Essential Elements You Need to Define Data Strategy

There isn’t such a company that doesn’t make decisions about how it operates with, engages, and leverages its data either at a project or enterprise level. Those businesses that develop a holistic perspective by adopting an enterprise-level data strategy are positioned well to optimize their tech investments and lower their expenses.

This kind of data strategy uses data as an asset to obtaining valuable insights. You can then use these insights to gain a competitive advantage as you integrate them into business operations. 

If you want to smoothly become a data-driven organization — the one that aligns operational decisions with the automatic and systematic interpretation of data — you are required to have a plan for treating data as a business asset. Your first step toward this plan is to develop a data strategy.

This way, you acquire the ability to use advanced analytics at every point of interaction for continuous improvement of decision quality and precision.

With a data strategy, you make sure that all data initiatives follow a similar method and structure you can repeat over and over. Such a uniform structure enables effective communication within the organization to rationalize and define all solution designs for leveraging data in some way.

What is a Data Strategy?

Your data strategy is nothing more than a plan you design to improve all the ways for collecting, storing, managing, sharing, and utilizing data.

Standards. Reuse. Collaborate. These terms are well-understood by companies. The majority of development teams know about system architecture, development methods, requirements collecting, testing, and reusing code. The majority of business teams know the concepts of business requirements, defining business processes, and measuring results. 

However, when it comes to using these concepts for data to improve access, precision, share, and reuse are mostly unknown to many companies. The approach behind creating a data strategy is to ensure that all data is positioned in a way that can be used, shared, and moved easily and effectively. 

Data shouldn’t be a byproduct of your business processing. It should rather be a key asset that allows processing and making decisions. Your data strategy will help with this. It will ensure that you manage and use data like an asset. Moreover, it will give you a common group of objectives across projects so you can make sure your business uses data both efficiently and effectively. 

Your data strategy should establish common practices, methods and processes for managing, manipulating, and sharing data across the company in a repeatable way. 

Yes, lots of businesses have several data management initiatives — master data management, master data, data governance, data integration, data quality, etc. However, these efforts concentrate on point solutions that approach particular organizational or project requirements. With a data strategy, you’ll create a roadmap for activity alignment across each data management system in a way they add on each other for delivering more advantages.

Eight Essential Elements of a Data Strategy

If you want to make sure your data strategy encompasses everything you need for providing enterprise-wide suggestions, you’ll need to include the following essential elements.

1. Semantics — a glossary of definitions for all topics and terms regarding data, its usage, and handling. 

2. Objectives and vision. This is where you explain why data strategy is important. You should add unique IT and company perspectives here, alongside a clear alignment with your business strategic objectives. In addition, it’s key that you define here a data maturity model for evaluating the current situation. You can also use this model to form the data strategy roadmap, a key tool for implementing the strategy.

3. Strategic principles. These are your common methods and standards your business will follow across all your data efforts. They are also your typical business-based principles. However, they directly impact how you enable technical design functionality and principles. You should include these in the reference architecture portion of your strategy.

4. Current documentation. These are your company operations and technical implementation that show how your business’ data operations work currently. You’ll use this as a base for analyzing your business capabilities, health, and maturity regarding data strategy vision. 

5. Governance — which consists of standards, compliance, change management, workflow guide, and organizational structure. 

  • The procedures, compliance, and standards of data are requirements you must meet for regulatory purposes. As well as those you voluntarily want to accept.
  • Change management represents the standards and methods you’ll use to introduce, evaluate, and confirm the change across the data strategy. You’ll conform them into an iterative evolution and communications of changes in data strategy. For example, the crowdsource contribution of ideas, changes, and communication across all levels of your company. It will also define how you identify, document, and handle deviations and exceptions to data strategy standards.
  • Workflow guide represents the methods and procedures you use to define and manage the data and solution life cycle. Include support-control and operational handoffs here.
  • Organizational structure is also a guide. But about HR and interactions within your data-related activities. Include adequate skill set definitions of all resources here. 

6. Data management guide. This will include all processes and standards you use to manage data elements, data groupings, and attributes. Include the following:

  • Topics of your data. These are groupings of data related to the function that operates on the top of the data model level of columns/table or file content.
  • Metadata. It is supplemental info about the data you manage and operate. You’ll typically manage it separately from the data it describes. Even in this case, you source the metadata from the same systems in the same feeds as raw data.
  • Data security, audits, curation, and stewardship. These are processes that make sure you adequately catalog data, secure it with adequate authorization from approved users, and that the data has high-quality.

7. Documentation of references. A good one will account for current or legacy standards and implementations, and enable you to integrate new innovations and standards into a combined model that still supports your company as it expands and changes. Its crucial aspects are:

  • Design principles of architecture. These are the core technical objectives and guides for all data solutions. They make sure you have consistency within the domains your data strategy affects.
  • Function and domain models. Your definitions and listings for base groupings of technical capabilities and their in-depth definitions. Include associated interactions that support complete data life cycle and exploitation/usage here. All the way from discovery and experimentation to production-hardened operations.
  • Patterns of your use of data. Align them with functional/domain mapping. These are groups of solutions that have similar tech and functional requirements. For example, data science, data discovery, and support of operational decisions.
  • Patterns of design. Your high-quality solution templates for typical repeatable architecture models. For instance, data storage in data lakes versus relational databases. Or data access by various user-profiles and data harmonization for several sources.
  • Matrix of tool function/mapping. This is a list of tools that align across functional capability models with primary-fit evaluation and preference.
  • Rationalization of tools. Your guides or justifications on when you should use particular tools. Include explanations and viewpoints of how you should use various tools together with other tools and various patterns of design.

8. Library of starter and sample solutions. This is a group of solutions you predesign. You should base them on proactive assumptions and the harvest of current implementations. Use interesting examples and accelerators for future solutions. Include:

  • Physical designs you have optimized for particular combinations of tools and interactions. See that you can reuse these as typical accelerators.
  • Logical solution models that you can leverage for various environments and tools.
  • Partner solution catalog is a list of prebuild APIs, services, and packages you sourced from external partners and vendors.
  • Intellectual property and prebuilt code are collaterals you can use for accelerators and automating. 

How to Implement, Maintain, and Evolve Your Data Strategy

Your data strategy should account for how your business plans to mature its data capabilities. And allow new analytics- and data-based services and products to mature. 

On the other hand, your data strategy roadmap is your short-term and long-term plan of initiatives to accomplish this. Use this plan to articulate the iterations and phases for every crucial data strategy component above. You should align your data-based initiatives with the roadmap. As well as align the data strategy your company adopts.

It’s key that you make sure you can achieve your first iterations of data strategy implementation before going for higher maturity goals. Usually, it’s not enough to begin with definitions and implementations of data strategy across its elements, if you don’t drive any of them to their overall state of maturity.

Just like with other technical or business processes, your data strategy comes with its own life cycle. Continual maturity, evolution, and scale. You’ll need to regularly revise your principles, definitions, and tools. As well as align them with trends in the market, new tech, and various priorities. The point is that you identify, interpret, and act on these changes quickly and effectively. 

You should define an operating model and milestones for the IT and company to stay in the loop and engage. Every big change — let’s say modernizing a data warehouse — requires both a roadmap and operating model before it starts.


Every company makes decisions about its data. They’re a core part of doing business. However, only those companies that create a holistic perspective through a data strategy can lower costs and gain valuable insights. If you want your data strategy to encompass everything, you need to include all eight essential elements we mentioned. 

And remember, your data strategy also has its life cycle. It will mature, evolve, and scale. That’s why you’ll regularly revise its principles and definitions, and align them with new trends and technologies in the market.

Act quickly. The results will come by themselves.

Don’t know where to start with your data strategy? Feel free to contact us. We can surely help.

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