopting AI for unstructured data is not just a tech upgrade; it’s a change management exercise too. Teams may worry about new tools or doubt AI’s outputs. Mitigate this by involving end-users in tool selection and development (so solutions solve the problems they care about) and by highlighting success stories. For example, if an ML model finds a pattern in customer complaints that leads to a product fix, celebrate that win and broadcast it internally. Show how AI is augmenting people’s capabilities, not replacing them, to build enthusiasm rather than resistance.

5. Start Small, Score Early Wins

Cultural shifts can stall if they take too long to show results. That’s why experts urge: “Don’t boil the ocean. Start showing value as early as possible.” Rather than launching a massive multi-year data overhaul before anyone sees benefits, implement a series of quick-win projects. These are limited-scope initiatives that deliver a tangible outcome using data, proving the concept of data-driven decision-making to skeptics.

For instance, you might begin with a pilot project in one department: say, using data analytics to reduce a specific cost or using NLP to automate part of customer support. If that project yields a 15% cost reduction or frees up 20 hours of support staff time a week, trumpet that success. It provides a compelling story of “this is what being data-driven can do.” Early wins build credibility and momentum. They also give you case studies to reference when asking for further investment or cooperation (“see how Project X saved us money – we want to replicate that success elsewhere”).

Starting small also allows you to learn and adjust with lower stakes. Perhaps your first attempt at a new data pipeline will hit technical snags or your dashboard doesn’t quite meet user needs; it’s better to discover those on a small scale and refine the approach. This iterative approach aligns with agile methodologies and helps create a culture of continuous improvement.

6. Invest in Data Literacy and Change Management

Even the best data and AI tools are useless if employees don’t use them. Cultural change is fundamentally about people, so investing in education, communication, and incentives is crucial. Never underestimate the need for change management in your data transformation. This includes:

• Data literacy training: Provide programs to upskill employees on data basics – understanding metrics, using dashboards, interpreting statistical results – as well as on any new analytics tools. The goal is to make staff comfortable and confident in working with data. For example, train marketing teams on how to explore customer data via a self-service BI tool, or train operations managers on reading AI-generated forecasts. Many companies roll out “Data 101” workshops or e-learning modules to build this foundation.
  
• Communication and storytelling: Consistently communicate the “why” behind data initiatives. Share success stories and even lessons learned from failures (celebrating a culture of learning). Consider internal newsletters or brownbag sessions highlighting how data-driven decisions improved outcomes. This keeps the momentum up and helps employees see practical relevance.
  
• Peer champions and coaching: Identify early adopters or enthusiastic users of data in different departments and empower them as data champions. These people can lead by example and coach their peers in adopting new practices. As one data leader advised, “be a king maker, not a king” – elevate business champions and put them on a pedestal. Recognizing and rewarding those who embrace data will encourage others to follow. For instance, if a sales manager uses unstructured data analysis to better qualify leads, share their story at the next all-hands meeting and acknowledge their initiative.
  
• Integrate into processes: Update workflows and templates to embed data. For example, require project proposals to include supporting data, or adjust meeting agendas to review key metrics first. When processes are designed to expect data, people will naturally incorporate it. Some companies even tweak their performance reviews to include data-driven decision-making as a competency.
  

Change management is often the hardest part of the journey, but it’s where lasting transformation happens. Encourage feedback, address fears (some employees may worry data will be used punitively to micromanage or cut jobs), and highlight that using data helps everyone succeed by making their jobs easier and more effective. Over time, as more team members gain comfort and see positive outcomes, the cultural shift gains self-sustaining momentum.

7. Foster a Culture of Experimentation and Resilience

Becoming data-driven it’s an ongoing journey that will have ups and downs. To sustain it, cultivate an environment of experimentation, patience, and resilience. Encourage teams to test hypotheses and run data experiments (A/B tests, pilots, etc.) as a normal course of business. When something fails, treat it as a learning opportunity rather than a reason to abandon data. This “test and learn” mentality is common at digital natives and is a hallmark of data-driven companies.

Leaders should model this by being willing to question assumptions and adjust strategies based on what the data reveals – even if it contradicts intuition or prior plans. Also, prepare the organization that this is a long-term shift: or as one executive put it, have a “Teflon spirit” – expect more downs than ups early on, and don’t let setbacks derail the mission. By maintaining a thick skin and long-term vision, leadership can keep pushing forward until the cultural norms flip in favor of data. Celebrate persistence and incremental progress, not just big wins.

It can be helpful to set up communities of practice or regular retrospectives on the data initiatives. This way, people from different teams can share challenges and solutions, learning from each other’s experiments. Over time, this builds an enterprise-wide competence at using data. When new hires come in, they’ll sense that “this is how we do things here” – and that’s when you know the data-driven culture has truly taken hold.

Tying it All Together: The 8allocate Perspective

Creating a data-driven culture in the age of unstructured data is a complex but rewarding endeavor. It requires visionary leadership, cross-functional collaboration, and smart technology adoption. Enterprises that succeed not only invest in data platforms and AI tools, but also align those investments with a supportive culture – one that values facts over intuition, welcomes insights from all data types (structured or not), and continuously hones its data capabilities. The payoff is significant: organizations with strong data cultures are more agile, innovative, and competitive, turning their data (including the once-neglected unstructured troves) into a differentiator rather than a burden.

At 8allocate, we’ve guided many organizations on this journey. Our experience in AI consulting and custom data solution development has shown us that the best results come when technology implementation goes hand-in-hand with strategy and training. For example, when deploying a new big data platform or AI model, we help our clients define governance policies and change management plans so that the solution is embraced and used to its full potential. We also emphasize quick wins and iterative development, aligning with the principle of showing value early. Whether you need to build a centralized data lake to break down silos or develop an NLP solution to extract insights from documents, our team can tailor an approach that fits your company’s culture and goals. We don’t do one-size-fits-all – we partner with you to embed data and AI in a way that makes sense for your people and processes.

Conclusion and Next Steps

Fostering a data-driven culture is one of the best “investments” a modern enterprise can make – but it must be nurtured through deliberate action. By securing executive advocacy, integrating your data (structured and unstructured) into a single accessible backbone, ensuring that data is clean and trustworthy, and empowering your people with the right tools and skills, you create fertile ground for data-driven decision-making to flourish. The journey will have challenges: expect initial resistance, technical hiccups, and the need for ongoing adjustments. Yet, as you persist and iterate, you’ll start to see a transformation: meetings where reports and gut-feelings are augmented with real data evidence; teams that proactively seek out data to solve problems; innovations sparked by insights hiding in what was once “dark” unstructured data. In the end, a strong data culture becomes self-reinforcing – success breeds more interest in using data, which in turn drives more success.

8allocate is here to support and accelerate this transformation. From auditing your current data maturity to building AI-driven analytics platforms, we bring both technical expertise and a keen understanding of organizational dynamics. Our value-driven, no-buzzword approach means we focus on solutions that make a tangible impact on your business outcomes. If you’re ready to turn your data ambitions into reality, we invite you to reach out and explore how we can help tailor the right strategy and solutions for your needs.

Contact us for a consultation on building the data architecture, analytics solutions, and cultural roadmap that will unlock actionable insights from all your data – structured and unstructured – and drive your business forward.

FAQ: Building Data-Driven Cultures in the Era of Big Data and AI

Quick Guide to Common Questions

What exactly does it mean to have a “data-driven culture”? Why is it important?

A data-driven culture means that decisions at all levels of the organization are informed by data insights rather than just intuition or past experience. In such a culture, people ask “What do the numbers say?” before making choices. This is important because it leads to more objective, evidence-based decision-making, which can improve performance and innovation. Companies with strong data cultures tend to outperform others because they leverage facts to find opportunities and address issues. It also fosters alignment – when everyone looks at the same metrics, there’s a single version of truth guiding the team. In short, being data-driven makes an enterprise more agile and competitive in a fast-paced, information-rich market.

Our data is mostly unstructured (texts, PDFs, images). How can we start using this data effectively?

Unstructured data can indeed be challenging, but it’s also extremely valuable. Start by creating a centralized repository (like a data lake) that can store raw unstructured data in its native format (e.g. videos, documents). Implement metadata tagging and cataloging so you know what each file is about. Next, leverage AI and machine learning tools suited for unstructured data: for text, use NLP algorithms to extract keywords, sentiments, or topics; for images, use computer vision to identify objects or patterns. Modern cloud-based tools and frameworks (from Hadoop and Spark to advanced NLP APIs) can process large volumes of unstructured data efficiently. It’s also critical to integrate this analysis into your workflows – for example, if you analyze customer support tickets (text), feed the derived insights to your product improvement teams regularly. Partnering with data specialists or providers can accelerate this. Remember, a lot of unstructured data’s value comes when combined with structured data (e.g. linking a customer’s call transcript to their purchase history gives a full picture). With the right strategy, you can turn unstructured data into a goldmine of insights that were previously overlooked.

How do we ensure our data is trustworthy and of high quality?

Ensuring data quality is essential – if users don’t trust the data, they won’t use it. Start by implementing data quality checks and audits. This can include automated validation rules (for example, flag out-of-range values or missing entries) and routine data profiling to detect anomalies. Many organizations assign data stewards to key data domains; their job is to continuously monitor and improve data quality (fixing errors at the source, reconciling duplicates, etc.). It’s also important to set up a formal data governance policy that defines data standards (formats, definitions) and responsibilities. Tools can help a lot – for instance, data quality software can cleanse and standardize datasets, and master data management solutions can ensure consistency of key entities (like customer or product information) across systems. Additionally, maintain clear data lineage documentation, so it’s transparent where each data point came from and how it’s been transformed. This transparency builds trust. Finally, foster a culture of quality – encourage employees to report data issues when they see them, treat data as a product that has consumers who expect reliability. When users see that data is accurate and up-to-date, their confidence grows and they’ll rely on it more for their work.

How can 8allocate help our company become more data-driven?

8allocate provides end-to-end support for organizations aiming to strengthen their data culture. On the strategy side, our senior AI strategists can assess your current data maturity and identify gaps in technology and process – essentially helping you craft a roadmap for building a data-driven organization (covering governance, architecture, and even training programs). We offer AI consulting services to align data and AI initiatives with your business objectives, ensuring you invest in the right solutions. On the technology side, we have expert teams to implement the needed solutions: from setting up data infrastructure (cloud data lakes, real-time data pipelines, data integration tools) to developing custom AI and analytics solutions tailored to your needs (like NLP systems for document analysis or machine learning models for predictive analytics). We also emphasize knowledge transfer – as we build out solutions, we work closely with your staff to train them on new tools and methodologies, fostering buy-in and self-sufficiency.

The EdTech sector accelerated during the pandemic as educational institutions and L&D organizations rushed to digitize education.

However, then came the period of disillusionment among investors. Research from Crunchbase shows that the total funding volumes nosedived by over 9X between 2021 and 2023.

Source: Crunchbase.  

Indian EdTech unicorn Byju’s has lost over $15 billion from its $22 billion valuation last year, due to layoffs, lawsuits, executive resignations, and poor cash flow management. Another major player, Fresno, filed for bankruptcy in June 2023. 

In Europe, EdTech funding slowed but remains above 2020 levels with back-to-back growth in half of 2023 data from Brighteye Ventures. The total number of EdTech deals in Europe also remained high. 

EdTech also remains a booming industry in LATAM and APAC. In 2021, investment volumes in EdTech in LATAM were sixfold the funding in the previous year, according to JP Morgan. The overall funding in this region is a ‘modest’ $1 billion, compared to 68.6 billion globally. The APAC region, in turn, may lead the EdTech industry by 2030, some analysts say, due to significant user bases and fast economic growth. 

Education has been (and remains) a multi-billion dollar market, growing at a steady rate year-on-year. After a slump, there’s likely to be a new peak in interest, especially for EdTech companies capitalizing on emerging technology trends.

Source: Yahoo Finance

6 EdTech Trends to Watch in 2024 

This year, EdTech funding will likely increase, especially for generative AI-enabled EdTech tools, corporate microlearning LLMs, and new education financing solutions — key EdTech trends our team has identified for 2024.

EdTech Companies Rush to Launch Gen AI Assistants 

ChatGPT is now everyone’s favorite study buddy. Half of UK undergrads use AI for assignments and 1 in 5 U.S. teens get homework help from ChatGPT.

By combining conversational interfaces with extensive ‘knowledge’, foundation models can perform various tasks like explaining concepts, validating responses, and generating new outputs (like an essay). These capabilities directly threaten many EdTech companies. 

Chegg’s stock plummeted by 40% in May 2023 as the company’s leadership admitted that ChatGPT is “having an impact on our new customer growth rate”. Soundly, business leaders rapidly turned the disruption to their advantage and started integrating foundation models into their products, rather than trying to outcompete them. 

Chegg signed a partnership deal with OpenAI to integrate the GPT-4 model into its personalized learning platform and train it on the company’s repository of 100 million study questions. Korean corporate learning platform Elice also rapidly launched an on-site chatbot powered by the GPT-3 model. Duolingo Max, a premium subscription tier, also includes AI abilities. 

At the start of 2024, Udemy announced it’s building an entire “gen AI-powered ecosystem” to power personalized learning solutions for professionals. The AI learning assistant will help learners discover the most relevant content, summarize course content, and offer personalized feedback. A separate AI Skills Mapping tool will help organizations better evaluate and determine the required skill sets for different roles. The gen AI-powered Thematic Analysis for Cohort Learning feature is designed to facilitate discussions and other group activities and ensure better collaboration between educators and students. 

The EdTech industry has a long history of applying AI for other use cases: Student analytics, test scoring, and adaptive classroom technologies among others. Generative AI is yet another group of technologies that can offer a new degree of interactivity and real-time feedback for learners. 

New technologies like RAG and Langchain also enable companies to fine-tune open-source models like GPT to better work in their domain. Effectively, these allow infusing extra data or connectivity with third-party tools into the model for it to provide more tailored responses. In 2024, we expect more EdTech companies to experiment with custom gen AI assistants and fine-tuned LLMs — an area where 8allocate AI & ML team can help. 

Generative AI Streamlines Educational Content Creation 

Apart from delighting students with instant feedback, gen AI models are also becoming great content co-development partners for educators. Standalone or embedded into a learning management system (LMS), generative AI models can: 

• Do simple design tasks e.g., generate charts, visuals, text, or video examples for training.  

• Create standardized tests, assessments, quizzes, and other testing materials, based on previous versions. 

• When pre-trained on existing educational content, produce new similar content to personalize classes.

For example, GoSkills, a popular LMS and provider of online business courses, launched Genie — an in-built Gen-AI assistant for course authoring. The tool can suggest the optimal lesson formats, based on the target audience, planned number of lessons, and preferred lesson length. It can also generate everything – from the course titles to the syllabus, lesson content, and integrated quizzes. 

Docebo, another popular B2B learning management system, acquired Edugo.AI, a gen AI-based technology platform, to expand its capabilities in the domain. With gen AI, Docebo plans to further automate e-learning course content creation, enable real-time feedback for learners to practice soft skills, improve content discoverability and accessibility with semantic search, and enable other hyper-personalized learner experiences.

In 2024, we expect more gen AI acquisitions by larger EdTech players, seeking to reduce time-to-market for new features. At the same time, we expect more gen AI course authoring tools to emerge and harness great support from investors. For example, a new startup Mindsmith enables course authors to use existing educational materials to generate lessons. Users can also fine-tune content with an in-built  AI assistant and add AI audio narration. Then sync all content with an LMS. ScormHero, a course authoring platform, also launched a spin-off gen AI tool, LearningStudio AI. 

Morgan Stanley expects that generative AI could generate an extra $200 billion in value for the global education sector by 2025. So clearly, it’s a domain worth exploring. 

Corporate Microlearning LMS Solutions Gain Momentum 

The LMS market remains on a growth streak. It’s expected to reach $28.1 billion by the end of 2025 and surpass $70 billion by 2030, growing at a CAGR of 19.2%. Increased adoption of digital learning among corporations has been one of the contributing factors. 

Over 600 million people globally will need to be upskilled or reskilled by 2030. Employers, however, don’t want to take people off work for extensive training, while workers are reluctant to complete administered training outside of their regular hours.

Several LMS companies are working to solve this dilemma. Ireland-based Zika Learn developed a new LMS for creating text-based courses to reach learners through their preferred mobile chat apps. The idea is to provide learners with bite-sized lessons they can read on the go, as well as various interactive exercises to improve information retention. The company claims the average lesson completion time is only 3 minutes, while the course completion rate is 85%, much above the industry averages. 

EduMe, another popular corporate microlearning platform, specializes in training design for frontline, gig workers, and other “deskless” employees. The LMS lets users create social-media-style video lessons and seamlessly share these across popular business products like Microsoft Teams, Workday, ADP, BambooHR, and more. The platform also includes robust learners’ analytics, course completion data, and real-time performance insights, allowing you to easily measure course efficiency and identify knowledge gaps. 

Belgium-based MobieTrain is developing a similar mobile LMS but for customer-facing teams. The tool helps create personalized onboarding flows and training materials for different roles and then administers these through an employee app. 

Microlearning helps companies administer fast, precise, and personalized training for various roles at a fraction of face-to-face training cost. According to Ray Jimenez, author of the “3-minute eLearning” book, micro-courses can be developed 300% faster and cost 30% less to deliver than other learning formats. Students love them more too, with 82% being highly satisfied with the experience and 87.5% being open to recommending microlearning to others.

Data Science Helps Orchestrate High User Engagement 

EdTech companies need to demonstrate high user engagement and retention to rekindle investors’ confidence. But learners’ motivation is fickle, especially in self-paced learning. 

Companies have already tried many creative user engagement strategies: from gamified guilt-tripping to cash rebates for full course competition. However, many also need a better understanding of how to magnify the different drivers of learner engagement. That’s the task data analytics can help with. 

Take it from Duolingo, which created a data-based growth framework for increasing its number of daily active users (DAUs) by 4X between 2019 and 2023. Duolingo’s analytical model classifies all users into 7 mutually exclusive categories (new, current, reactivated, resurrected, at-risk weekly active users, at-risk monthly active users, and dormant users). The model monitors the % of users moving between different categories and alerts the team about current dynamics. 

Based on the available user data, Duolingo then created a predictive algorithm to identify new metrics that were likely to increase DAU if optimized. The largest impact on DAU could be gained by increasing the Current User Retention Rate (CURR) by 2% month-over-month, so that’s what the product team has focused on. 

The team later expanded its data science model to predict other individual drivers that can impact DAUs and set quarterly and annual goals for teams, above and beyond anticipated movement in the metrics. 

Progressive educational institutions also rely on data science to identify students at risk of drop-off.  Western Governors University in Utah used predictive models to recognize at-risk students and re-engage them with early intervention programs. Between 2018 and 2020, the institution increased the graduation rate for the university’s four-year undergraduate program by five percentage points, thanks to the model. 

Historical student data already has answers to low (and high) engagement. Different data analytics models and machine learning techniques help uncover these patterns and transform them into new product features or tailored interventions for different student archetypes. 

Immersive Learning Solutions Will Become More Mature with AI

Pioneer adopters of AR and VR technologies in the classroom have a positive sentiment. Employers are also eager to use more VR for corporate training but are often deterred by high training development costs.  

Generative AI can dramatically decrease the cost of content generation, while analytical AI models can improve training efficiency. 

VR allows recording and analyzing reactions, e.g., whether a person is distracted versus paying attention to external stimuli, events, or conversations. With AI integrated into the immersive learning experience, L&D organizations can track how individual and collective learners interact with the experience, allowing them to customize it and provide continuous improvement. 

Verizon developed a VR-based training program, using Strivr learning platform. The fully immersive experience teaches associates how to act in emergencies like a robbery. After a series of pilots, where Verison noted the high engagement and solid learning outcomes, the company scaled the training program to 1,600 stores across the country. 

Virtual Speech, in turn, uses AI to generate role-play exercises in VR and provide learners with instant feedback. The platform currently supports 25+ different workplace scenarios, ranging from difficult customer conversations to sales pitches and job interviews. 

Ultimately, gen AI can also help with world-building in VR, generating more realistic environments and 3D-based replicas of complex manufacturing systems or medical equipment employees need to learn how to operate. Generative tools can also help with personalizing VR scenarios, based on the learner’s profile, to achieve even better outcomes. 

Solutions for Financing Education Become a New Thriving Niche 

Students struggle to pay off educational debt or access affordable loans in the first place. Employers seek the best way to upskill their staff without shouldering all of the expenses. 

On this nexus of FinTech and education, emerged new companies, seeking to fix access to education reimbursement, co-funding, loans, and grants. 

ClassWallet is building a digital wallet-based purchasing and reimbursement platform for better distributing public funds. Because of compliance-related bureaucracy, governors and policymakers fund programs based on perceptions of demand but find themselves months or even years later with unspent funds.ClassWallet aims to streamline fund distribution by providing better technology for every step of the purchasing and reimbursement lifecycle. 

Singapore-based EduFi, in turn, wants to improve access to funding for students. The startup recently closed a $6.1 million pre-seed round to develop an AI-based study now, pay later (SNPL) lending platform for students in emerging economies, where traditional lending products are inaccessible. 

On the other side of the globe, US-based Candidly is building an embedded educational loan repayment product that allows users to repay their debt faster by partnering with employers, retirement recordkeepers, and financial services companies. The company has UBS, Fiserv, Lincoln Financial Group, and Vanguard among its partners. In 2022, the startup experienced a 10X revenue growth and a 3,600% increase in transaction volumes, signifying a strong demand. 

In 2024, we expect further synergies to emerge between the EdTech and the financial industry, with the former aiming to tackle one of their biggest barriers to user acquisition — high education costs. As a technology partner, experienced both in EdTech and FinTech domains, we’re particularly excited about this trend.  

Final Thoughts 

Global governments ramp up support for digital transformation projects and advocate for further integration of technologies into the classrooms like the updated National Educational Technology Plan in the US or the EU Digital Education Action Plan. Larger corporations, in turn, increase L&D budgets to battle skills shortages. Both factors are good news for EdTech companies in respective sectors and markets. 

Likewise, learners’ interest in digital learning remains high, especially in emerging markets, which are still largely untapped by existing players. Overall, we expect 2024 to be another promising year in terms of sector growth and are excited to be part of it through our ongoing partnership with EdTech companies. Get in touch with our team if you’re looking for a technology partner who can help you integrate innovative features into your educational products. 

Frequently Asked Questions

Quick Guide to Common Questions

How is generative AI transforming EdTech in 2024?

Generative AI is redefining education by automating content creation, enabling adaptive learning, and powering AI assistants for personalized tutoring. EdTech companies are integrating fine-tuned LLMs into their platforms to provide contextual support, generate quizzes, and personalize coursework, reducing educators’ workload and enhancing student engagement.

What role do AI-powered assistants play in modern learning platforms?

AI-powered assistants help students by answering queries, summarizing lessons, and providing instant feedback. Platforms like Udemy and Chegg have already embedded AI tutors, improving learner retention and engagement. With advanced models like RAG-enabled LLMs, these assistants can deliver more domain-specific and context-aware responses.

How is data science improving user engagement in EdTech?

Data-driven learning platforms analyze user behavior to predict drop-off risks and personalize content. Companies like Duolingo use predictive models to refine engagement strategies, ensuring higher daily active users (DAUs). By leveraging data analytics, EdTech providers can optimize course design, track learning progress, and enhance student retention.

FinTech is an industry where data is the main asset that drives informed strategic decisions and fuels growth. From the moment data is collected, it starts its transformative journey from raw numbers to insights. It’s stored, utilized, and analyzed to help FinTech vendors outperform competitors, improve user experience, increase operational efficiency, access credit risk, etc. The more seriously vendors treat data management, the more value they can extract from the numbers gathered. 

Neglecting the data management aspect, in turn, can lead to ineffective use of resources, missed business opportunities, and wrong business decisions. FinTech companies should lay the ground for risk-free and effective data management by leveraging the industry’s best practices and tips to prevent this from happening.

In this article, we will overview data management in FinTech, discuss challenges businesses face when managing data, and share effective solutions to overcome them. After reading it, you will better understand the processes and components of FinTech data management that help companies navigate this competitive and dynamic field. 

Common Challenges in FinTech Data Management

With FinTech being one of our focus areas, we have vast experience in building tailored financial management solutions. But before developing such systems, we deeply explore the challenges clients face to understand what features, functions, and capacities we should add to the product. 

As our experience shows, some challenges are often common to FinTech vendors. Being aware of them helps companies mitigate the risks and develop a robust data management strategy. Here are the critical data management challenges that FinTech vendors typically face:

Data Silos

It’s not by chance that we start with data silos being a common problem among FinTech companies. As recent report shows, nearly 54% of financial institution leaders consider data silos to be the main obstacle on their way to innovation and competitive positioning in the market. 

Data silos are isolated and disintegrated data repositories managed and maintained by different departments within a FinTech company. Each department collects, stores, analyzes, and utilizes data independently, having no centralized solution for data management. 

Siloed data makes it harder for decision-makers to view their business at 360 degrees. Instead of generating insights at the organizational level, teams draw insights at the level of separate departments, which gives them an incomplete picture of business functioning. 

What’s more, ineffective data management leads to time and resource waste, increased expenses, and the consumption of unnecessary storage space. To prevent these issues, FinTech organizations should centralize their data management effort and eliminate any effects of data silos on insight generation.

Data Quality

Data quality is another challenge common to FinTech organizations. As McKinsey highlights, financial institutions frequently face problems related to data quality, and improving it is one of the primary goals they set in their data management planning.

Typically, banks and FinTech companies have programs that allow them to automatically check data quality to spot, prioritize, and fix issues. However, such programs utilize the thresholds and rules designed by each bank independently. In the context of FinTech, there are no universally accepted standards for using such systems. 

Another problem that stems from data quality is fixing issues, which is often time-consuming and requires much manual effort. As a result of the growing workload, companies struggle with huge backlogs and task separate departments to conduct data cleansing manually. 

To handle data challenges, financial institutions use different tactics that enhance data quality standards, including:

Data Profiling: It is like a health check-up for your data. It involves examining the data available and collecting statistics or informative summaries about that data. The purpose of this tactic is to get a clear picture of the state of the data, identify anomalies, and understand where improvements are needed. 

Data Standardization: Standardization is all about consistency. It involves bringing different formats and units of data into a common standard. This makes data easier to work with and helps FinTech companies ensure accuracy when data is used in analysis or decision-making. 

Data Verification and Validation: These are two sides of the same coin. Verification checks if the data was entered correctly, while validation checks if the data is both correct and useful. These processes help FinTech firms ensure that the data is accurate, reliable, and applicable to the current task or project. 

Data Monitoring: Data monitoring is the ongoing process of checking and analyzing the condition of data over time. FinTech businesses use it to maintain the quality of data and quickly spot any new issues or anomalies that might arise.

Altogether, these tactics not only help to improve the quality of data but also contribute to the effectiveness of FinTech services by enabling better decision-making, reducing errors, and enhancing operational efficiency.

Legacy Systems

Legacy systems raise a lot of concerns among the leaders of FinTech organizations. Namely, the use of outdated and incompatible solutions is one of the reasons why companies are not ready to leverage big data. 

There are several factors that impede the shift from traditional, legacy systems to modern and smart solutions. Factor number one is that legacy system modernization is not prioritized among FinTech companies. As reported, 80% of IT team members spend most of their time on recurring tasks, having no capability to start a full-scale migration to modern systems. 

The complexity of systems is stated as another problem, faced by more than 35% of banks. Legacy systems are hard to manage and maintain, not to mention the effort required to move to a brand-new option. 

The issue is complicated by a lack of initiative in adopting modern technologies among FinTech companies. With the financial market being one of the most conservative ones, only 5% of banks see modern technologies as the main driver of growth and innovation. Other 57% state that they lack a budget for adopting modern solutions, while nearly 40% of banks do not even have a consistent tech-driven strategy.

As seen from these figures, lack of resources, absence of long-term vision, and lack of tech expertise are the main factors complicating the upgrade of FinTech systems. Neglecting the issue can result in missed opportunities and inefficiencies, which makes legacy systems a significant challenge for financial data management.

Data Privacy and Compliance

Data privacy and compliance are top priorities for FinTech institutions. However, this aspect also poses a significant challenge to vendors aiming to modernize their data management strategies. Let’s take the process of cross-border data flows as an example. 

When FinTech institutions use cloud services and servers located around the world, data goes through different jurisdictions before it reaches its destination. As the World Economic Forum reports, fragmented regulation of data management slows down the digital transformation of this market at the international level. 

Besides, it leads to tensions and debates between jurisdictions. Businesses suffer from frustration, having no solid data management basis that will be acceptable at all levels and in all regions. Non-compliance and data insecurity, in turn, can lead to financial penalties, operational disruptions, reputational damage, legal actions, and potential loss of licenses. 

Thus, FinTech businesses should invest in robust, scalable, and compliant data management systems that meet the requirements of all jurisdictions they operate in.

Scalability

FinTech companies face unique difficulties due to their size and complexity. One of the main challenges is handling and safeguarding the growing volumes of data. As FinTech companies scale, the volume of data they handle also increases exponentially, turning big data management into a critical issue impacting the financial field. 

Every day, hundreds of millions of financial events occur, leading to a similar number of financial transactions. This massive amount of data is a new aspect of data management that financial practitioners and analysts are now considering. Industry leaders highlight the need for improved technologies to gather, interpret, and give meaning to this data to stay competitive and forward-looking.

How to Improve Data Collection and Storage in FinTech Data Management

The methods used to gather and store information play a key role in a company’s decision-making, customer service, and competitive edge in the FinTech sector. 

By adopting effective data collection and storage strategies, FinTech vendors can maximize data’s value, which, in turn, will help them excel in critical areas. Here are some of the ways to improve data collection and storage in FinTech:

Flexible Data Architecture

In the financial technology industry, a flexible architecture is essential for managing data efficiently. As IBM Developer points out, sharing data enhances decision-making by offering a comprehensive view of business results, processes, and operations. 

The efficiency of obtaining, generating, exchanging, and using information depends on the organization’s ability to share these isolated data silos across the enterprise. Therefore, businesses should implement a flexible data architecture that enables cross-department data sharing.

Source: Gartner

The image above shows how the perception of data architecture has evolved. While in the 2000s, data was typically utilized in fragmented, siloed form, the 2020s marked the beginning of the active metadata era. 

The active metadata era is characterized by comprehensive data management practices, enabling businesses to transform 360-degree analytical outcomes into instant operational alerts and decisions. This is possible thanks to borderless data flows across systems within a unified data architecture.

Shared data improves decision-making by reducing reliance on unreliable sources and unmanaged information. As a result, a well-designed data architecture makes it easier to access data, avoids repetition, and ensures data accuracy, which is crucial for the operational efficiency of FinTech companies.

In our recent article, we explore data architecture as an indispensable component of data management. Check it out if you’d like to learn about the key steps of developing a robust data management strategy and increasing ROI from data management initiatives.

Shift to Cloud-Based Solutions

The shift to cloud-based solutions is revolutionizing FinTech data collection and storage. Cloud computing brings about cost savings and speed improvements for FinTech services.

For instance, banks can save up to 65% on costs, with an average saving of around 20%, by running data in the cloud. The scalability of cloud solutions ensures that you only pay for what you use, which helps reduce overhead costs.

Besides, moving data to the cloud can be a path to unlocking its full potential. With data in the cloud, banks can leverage artificial intelligence and machine learning tools to extract insights. This can lead to improvements in customer acquisition, lead generation, and cross-selling. Plus, cloud-based data facilitates self-service reporting, better data visualization, and easier monitoring of KPIs. 

Secondly, the cloud offers the opportunity for burst computing. Unlike a legacy mainframe with a fixed amount of computing power, the cloud allows for elastic scaling. When there’s an increase in workload, banks can simply enlist additional cloud servers. Once the workload decreases, they can scale down, avoiding the cost of maintaining unnecessary resources.

Lastly, there’s a growing need for banks to migrate their core functions to the cloud. This move can lead to significant cost efficiencies and revenue-generating opportunities. Thus, leveraging the power of the cloud enables FinTech companies to manage their data more efficiently and drive business growth.

Robust Data Governance

FinTech firms should collect and store data responsibly. Data governance ensures accountability by verifying data accuracy, transparency, and adherence to regulations, which are all vital for informed decision-making. 

Robust data governance is a key component of effective financial data management. Understanding who controls the data improves data quality and reduces risks. Additionally, implementing appropriate access controls and stewardship practices enhances data quality and fosters customer trust.

FinTech firms can make the most of their data and stay competitive in the rapidly evolving FinTech landscape by ensuring data accuracy, transparency, and regulatory compliance.

Use of Data Catalogs

Data catalogs are valuable for the FinTech industry as they enhance data management processes. Data catalogs improve data accessibility and promote teamwork. Successful implementation leads to faster data analysis, increased engagement, and informed decision-making. 

Comprehensive data catalogs with metadata enhance data comprehension, enabling insights across various functions and fostering a data-driven culture. They empower FinTech companies to extract maximum value from data by improving its discoverability and understanding. As a result, this also aids them in maintaining a competitive edge in the market.

Automated Data Pipelines

The use of automated data pipelines can significantly enhance financial data collection and storage. The image illustrates the components an effective data pipeline should include. Automating the pipeline helps FinTech firms increase their adaptivity and responsiveness. With its help, businesses can adapt to real-time changes, such as a surge in data volume or a change in code, ensuring that data flow remains uninterrupted and up-to-date.

According to ESG Research, automated data pipelines reduce costs and save time. Organizations that use automated pipelines can experience a reduction in coding and preparation time by up to 91%, spend up to 80% less time building pipelines, and save up to 68% on tool costs.

However, the benefits of automated pipelines go beyond just cost and time savings. They also free up data engineers to focus on more valuable tasks. This means that instead of spending time on routine tasks, data engineers can take tasks that are more value-added.

In addition, automated pipelines improve data mobility. This means that data can be moved easily and quickly, which is crucial in today’s fast-paced FinTech environment. By reducing costs, saving time, and improving data mobility, automated data pipelines play their role in maximizing operational efficiency and bringing better business outcomes.

How to Improve Data Utilization and Analysis in FinTech Data Management

In the world of financial technology, data goes beyond mere collection and storage. It’s data analytics that fuels decision-making and ultimately drives business success. The true potential of data can be unlocked only when businesses use and study it effectively. 

In this section, we will delve into approaches and best practices to improve data utilization and analysis in FinTech, transforming raw data into valuable knowledge that informs decisions and actions.

Advanced AI Analytics

Advanced analytics in FinTech revolutionizes data handling. It goes beyond basic data analytics and employs advanced algorithms and methods to anticipate future trends and customer behavior. 

BlackRock, a renowned asset management company, leverages advanced technology, including big data, data science, and AI tools such as ChatGPT, to revolutionize the FinTech business. This innovative approach empowers BlackRock to identify potential investment opportunities by mining massive datasets, financial indicators, and market trends. It also aids the company in delivering exceptional value to clients by offering tailored solutions.

By utilizing data-driven strategies, BlackRock analyzes historical data effectively, makes informed investment decisions, and continuously optimizes portfolio performance. Techniques such as predictive modeling, machine learning, and AI bring significant advantages to FinTech analytics and help companies like BlackRock develop informed data-driven strategies.

Real-Time Analytics

Real-time analytics is a valuable tool in managing data in the FinTech industry. It provides advantages in diverse areas, including preventing fraud, enhancing customer satisfaction, and boosting operational effectiveness.

For instance, American Express is one of the FinTech companies that harness big data to combat fraud. They use cardholder details, transaction history, and merchant information to develop machine-learning models that detect anomalies and make decisions almost instantly. 

According to Allegra Driscoll, a high-ranking executive at American Express, the company embraced AI early on, particularly machine learning, which has been utilized to combat fraud since 2010. This approach enables the company to stay ahead of fraudsters and protect their customers.

J.P. Morgan, a multinational finance corporation, is another example showcasing the effectiveness of real-time analytics in finance technology. For more than two years, they have been using artificial intelligence (AI)-powered language models to screen payments for validation. This measure significantly speeds up processing, minimizes incorrect validations, and optimizes queue handling. 

To sum up, real-time analytics are vital for data management in the financial technology industry. They not only address scalability challenges but also transform data into a valuable resource that can inform decision-making and foster business expansion.

Other Important Considerations of FinTech Data Management

Apart from direct measures impacting data collection, storage, analysis, and utilization, let’s explore other important considerations of FinTech data management. We’ll be looking at both the security and ethical aspects, which are crucial when dealing with data in this industry.

Security of FinTech Data Management

Data security determines the quality of services provided to the client, the reliability of operations, and the competitive edge of a FinTech company. This aspect involves several key measures you can adopt in data management:

Encryption: Encryption is a must if you aim to secure your data, from the point of storage to analysis and transmission. This process lies in converting data into a code, especially to prevent unauthorized access.

Access Controls: Access control regulates the access to resources in a computing environment. This security technique enables FinTech companies to control access to data by segmenting authorized individuals based on their roles. 

Anonymization: Anonymization helps protect user privacy by removing or masking personal information. The process lies in modifying sensitive data to adhere to privacy laws while enabling its use in software testing and analytics.

Security Audits: You should regularly evaluate the system to identify vulnerabilities and maintain its health. Security audits are systematic evaluations of the security of the data management system by assessing if it aligns with the established standards or criteria.

Implementing data security measures will help you ensure the integrity and confidentiality of data, safeguard user privacy, and maintain system robustness. 

Ethicality of FinTech Data Management

In the realm of ethics, FinTech firms must handle customer data responsibly. Here is how you can ensure it:

Fair Use of Data: Introducing practices that prevent discrimination is essential for businesses striving for fair data use. Firms have to set a transparent communication with users, who should possess the full knowledge of collected data and its usage. 

Adherence to Ethical Standards: Beyond technicalities, FinTech data management involves protecting security and upholding ethical standards. By adhering to these principles, your company can harness the power of data while safeguarding user privacy and respecting rights.

Summing up, ethical data management is an immediate responsibility of FinTech firms. By ensuring fair use of data and adhering to ethical standards, businesses will maintain transparency and prevent discrimination.

Final Thoughts

This article provides an overview of the general tips and practices applicable to a wide range of FinTech data management challenges. By investing in data management, decision-makers get a 360-degree view of business and can continuously improve their services with regard to changing client demands. 

However, to reach maximum effectiveness and develop a robust data management strategy, businesses should center their effort around their specific needs. For this, the help of an experienced software development company will be essential. 

8Allocate is a software development partner specializing in FinTech. We help organizations develop and fine-tune trading and digital payment solutions, blockchain apps, online banking solutions, and financial management tools. 

Apart from the tailored tech solutions we create, our experts also share effective guidelines and best practices to maximize efficiency, improve risk management, and leverage the full potential of data assets for long-term success.

If you’re among the FinTech companies aiming to navigate the complexities of data management, leave us a note. Our professionals will investigate your business needs and come up with a comprehensive data management strategy that will give you a strategic advantage.

Frequently Asked Questions

Quick Guide to Common Questions

Why is data management critical for FinTech companies?

FinTech companies rely on secure, high-quality data to enhance decision-making, customer experience, and regulatory compliance. Without effective data governance, businesses face inefficiencies, security vulnerabilities, and missed opportunities in an industry where data drives everything from fraud detection to risk assessment.

What are the biggest challenges in FinTech data management?

One of the primary challenges is data silos, where different departments manage data in isolation, limiting visibility and making cross-functional insights difficult. Poor data quality further compounds the issue, as unstructured or inconsistent data leads to inaccuracies in financial modeling. Many FinTechs also struggle with legacy systems that slow down operations and make integration with AI-driven analytics difficult. Regulatory compliance remains a key concern, as financial institutions must continuously adapt to evolving standards while maintaining data privacy and security.

How can FinTech companies overcome data silos and improve integration?

Breaking down data silos requires a centralized data architecture that ensures seamless information flow across systems. Many companies are shifting to cloud-based solutions that provide scalability, interoperability, and real-time data access. Automated data pipelines help streamline data ingestion and processing, reducing inefficiencies and improving decision-making.

What steps can FinTech companies take to improve data quality?

Maintaining high data quality starts with standardization protocols that ensure consistency across datasets. Data validation and real-time monitoring play a crucial role in detecting inconsistencies and preventing errors before they impact financial operations. FinTech companies are increasingly using AI-powered analytics to clean, categorize, and optimize data for predictive insights.

How does AI enhance FinTech data management?

AI-powered solutions automate data governance, fraud detection, and compliance monitoring. Advanced machine learning models analyze vast amounts of structured and unstructured financial data, enabling faster decision-making. Real-time analytics also help companies identify emerging risks, optimize lending decisions, and improve customer personalization strategies.

What’s the role of cloud computing in scalable FinTech data management?

Cloud platforms enable cost-effective, scalable, and secure data storage and processing. They support real-time analytics, automated compliance checks, and AI-driven insights while reducing infrastructure costs. By adopting cloud-native architectures, FinTech firms gain agility in handling large volumes of financial transactions and customer interactions.

How can companies ensure security and compliance while scaling their data operations

Regulatory compliance requires a robust data governance framework that aligns with evolving financial regulations. Security strategies such as encryption, access control, and anonymization protect sensitive data while maintaining transparency. Regular security audits and AI-driven threat detection help prevent fraud and ensure data integrity across financial ecosystems.

How does 8allocate support FinTech companies in optimizing data management?

8allocate provides tailored data management solutions that help FinTech firms centralize their data, improve quality, and implement AI-powered analytics for enhanced risk management and compliance. Our expertise in scalable cloud architectures, real-time data pipelines, and regulatory frameworks ensures that financial institutions can harness data-driven insights while maintaining security and operational efficiency.

FinTech companies just emerged from an interesting year. Funding volume and deal count have declined since Q3 2022.

In the first nine months of 2023, FinTech companies raised $29 billion in 1,655 funding rounds, compared with 2,684 rounds worth $54 billion a year ago. Increasing capital costs forced FinTech companies to adopt more profitable practices and explore new revenue streams.

Source: S&P Global.  

But better times are ahead for FinTech companies, benefiting from the banking industry transformation, digital adoption, and ecommerce growth. Despite short-term economic pressures, many achieved growth in new markets. In Q3 2023, Brazilian Nubank posted a net profit of $300 million and is close to hitting $1 billion in profits in 2024.

McKinsey predicts FinTech industry revenues will grow almost three times faster than the traditional banking sector until 2028. But to make that happen, FinTech companies need to mature and evolve their products to reach wider audiences and improve revenue growth rates and EBITDA (as many investors pressure them).

6 Areas of Innovation in FinTech

Mature FinTech companies went through user acquisition sprees. Many are shifting from growth at all costs to profitability and revenue diversification. How will they do it?

Our team identified six areas of innovation that will mark this year and shape a more profitable future for FinTech. 

Expansion of Embedded Finance Segment

Payment is one of the fastest-growing FinTech sectors, and embedded finance transactions are the niche to watch. In 2023, the embedded finance market reached $83 billion. By 2026, Bain predicts that the embedded banking, lending, and insurance market will be worth $7 trillion.

Platform businesses are fueling the growth of embedded finance products. They aim to sustain customer experience by providing seamless checkout, payment, and lending experiences at the point of sale. However, they lose customers due to unsupported payment methods, incompatible bank accounts, or limited lending options.

From ecommerce to ride-sharing apps, businesses partner with payment and banking infrastructure providers. FinTech companies, aiming to offer new services to consumers faster with less regulatory red tape, team up with traditional players and infrastructure providers.

Source: Bain.  

Sunrise Banks helped Self Credit Builder and TrueConnect launch new lending products using its underwriting and loan approval infrastructure. For TrueConnect, Sunrise processed over 6,000 loans totaling $14.7 million.

Tide Bank partnered with Liberis, an embedded lending platform, to get innovative, personalized products and offer flexible repayment plans. Swiss supermarket chain Coop presented Coop Finance+ in late 2023 — a branded bank account and debit card — launched in collaboration with FinTech orchestrator provider Additiv. Investment and insurance products may follow.

Overall, 75% of FinTech companies expect that most financial products will be offered outside traditional channels in the next three years, according to the Global Embedded Finance Market Report 2024 from FinTech Futures. Consumer retail, telecom, and healthcare will attract the most interest.  In Europe, embedded finance growth is expected to approach EUR 150 billion by 2028, while the APAC market will exceed $300 billion by 2024.

Success depends on FinTech companies effectively externalizing their offerings to become ‘embedded’ partners. This requires strong expertise in scalable, secure cloud architecture development and API management.

We expect more embedded AI lenders to enter the market, providing hyper-personalized customer rates using analytics and ensuring a low rate of non-performing loans. Lendica offers an embedded product for upfront sales invoice payments for SMBs, with lending decisions made in seconds through machine learning analytics. Lama offers a fully automated underwriting experience in its embedded lending platform. Both help financial companies bring new digital products to the market faster. 

Generative AI Has Profitable Use Cases 

Generative AI has sparked interest in finance. According to EY, 75% of European finance leaders plan to increase Gen AI spending in 2024. However, some view Gen AI as a ‘gimmick’ with unproven ROI, especially compared to more established machine learning models.

Our take? Gen AI will not replace existing algorithms for credit scoring, underwriting, risk management, or financial market analysis. Instead, it will complement them by making the data more accessible to the average business user.

Thanks to retrieval augmented generation (RAG), companies can fine-tune open-source large language models (like GPT-3/4) to analyze and synthesize knowledge from private sources (e.g., a corporate policy database). Morgan Stanley prompt-tuned OpenAI’s GPT-4 model on its investment, business, and process documents to supply analysts with context-relevant replies. Morningstar fine-tuned the GPT-3.5 version of its library of investment research, editorial, and ratings. So, instead of opening a myriad of PDFs, the firm’s customers can send a quick question to the model and get a concise, precise response.

Foundation models can also improve customer service and engagement. Ally Bank piloted an AI-powered app to transcribe and summarize customer calls, saving hundreds of productive hours yearly. The information can also be used for sentiment analysis and service improvements.

Gen AI isn’t just about full automation. Many mid-office and back-office workflows in the financial sector will always need human oversight. But Gen AI apps can do much of the ‘heavy lifting,’ assisting humans with information-intensive jobs. Lucinity, a FinCrime prevention platform, released an AI copilot for financial fraud case investigation. SymphonyAI has a similar gen AI assistant for KYC, CDD, and AML investigations.

In the next few years, we expect more gen AI products at the ‘front,’ offering a fully conversational product interaction. This is appealing in personal finance and wealth management, where consumers seek personalized advice but can’t always afford a human consultant (or have one available 24/7). LTX, a bond trading platform, launched BondGPT — a conversational assistant suggesting optimal corporate bonds based on the user’s real-time liquidity needs and other criteria.

Gen AI has potential for the finance sector, but it’s challenging to implement. Over 40% of financial leaders, surveyed by EY, say they lack the proper data infrastructure, and 35% lack technology infrastructure. Moreover, AI systems come with a new range of cyber security and operational risks.  If you’re unsure where to start, 8allocate can advise in AI & ML model development. 

Smarter Automation For Compliance and Security 

High compliance costs undercut FinTechs’ path to profitability. Last year, 60% reported paying at least $250k in compliance fines and penalties, one-third — $500,000, according to Alloy.

The same survey shows that larger companies (1,000+ employees) were more likely to face $500K fines. Scaling compliance is difficult as data volume, diversity, and use cases multiply — and when entering new markets or product verticals.

Compliance doesn’t have to increase your budget when intelligent automation solutions are available for AML, KYC, fraud detection, and risk management.

Revolut developed a scalable machine-learning model to monitor real-time payment transactions and flag suspicious ones for manual investigation. The company also uses the AI-powered CUBE platform to receive real-time notifications on new regulatory announcements, enforcement actions, speeches, and other updates.

A slew of FinTech infrastructure providers offer modern, scalable, and competitively priced solutions for automatic risk and security management: Alloy, Middesk, Sila, Sardine, and others. Most of them rely on advanced data analytics models and AI. Pagos uses machine learning models to identify anomalies in payment data and determine the likelihood of fraud.

ML has proven its efficiency to the extent that the European Financial Action Task Force (FATF) actively encourages financial companies to use machine learning and data analytics for anti-money laundering and anti-terrorist financing (AML/CFT) monitoring. That said, even the most advanced models will fall short if your company lacks an appropriate data management strategy, so that’s the area to prioritize.

Point of Sale (POS) Lending Expands into New Verticals

Since 2020, BNPL platforms have proliferated fast, now valued at $25.4 billion globally and set to grow at a CAGR of 20%. The industry aims to expand offline to convert in-person transactions and new product/service categories.

Some 40% of US consumers are interested in BNPL offers for travel, home remodeling, medicine, and prescription services. ZeeFi, an Australian FinTech, launched a ‘Study Now, Pay Later’ product for educational services providers last year. Booking.com partnered with Affirm in September 2023 to offer a BNPL option in several markets. Neobank Upgrade acquired Uplift, a BNPL provider for travel companies, and plans to integrate the offer into its platform.

Leaders are eyeing the B2B payments sector, which is twice the size of the B2C payments sector. There are opportunities in NPD, such as advanced invoice payments and instant financing for inventory purchases. Mondu, for example, provides infrastructure for setting up digital trade accounts and receivable cash advances. 

New BNPL regulations are coming this year. The UK plans to regulate BNPL products like traditional lending products, and the EU plans to add BNPL under the existing credit umbrella regulation (the Consumer Credit Directive). The US doesn’t have direct regulations yet, but the Consumer Financial Protection Bureau (CFPB) has been looking into the market. 

BNPL lenders will need to demonstrate fair, ethical, and compliant operations. Strong data analytics and proactive monitoring help ensure equitable credit extension and a low rate of non-performing loans.

Smarter CFO Tech Stacks Wanted

Financial teams lack efficiency, especially in small businesses. Many still rely on paper checks and manually run accounts receivable (AR) and accounts payable (AP) workflows. Overall, 46% of SMBs say they’re too dependent on non-automated processes for financial tasks, according to 451 Research. Another 41% also admit managing cash flow is a challenge.

In larger companies, matters are marginally better. While a larger fraction of financial workflows are automated, CFOs struggle with other issues: 55% plan to take a proactive approach to revenue compliance checks and audits, and 71% want to increase the use of digital (planning) tools to make their supply chains more resilient, according to a 2023 CFO survey by Deloitte. 

Below is the typical job description of a CFO: strategic decision-making at the top and transactional and financial reporting at the bottom.

Source: Andreessen Horowitz 

At every level, CFOs could benefit from extra tools, especially those that promise to eliminate manual, repetitive (and often — error-prone) tasks. UK-based Teya (former SaltPay), for example, built out an integrated stack of payment, financial analytics, recordkeeping, and cash flow management tools for SMEs. The platform serves over  300,000 customers globally and boasts an impressive $9 billion valuation as of 2022. 

Pleo, once known as an expense management platform, substantially updated its product this year to become a one-stop-shop provider for mid to enterprise clients in Europe. The platform now has a direct integration with Oracle NetSuite ERP, plus new invoice management tools, virtual cards for vendor payments, and instant access to overdraft. 

Plenty of other gaps still exist in the FinOps stack, namely in tax and treasury management, stock options administration, and advanced financial analytics. Thanks to the rise of FinTech infrastructure, companies can now easily venture into adjacent niches. Expense management companies can create virtual cards and digital bank accounts to profit from interchange fees on all expenses. Software companies can provide better data for loan underwriting. As the ecosystem grows, more revenue sources emerge.  

FinTech Companies Diversify Revenue with Subscription Models 

Investors press for revenue growth and profitability. Subscription business models have proven effective in other industries and banking (where account-holding fees are still common).

The era of no-fee debit cards/current accounts isn’t over, as many companies seek recurring revenue from subscriptions. Cleo, a sassy PFM app, launched a Cleo Plus subscription in 2018. By 2019, it was the fastest-growing FInTech app globally. In 2022, the app reported $45 million in ARR and a 2.4X growth of the Cleo subscriber base.

Klarna kicked off 2024 with the launch of a subscription offer in the US. Klarna Plus lets users use Klarna’s One Time Card product with no fees, get double rewards points, and access special deals with popular brands. Affirm is also working on a subscription service to diversify its revenues.

Subscriptions are an extra revenue stream that delights existing consumers with added-value services and drives retention. They are less affected by market volatility (e.g., interest rates) and have fewer constraints (e.g., low upfront capital investment).

Conclusions

For years, FinTech companies had a technology edge over incumbents, offering digital account opening, faster payment processing, and lower fees. That’s changing. Over 94% of banks plan to invest in new payment infrastructure for cross-border and real-time payments. Many are partnering with BaaS platforms and FinTech infrastructure providers to improve customer experience. FinTech companies must continue to innovate and experiment with new revenue sources to retain and attract users.

Want to sharpen your product strategy for 2024 and beyond? 8allocate helps global companies level-set technical feasibility and user needs as part of our product discovery service and transform your vision into life with dedicated project teams. Contact us to learn how we help FinTech companies grow profitable and stay competitive. 

Frequently Asked Questions

Quick Guide to Common Questions

What are the most significant FinTech trends shaping the industry in 2024?

FinTech is evolving with embedded finance, generative AI, smarter compliance automation, expanded POS lending, advanced CFO tech stacks, and subscription-based revenue models. Companies are focusing on profitability, improving operational efficiency, and expanding into new verticals while integrating AI and automation into financial workflows.

How is embedded finance transforming financial services?

Embedded finance is allowing businesses to integrate financial products into non-financial platforms, enabling seamless payments, lending, and insurance at the point of sale. Companies like Tide Bank and Coop Finance+ are partnering with FinTech providers to offer financial services without traditional banking infrastructure. Success in this space depends on strong API management, cloud scalability, and security.

What are the most promising applications of generative AI in FinTech?

Generative AI is enhancing financial workflows rather than replacing existing machine learning models. It is being used for customer support automation, investment research, regulatory compliance monitoring, and fraud detection. Financial institutions like Morgan Stanley and Morningstar have fine-tuned LLMs to provide real-time insights, while companies like Lucinity and SymphonyAI are applying AI to financial crime prevention and compliance operations.

How is AI improving financial compliance and security?

AI-driven compliance automation is helping financial institutions reduce operational risks, streamline KYC/AML processes, and minimize fraud. Companies are leveraging machine learning to monitor transactions in real-time, analyze behavioral patterns, and proactively detect suspicious activity. Regulatory bodies are increasingly recommending AI for compliance, but effective implementation requires a strong data management strategy.

How does 8allocate support FinTech companies in AI and digital transformation?

8allocate provides AI and ML implementation, data-driven financial software solutions, and compliance-driven AI solutions tailored for FinTech businesses. With expertise in AI-powered automation, predictive analytics, and digital transformation strategies, 8allocate helps FinTech companies improve operational efficiency, optimize risk management, and scale their technology infrastructure.

Artificial intelligence (AI) is entering the era of mainstream. But like any emerging technology, AI carries both benefits and risks. 

Over 80% of users are worried about the security risks posed by generative AI apps (like ChatGPT). Model cybersecurity, user data privacy, and ethical considerations, in particular, are among common concerns. 

This post explains how to build secure AI systems by addressing potential vulnerabilities at three levels: Data, development, and model operations. 

The Three Pillars of AI Security 

Over half of global businesses don’t have appropriate deference strategies against AI attacks despite being concerned about risk proliferation.

But most are looking to change that. Better AI trust, risk, and security management (AI TRiSM) is one of the major tech trends for 2024. This Gartner-proposed framework encapsulates different approaches business leaders take to build reliable, fair, and secure AI applications and reduce operational risks. 

AITRiSM principles include better model monitoring and explainability, which allow leaders to understand better how the model performs in real-world settings, detect biases early on, and address possible security vulnerabilities. ModelOps, in turn, is a set of practices and technologies aimed at improving model governance and lifecycle management. Similar to DevOps, in software engineering, ModelOps brings greater predictability to model development through better training data management practices, model version control (to enable roll-back, re-training, and fine-tuning), extensive testing, and ongoing monitoring. 

These two practices further build up a wider range of mechanisms for ensuring high AI application security and data privacy, as well as air-tight protection against the common AI security risks through AI data governance.

AI Data Security 

Machine learning and deep learning require ample, variable, and vetted datasets for model training validation and retraining. Training datasets must also be representative to prevent model biases, but properly pre-cleansed and anonymized to ensure compliance with global privacy laws, which not all businesses do. 

Haphazard AI data storage practices and lack of security for data in transit can also lead to data theft or data poisoning — another two major risks. 

Risk: Privacy and Copyright Breaches 

Generally, you need 10X more data than model training parameters to develop a reliable AI model. However, global privacy laws limit AI developers’ ability to use customer data for model training. 

Still, many developers, knowingly or not, train models on personally identifiable information (PII), obtained in bypass of existing laws. Spotify had to settle a $5.4 million GDPR fine because it didn’t provide proper disclosures on how data it collected on users was used by its machine learning model. OpenAI also received one class action suit for using allegedly stolen information of Internet users without their knowledge or consent for model training. And another one from the New York Times for stealing its copyrighted content (with more likely to come soon). 

Newly emerging AI regulations like the EU AI Act and the AI Regulation Bill from the UK government plan to implement more stringent regulations to ensure appropriate levels of data confidentiality and limit AI developers’ ability to inject user data without their explicit consent and acknowledgment.  

Solution: Data Minimization and Data Anonymization 

Without robust anonymization, sensitive data can become part of the model’s training dataset, which poses two types of risks:

• Legal and compliance if your organization doesn’t provide subsequent privacy protection. 

• Cybersecurity: Threat actors can tamper with the model to reconstruct its training data and perform data theft. 

To address both, use appropriate privacy-preserving techniques like homomorphic encryption (HE), differential privacy, secure multiparty computation (SMPC), or federated learning techniques as part of AI and ML implementation and integration.
Sony, for example, created a specialized privacy toolbox that combines several methods for ensuring high data security and privacy. Users can automatically remove sensitive elements from training datasets, opt to use privacy-preserving training algorithms such as DP-SGD, and benchmark how the model can resist known attack techniques like model inversion or membership inference attacks (MIA).

Source: Sony

Risk: Data Poisoning

Cyber-exploits such as data poisoning aim to introduce biases, errors, or other vulnerabilities into the model training data. On a smaller scale, model data poisoning leads to concept drift. For example, a computer vision system starts classifying Ford cars as Ferraris. 

On a larger scale, such cases can render the system fully inefficient. A cybersecurity firm recently managed to gain access to 723 accounts with exposed HuggingFace API tokens, held by companies like Meta, Microsoft, Google, and VMware among others. This vulnerability effectively allowed the team to have full control over the training data repositories, which opened a direct path to data poisoning attacks. 

Solution: Data Validation and Ongoing Model Monitoring 

OWASP AI Security and Privacy Guide provides several must-follow practices to prevent data poisoning in AI systems: 

• Implement automatic data quality checks and privacy controls into data ingestion pipelines to validate the accuracy of incoming data. 

• Add multiple layers of protection to training data storage: encryption, firewalls, and secure data transfer protocols, especially when integrating AI into existing platforms.

• Implement advanced model monitoring to detect anomalies in model performance and abnormalities in training data (e.g., odd data labels or sudden changes in the data distribution patterns). 

Another helpful practice top AI teams use is training models on different subsets of training data (when possible). Then use an ensemble of these models to make predictions. By doing so, data poisoning attacks can be minimized since the attacker would need to compromise multiple models. 

Secure Model Development 

Vulnerabilities, in regular software and AI models, can be acquired or created. Poor code or overlooked system dependencies can create security flaws in your model, an attacker could explore. Furthermore, acquired components, such as open-source libraries or pre-trained models, may contain inherent flaws that put your company at risk.

Risk: Compromised Model Components 

New AI developer tools, libraries, frameworks, platforms, and other application components emerge every day. Commoditized access to open-source and commercial solutions massively accelerated the speed of new model development. However, greater reliance on third-party technologies also increases risk exposure. 

Cyber researchers have demonstrated how a tampered version of a machine learning model can be used to distribute malware in an organization. Researchers conducted a test with the popular PyTorch framework. But the same attack mechanics can be adapted to target other popular ML libraries like TensorFlow, scikit-learn, and Keras. In other words, companies that take pre-trained machine learning models from public repositories like HuggingFace or TensorFlow Hub are at risk of bringing cyber threats. 

Solution: Audit Your Toolchain 

Make sure you procure all hardware and software for AI application development from reputable sources. This includes data, software libraries, middleware, external APIs, on-premises, and cloud infrastructure. NCSC provides extensive recommendations for improving supply chain governance for technology acquisitions (including AI systems). 

Also, evaluate all pre-trained models for signs of tampering or hijacking. Modern security tools can scan model codes for evidence of abuse (for example, malicious code stored within the model learning files). 

As a general principle, apply secure software development practices to AI models and use existing standards for application security controls and operational security — e.g., ISO/IEC 27090 AI infosec or the more general OWASP Application Security Verification Standard (ASVS).  

Risk: Subpar Model Performance 

Generative AI applications require substantial effort to develop. But on average, companies shelve over 80% of machine learning models even before deploying them to production. Yet, even a deployed model may not bring the expected “dividends”. 

By the time a trained model enters production, its efficiency may be lower than expected because the real-world data differs a lot from the training one. The Sophos Group demonstrated how well-performing malware detection models degraded within a few weeks in production. So some teams may be tempted to rush the model release and cut some corners at the development or testing stages. 

Obviously, this undermines model security and creates avoidable technical debt, which then leads to higher operating costs. Cloud infrastructure and/or local data center costs for large AI models can easily reach seven figures per month, especially as your user base grows. 

Solution: Pay Attention to Technical Debt 

Low model performance and high operating costs are often the result of subpar development decisions in favor of short-term results, but at the expense of long-term benefits. Google team outlined several factors that accelerate technical debt in machine learning models:

• Entanglements in machine learning packages. When the model mixes data sources, it’s extremely difficult to make isolated model improvements (e.g., optimize individual hyperparameters).  

• Hidden feedback loops indicate false correlations in data, leading to biased model outputs. 

• Undeclared consumers, aka applications and other systems, use the output of a model as an input to another component of the system without the developers’ knowledge. 

• A wide range of data dependency issues such as underutilized packages, available, but mostly unneeded for the model. 

…And the more familiar issues, driving technical debt in other software systems, like glue code, data pipeline jungles, and configuration debt. 

By acknowledging and addressing accumulated technical debt in AI applications, developers can reduce the system’s operating costs and improve its performance. Likewise, by establishing strong ModelOps practices of continuous integration, model versioning, and automated model re-training, among others, AI teams can release more robust and secure models faster but without compromising on quality. 

Secure Model Operations 

Productized AI is the ultimate destination for many businesses. However, exposure to real-world conditions and external users also means increased risk exposure. 

Hackers can stage direct adversarial attacks to tamper with the model performance (e.g., to disclose sensitive training data, compromise its outputs, or use the model interface to gain access to other enterprise systems). A group of security researchers already showed how hackers could potentially use the GPT model to deliver malicious code packages into developers’ environments. All of this calls for appropriate AI model security measures in production environments. 

Risk: Data leakage and disclosures 

User-facing AI models must have appropriate security mechanisms to prevent: 

• Disclosure of user inputs (e.g., prompts, input data values, etc). 
• Disclosure of model outputs to any unauthorized third parties 

Forcing a model to disclose either of the above is easier than you may think. Before that, researchers from the Google Brain project, also demonstrated that with a bit of fiddling, you can access data, saved in a generative model memory. In their case, it was a fake social security number. 

Accidental data leakage also happens quite often, mostly due to technical misconfiguration. OpenAI had to deal with its first data leak in March 2023 when paid users’ names, email addresses, and credit card numbers (the last four digits only) became publicly accessible for a brief period due to a system glitch. 

Solution: Strong data management practices

To prevent accidental or malicious data leakages implement strong data management practices. 

Implement robust access and identity management (IAM) controls to ensure that only authorized users (and applications) have access to the model data. Use a model version control system (VCS) to keep a better track of model variants and prevent releases of models trained on sensitive data. Apply encryption to both data in rest and in transit to minimize the odds of breaches. For generative AI models, implement input sanitization mechanisms to prevent certain characters or data types (associated with prompt injection) from being entered into the system. 

To secure its AI products for finance, JPMorgan established a new AIgoCRYPT Center of Excellence (CoE) in 2022 — a unit, focused on designing, sharing, and implementing new techniques for secure computation of encrypted data. In particular, the team performs research into fully homomorphic encryption (FHE), privacy-preserving machine learning (PPML), privacy-preserving federated learning (PPFL), and zero-knowledge proofs (ZK) among other areas. A lot of the produced research is public. 

Risk: Direct Adversarial Attacks 

Evasion attacks are another common type of AI security risk. In such cases, attackers try to alter the model behavior to either tweak the model to act to their advantage or to compromise its performance. Researchers from Tencent demonstrated that adding small stickers in an intersection can compromise Tesla’s autopilot system to drive into the wrong lane. 

Evasion attacks often rely on prompt injections — submission of malicious content into system inputs/prompts to manipulate its behavior. But they may be also staged in other ways by leveraging vulnerabilities in AI application design e.g., unprotected APIs, weak access controls, etc. 

Apart from causing performance issues, hackers may also want to nick the entire AI model. This can be a brute force attack on a private code repository or phishing attacks, aimed at users of MLaaS providers. 

Solution: Enforce Strong Security Policies  

Asset management is the pillar of strong cybersecurity. You should have a clear view of where all the model assets reside and the ability to track, authenticate, version control, and restore them in case of a compromise. 

Similarly, enforce strong data management principles. Use the principle of the least privilege when it comes to accessing model training data. Implement different degrees of security, based on the type of data and content the AI systems interact with. 

To communicate model security to downstream users, you can also implement cryptographic model signing, which ensures a certain level of trust and can be verified by other users. 

Risk: Model Drift

Model drift is a progressive decade of machine learning’s predictive capabilities. It mostly happens when the conditions in the analyzed conditions have changed, but the model lacks the knowledge to properly process these. For example, a mortgage risk prediction model, trained on houses under $1 million loses its accuracy when prices increase. 

In other words: the model loses its accuracy and this can lead to some major consequences. Take it from Zillow, whose proprietary property valuation algorithm over-estimated the value of the houses the company purchased in Q3 and Q4 by over $500 million. 

The two common types of model drift are: 

• Data drift happens when the distribution of the input data changes over time. For example, housing prices change. 

Concept drift happens when the task that the model was trained to do changes. For example, a new type of payment fraud has emerged and the model cannot detect it. Concept drift can be sudden, gradual, incremental, or reoccurring.

Source: Arxiv 

In either case, drift is no good for businesses as it can create model biases, inaccuracies, or even render them fully ineffective. 

Solution: Model Monitoring and Automatic Retraining 

To ensure the model outputs remain accurate over time, implement automatic drift detection. Algorithms like Population Stability Index (PSI) and Kolmogorov-Smirnov (K-S) test among others are great at detecting data drift. Concept drift detection requires a combination of model performance metrics (e.g., recall, precision, F1-score, etc) to evaluate its behavior over time. Comprehensive monitoring helps detect both natural drift and signs of malicious intrusion. 

As a next step, consider implementing automatic model retraining when the model performance dips below the acceptable threshold. Even a 1% improvement in model accuracy performance can lead to substantial benefits: Better operational insights, faster operating speeds, and higher revenue. 

Conclusions 

AI application security hinges on businesses’ ability to apply strong data management governance, secure application development principles, and robust security controls for deployed products. 

By addressing security at three levels — data, development, and deployment, you can minimize the risks of dealing with both current and emerging threats. Use appropriate privacy-preserving techniques in combination with automatic data validation to protect data inputs and outputs. Be diligent about your AI development toolkit and system architecture to prevent the accumulation of technical debt and the proliferation of different vulnerabilities. 

Implement proper security controls for access and identity management. Ensure comprehensive monitoring to detect signs of external tempering and natural decay in model performance. 8allocate AI and ML experts would be delighted to further advise you on the optimal AI security controls and best practices. Contact us to schedule an audit.

Frequently Asked Questions

Quick Guide to Common Questions

What are the most significant security risks in AI systems?

The most common risks include data privacy breaches, data poisoning, compromised model components, adversarial attacks, and model drift. These threats can compromise AI integrity, introduce biases, and expose sensitive information.

How can AI models be protected against data poisoning?

Implement data validation, anomaly detection, and strong encryption in data pipelines. Multiple models should be trained on different data subsets to reduce the impact of poisoned data.

What’s the best way to secure AI models from adversarial attacks?

Enforce strict access controls, API security, and prompt sanitization. Apply cryptographic model signing and use adversarial training techniques to strengthen model robustness.

How can companies prevent AI model drift?

Set up continuous model monitoring with automated drift detection algorithms (e.g., PSI, K-S test). Retrain models automatically when performance degrades to maintain accuracy and reliability.

How do businesses ensure AI data privacy and regulatory compliance?

Use data minimization, anonymization, and encryption techniques like homomorphic encryption and federated learning. Align AI governance with GDPR, AI Act, and other regulations.

What security measures should be in place when using third-party AI models?

Conduct regular audits of pre-trained models, scan for vulnerabilities, and verify model provenance. Follow secure development practices such as ISO/IEC 27090 and OWASP AI Security Guidelines.

How can organizations reduce AI technical debt?

Adopt ModelOps best practices, version control, and continuous integration to avoid hidden dependencies, bias accumulation, and scalability bottlenecks in AI systems.

Resilient and determined — that’s how 8allocate teams powered through another challenging year ridden by ongoing geopolitical tensions and economic turbulence. Heightened cybersecurity risks and growing concerns about the implications of various emerging technologies (like generative AI) also add to the conundrum. 

Globally, the business climate remains challenging, but many leaders see disruption as an opportunity to innovate — and so does 8allocate. 

Harnessing New Growth Opportunities Amidst Disruption 

Driven by the increased pace of digitization, our business volume increased by 85% through 2022. In 2023, we added another 16%, effectively doubling our size by scaling existing client engagements and entering new partnerships. As the global tech talent shortages remain rampant and the demand for AI expertise increases, we expect 2024 to be another strong year for our company, as well as the global businesses we support. 

Our core focus remains on data management, a segment that sees continuous demand. With greater adoption of data analytics and, more recently, advanced machine learning (ML) and artificial intelligence (AI) models, business leaders are recognizing a stronger need for robust, scalable, low-latency data infrastructures. Custom large language models (LLMs), for example, require 570GB to 45TB of training data. Data generation, however, is often constrained by data privacy and compliance laws. 

As we mentioned in our outlook of the key tech trends in 2024, new data architecture patterns are required to support faster, more affordable data provisioning for analytics systems. Better data governance and transparency are also in focus as companies start exploring new AI applications for achieving ESG targets. 

8allocate has already helped our clients deploy advanced data analytics solutions for customer sentiment analysis (based on NLP), content recommendations, and financial market modeling. In 2024, we’re thrilled to support further feature maturation strategies for the existing clients and take on new challenging analytical problems for ML/AI application development. 

Sector-wise, FinTech, EdTech, Logistics, and SaaS remain our key focus areas. This year, we expect some of the earlier technology trends in EdTech to further mature. Apart from test generation, Generative AI will likely expand to new use cases. For example, provide real-time voice guidance to language learners or facilitate information look-up in corporate eLearning platforms. 

Similarly, we’re eager to see how the usage of AI in FinTech will evolve in 2024. Fraud detection, product recommenders, and claims processing are already some of the more established use cases. According to NVIDIA, 90% of financial services companies are either assessing AI or already using it in production, so 2024 will likely be the year of innovative financial products. 

We expect further expansion of the “embedded finance” solutions — real-time, digital delivery of financial services through multiple interfaces and channel partners (e.g., insurance companies, online merchants, marketplace apps, etc). AI also holds great promise for wealth and asset management, especially for retail investors, dramatically reducing the learning curve for novice investors (which, in turn, drives user acquisition). 

As global supply chains remain fractured and new sourcing strategies emerge, the logistics sector has also turned greater attention to emerging technologies. Location-based services (LBS), coupled with affordable sensor technologies, now allow shipping companies to track assets in real time and obtain new insights for route optimization. In 2024, 50% of supply chain companies plan to invest in applications with advanced analytics and AI capabilities to further streamline supply planning and execution. Other exciting areas of innovation include predictive inventory management, optimized warehousing operations, and a greater focus on sustainability as leaders are pressed to reduce their carbon footprints. 

Broadening Our Operating Horizon 

Over the last decade, 8allocate has forged strong client partnerships in the US and the UK markets. In 2023, we also expanded to South Africa and APAC, supporting local businesses on their journeys to digital excellence. We’ve conducted strategic operating sessions online and in person, looking for new ways to improve the synergy and alignment between our clients and our company. Our CSAT scores remain above 95% on average, and we’d strive to increase them to 99.99%! 

This year, we’re looking forward to connecting with more companies in the Nordics and MENA region. The Nordic countries (Sweden, Norway, Finland, Denmark) have a burgeoning tech scene, with 38% of funding going towards impact startups — companies committed to putting emerging technologies at the service of ESG. 

The Middle East and Northern Africa (MENA) also has emerging digital players in the FinTech, education, retail, and e-commerce sectors. In the first half of 2023, the local startups attracted a record $1.1 billion in funding. Creating a knowledge-based economy in place of a resource-dependent one is a key driver of digital transformation efforts in many MENA countries. And that’s a mission 8allocate would love to support.

Staying Committed to Operational Excellence  

8allocate is an end-to-end software development partner. We’re supporting businesses at every stage of their new product development journey — from pre-project consulting on technology stack and product discovery to ongoing product development with dedicated teams.    

Big data analytics, AI/ML solutions, and cloud remain our key focus areas, where we’ve proven to add the most value for our clients. But we’re also increasing our adjacent competencies. 

Last year, we invested extra resources in expanding our UX/UI department, bringing new talent on board, and upskilling the workforce. Great customer experience has become the key differentiator for brands and delivers tangible benefits: Lower customer service costs, increased intention for future purchases, and higher price tolerance, among many others. 

Our team has also improved our framework for minimal viable product (MVP) development. By combining Agile software development practices with an incremental approach to new product development, our framework allows small and medium-sized companies to launch new products faster and at a more affordable cost.  

Internally, we’ve also been experimenting with generative AI — one of the biggest software engineering trends of 2024. To eliminate menial and manual work, our engineering team has adopted several commercial AI developer tools to optimize engineering and IT service delivery functions. During the pilots, our benchmarks showed a 2X increase in productivity among software engineers. 

When used responsibly, Gen AI can act as a force multiplier, helping businesses accomplish more meaningful work with less effort and fewer resources. The technology is also highly flexible and can be adapted to multiple use cases. Besides coding copilots, we’re also experimenting with internal gen AI models to support our Sales and HR departments. 

Great Talent is the Backbone of Our Success 

8allocate continues to operate from three main locations: Poland, Ukraine, and Romania. Central Eastern Europe (CEE) remains one of the best regions to hire software engineering talent. Ukraine, Romania, and Poland are home to over 870,000 tech specialists with a broad range of competencies in application development, legacy software modernization, quality assurance (QA), and emerging technologies (AI, IoT, blockchain, etc). Great knowledge of English, close cultural proximity to Western countries, and strong work ethics come as some bonuses. 

This year, we have welcomed many talented colleagues, including junior talents. We strongly believe that nurturing young professionals is essential to ensure strong industry growth and sustained interest in pursuing STEM degrees. In the past year, we have also launched several professional training programs and invested in professional certifications for our team. For 2024, we have already allocated a higher budget towards new learning and development (L&D) programs. 

Apart from supporting our workforce, we continue to positively contribute to our society by bringing new business to the country. 8allocate also regularly shares some of our revenues with several non-profit organizations supporting social welfare and infrastructure restoration in Ukraine. Hence, we would like to thank our existing clients for their continued trust in our business, as it helps our country recover faster. 

Accomplishing More in 2024 

Over the past year, we’ve also made new connections with established and emerging digital leaders at the FinTech Connect, Dublin Tech Summit, TechEx London, and other leading industry events. They have helped us broaden our knowledge of the respective markets and develop new technical know-how for better product shipping. 

In early 2024, the 8allocate team plans to head north for Stockholm Fintech Week and then catch up with our partners and make new friends at the MoneyLIVE Summit in the UK. We’d love to connect in person if you’re around! 

As for the broader outlook, 8allocate remains confident in our ability to deliver stellar software engineering services for our clients through 2024. We’re entering this year with a renewed penchant for innovation, learning, and operational improvements.