Data is ever-present, but its value isn’t. Despite unprecedented data availability (and sometimes because of it), organizations struggle to transform the immense data reserves into actionable insights for decision-making.
Manual workflows, legacy data architecture, and the overall lack of structure in data management practices hamper businesses’ abilities to move from baseline descriptive analytics to predictive and prescriptive solutions.
This post explains how you can break through the existing barriers and hit the plateau of data analytics productivity in your organization with the right data management strategy.
How to Create a Data Management Strategy
A data management strategy is your roadmap for putting data at the service of the key business objectives. It helps you formalize a set of use cases for data analytics and AI adoption, plus chart a tech-led path forward to achieving these through strategic process transformation.
To move from data management chaos to clarity, you need to complete the following steps:
- Document your current data architecture
- Identify data integration requirements
- Select the optimal analytics solutions
- Develop a data governance strategy
1. Analyze Your Current Data Architecture
Any type of strategy requires a starting point. Your first goal is to evaluate your current data management practices and workflows as part of your data management strategy.
Our data analytics specialists recommend to:
- Analyze existing storage infrastructure (e.g., storage type, location, operating costs)
- Map different data sources; available and missing data integrations
- Document different data formats in use (e.g., numeric, object, graphic data formats)
- Identify pain points and blockers in current workflows
In other words: You need to create a detailed data topology — a blueprint of your current data, which classifies, clusters, and visualizes the relationships between different elements of your data architecture.
Data topology helps answer the following questions:
- How data is collected by different systems (format, frequency, compliance)?
- Where does it get stored afterward (destination, security, access controls)?
- Who has access to it (apps, people, analytics tools)?
- Where extra data integration scenarios are required?
With a complete baseline view, you can make more sound decisions on implementing new data architectures to enable higher data availability to different users, improve cost-effectiveness, and strengthen data governance and security. This is where a robust data management strategy comes into play.
Sample data architecture modernization scenario
Source: McKinsey.
Components of Data Architecture
Data architecture defines how data assets are conceptualized, logically expressed, and physically maintained. It also establishes how different data management systems will work together.
The main components of data architecture include:
- Application databases
Most software systems generate and store bounded data (e.g., about a transaction record, sales total, or employee total work hours). These raw data sources generate the most value for businesses but are the hardest to procure without auxiliary data analytics systems.
- Data warehouse
A data warehouse (DWH) aggregates data from different app databases and other data sources into a centralized, consistent repository. All incoming data undergoes different transformation processes to meet the predefined structural standards (schemas). Warehoused data can be then easily shared with connected business intelligence (BI) tools and analytical applications.
- Data mart
A data mart is a smaller-scale data warehouse, which contains bounded data subsets for a line of business or subject area. Instead of querying the entire DWH, business users can parse data subsets to generate new insights faster.
- Data lake
A data lake houses large volumes of structured and unstructured data in a cost-effective manner. Unlike DWHs, data lakes don’t require applications of any specific schemas, which reduces the data transformation costs and increases data availability. Data lakes are mostly used to store raw data for custom machine learning (ML) and deep learning (DL) projects.
- Data pipelines
A data pipeline is a set of processing techniques for ingesting raw data from various sources to various data stores (data lakes, data marts, or data warehouses). Pipelines apply filtering, anonymization, and aggregation to ensure data standardization and effective integration. In other words: Data pipelines help deliver “consumable” data to applications that need it.
- Application programming interfaces (APIs)
APIs can expose certain data from applications to enable real-time data exchanges between different systems. They can be used to specify data structure, access methods, and security controls for the selected data flows, which makes them a solid option for processing recurring data analytics jobs.
- Analytics tools
All of the above data architecture components are built to pursue one goal: to enable streamlined, cost-effective, and scalable data delivery to various analytics solutions. These can range from SaaS and self-service BI tools to open-source analytics algorithms and custom-built AI solutions.
Types of Data Architectures
There are different conceptual ways to organize data management, using the above structural elements.
Popular enterprise data architecture frameworks include TOGAF, DAMA-DMBOK 2, and the Zachman Framework for Enterprise Architecture. These provide the essential IT best practices for data modeling and design.
The latest trends in data architecture, however, promote the usage of data fabric and data mesh architectures.
Data fabric architecture promotes the creation of an integrated data exchange layer (fabric), governed by automated processes and continuous metadata discovery. Effectively, you create a set of data services that provide consistent capabilities across all connected environments, including hybrid and multi-cloud ones.
Data fabric reduces the time for integration design by 30%, deployment by 30%, and maintenance by 70% because the technology designs draw on the ability to use/reuse and combine different data integration styles.
Data mesh architecture established decentralized, domain-driven data architecture, where all corporate data gets treated as a product. Each business domain (e.g., sales, customer support, finance) is appointed as product managers over the datasets they produce and empowered to improve data quality, accessibility, documentation, and access. Such a federated strategy enables faster, self-service access to insights and reduces dependencies on the IT teams.
Data mesh architecture promotes the usage of multiple data lakes, marts, and warehouses as federated data repositories. Data meshes also frequently rely on data fabric to automate the development of new data products and implement global data governance.
2. Determine the Necessary Data Integration Scenarios
Data silos is the biggest barrier to data-driven operations. When data is undiscoverable, restricted in access, or incomplete, your data analytics will never be reliable and your teams — productive.
Four in five respondents say teams throughout their organizations are siloed and another 68% admit that their work is negatively impacted by lack of visibility into cross-functional projects.
The bigger problem? Over 40% of companies said that the volume of data silos has actually increased over the last year and only 10% have succeeded in improving company-wide information flows.
Manual, unstandardized workflows are often the first reason behind mounting data silos. Legacy software and lack of data integrations are the second.
A big part of your data management strategy should be focused on determining the necessary set of data integrations to break down the silos and enable automated, data-driven process execution.
Popular Data Integration Strategies
To ensure effective data flow between two (or more) systems, data engineers apply the following strategies:
- Data consolidation assumes the creation of a centralized, unified data store for aggregating all corporate information. Such a “single source of truth” serves data to all connected applications and supported analytics use cases. It’s easier to secure, govern, and audit, yet may lack the scalability to support a larger number of analytics applications due to latency issues.
- Data federation technique established a meta-database management system, which united autonomous database systems into a single, virtual federated database. The federated virtual database sends your queries to the relevant data source to retrieve the required data on demand. The advantage is concurrent, near-real-time access to data, stored in multiple sources (without physically moving the data). The downside is that the query performance speed isn’t as good as for data warehouses. This approach also requires careful query structuring to avoid logical errors, as well as quality controls for data reliability and usability.
- Data propagation requires moving data from one source (e.g., an application database) to another (e.g., a data warehouse) on an event-driven basis. You can program synchronous or asynchronous data replication in the target environments using EDR (enterprise data replication) and EAI (enterprise application integration) software. EAI helps exchange data, messages, and transactional information in real time. EDR, in turn, is used to dispatch large data volumes to other databases using base triggers and logs.
- Middleware data integration approach uses third-party applications to facilitate data transfers between the source systems and a target destination. Some middleware services also offer in-memory database and data cache services, as well as API management tools. Middleware solutions come in handy when you want to access data from legacy systems without moving it and make it usable for more modern analytics systems.
- Data virtualization software provides an interface for aggregating structured and unstructured data sources for virtual viewing through a dashboard. This way, you can virtually integrate data stored in different data models without moving it. Effectively, this technique promotes the discovery of metadata and presents users with a bird-eye view of the company’s data from a single dashboard regardless of the data’s location.
- Data warehousing implies data replication in a new target location (a DWH). During the replication processes, all data is cleansed, standardized, and transformed, based on the specified database schema type. Data warehouses promote better data quality and integrity, thanks to all the transformation steps. However, such solutions can be expensive to maintain.
3. Decide On Your Data Analytics Setup
Data architecture design is guided by the selected analytics use cases e.g., better customer segmentation for sales prospecting or automated credit scoring.
Each of the selected cases can be tackled with different analytical approaches (statistical inference, regression analysis, etc.) and different types of analytics solutions.
Your options include:
- Self-service BI tools are software apps that provide visual interfaces for accessing, analyzing, and visualizing data. Such tools enable your teams to interact with available datasets directly without relying on the IT department for model creation or report generation. However, to benefit from self-service BI tools you’ll need a strong data governance strategy in place to ensure that the right people have access to the right data. Examples include Microsoft Power BI, Tableau, Looker by Google, and SAP BusinessObjects BI Suite among others.
- Open-source analytics solutions are community-design and maintained analytics models. The open nature means that you get full access to the source code and can modify the solution to best fit your needs. Popular open-source analytics tools include H2O (distributed in-memory machine learning platform), Konstanz Information Miner (KNIME) (a low-code data science platform), TensorFlow (software library for machine learning), and Apache PredictionIO (an open-source machine learning server).
- Proprietary Platform as a Service (PaaS) solutions provide a cloud-based environment and pre-made tools users can leverage to build different analytics assets and solutions. Effectively, you get a toolkit for kickstarting custom analytics application development, deployment, and lifecycle management. These include solutions from major cloud providers such as Microsoft Intelligent Data Platform, AWS SageMaker, and IBM Watson Studio.
The combination of the above solutions should cover most of the analytics use cases, starting from the simplest ones like regular report generation, and all the way to custom AI model development.
Our big data analytics team would be delighted to advise you on the optimal analytics tech stack for your project and further explain the benefits and constraints of using different analytical approaches.
4. Create a Formal Data Governance Policy
No data management strategy is complete without a chapter on data governance.
Data governance is a set of policies, guidelines, and provisions for managing the company’s data assets. It ensures that data is effectively managed, protected, and used to support business objectives.
Source: IBM
A well-documented and universally understood data governance policy is crucial for cultivating high data standards and scaled adoption of analytics without exposure to extra compliance or security risks.
Effectively, it’s designed to shape the right behaviors around data quality, privacy, and management for structured and unstructured data. These policies are then codified into all the analytics applications and development environments to achieve streamlined adherence and optimal levels of protection.
Components of a Data Governance Strategy
The Data Governance Institute establishes the following components of an effective data governance strategy:
- Mission and Value
- Beneficiaries
- Data Products
- Controls
- Accountabilities
- Decision Rights
- Rules
- Processes
- Tools
Source: DGI
Such a framework helps establish clear areas of accountability among different user groups, establish clear oversight models, and cultivate the right standards of data collection, usage, and operationalization. It’s an ongoing process, which helps your organization establish higher levels of data maturity and capture more value from adopted analytics solutions.
Conclusion
A data management strategy helps companies understand their current levels of data maturity and progress towards the desired to-be state — becoming a data-driven organization.
It can seem like a complex journey, especially given the scope of planned work — from an initial data asset inventory to new data architecture development and selection of the best-fit analytical tools and approaches. But it can be made easier with the right software development partner by your side. Implementing a robust data management strategy is key to this transformation, ensuring that your organization is well-equipped to handle the challenges and opportunities of the data-driven era.
How 8allocate Can Help Build a Robust Data Management Strategy
8allocate helps global leaders unleash the power of Big Data with high-concurrency distributed analytical systems, scalable, cost-effective data storage, and custom analytics solutions, boasting the highest level of data security. Our data management strategy ensures that we deliver the best solutions to our clients.
Our data engineering teams, skilled in data management strategy, have already helped develop:
- Scalable data pipelines to support large-scale machine learning projects
- Robust data lakehouse architectures on Azure Databricks
- AI-driven Revenue Growth Intelligence product for the US market
Partner up with world-class data engineers and machine learning specialists. Contact us to receive a personalized presentation on our capabilities, service lines, and successfully delivered projects.
Frequently Asked Questions
Quick Guide to Common Questions
Why is a data management strategy essential for businesses?
A data management strategy provides a structured approach to organizing, storing, integrating, and analyzing data to support business objectives. Without a clear strategy, companies struggle with data silos, inconsistent data quality, and inefficient decision-making processes. A well-defined strategy enables higher data availability, improved governance, and stronger security controls.
What are the key steps in building a data management strategy?
Developing a data management strategy involves four main steps:
- Assess your current data architecture – Evaluate storage infrastructure, data sources, formats, and integration gaps.
- Determine necessary data integration scenarios – Break down data silos with appropriate integration frameworks like data lakes, warehouses, or APIs.
- Select an optimal data analytics setup – Choose between self-service BI tools, open-source analytics, or cloud-based AI/ML solutions.
- Create a data governance framework – Establish policies for data security, compliance, and access control to ensure responsible data use.
What are the most common challenges in data management?
Companies often face challenges such as:
- Data silos – Information is trapped in isolated systems, limiting accessibility.
- Poor data quality – Inconsistent or incomplete data reduces analytics accuracy.
- Legacy infrastructure – Outdated storage and processing systems slow down data retrieval.
- Security and compliance risks – Without proper governance, businesses risk regulatory violations and data breaches.
How does data integration improve business intelligence and analytics?
Data integration ensures seamless connectivity between different data sources, making it easier to extract insights from multiple systems. Approaches like data warehousing, data lakes, and real-time API connections help businesses consolidate and analyze information faster, leading to improved decision-making, operational efficiency, and AI adoption.
How does 8allocate help businesses implement data management strategies?
8allocate provides expertise in data engineering, storage architecture, and AI-driven analytics to help businesses design scalable data infrastructures. Our team has experience in building high-concurrency data pipelines, robust data lakehouse architectures, and AI-powered analytics solutions, ensuring secure and efficient data management tailored to business needs.
