A Data Strategy: Theory vs. Practice. Part 2

This is Part 2 of the article “A Data Strategy: Theory vs. Practice.” In Part 2, we will continue the analysis of data strategy examples and discuss the following:

• A data (management) strategy content: recommended vs. presented in strategies mentioned above (sections 2 and 3)
• Recommendations for developing a (meta)data (management) strategy

A data (management) strategy content: recommended vs. presented in the real strategies (Continuation)

“What” section

This section forces a company to make a serious decision that will impact the success of the data management initiative. This decision is the balance between “we want” and “we can.” In other words, it goes about the feasibility of the strategy. When you start writing a data (management) strategy, you should be honest with yourself and your company about the goal of writing the strategy. Do you write it pro forma for demonstration purposes because “all others do it,” or do you really need it with the key goal of its implementation? You can stop reading this article if your goal is the first one. If your goal is strategy implementation, I encourage you to dive into the topic of data management principles, framework, and core data management capabilities.

To define the feasible data management strategy, you should make decisions about the following topics:

Topic 4: Data management principles

Theory

A data management principle is a rule that regulates the way data management is implemented.

Different leading industry guidelines have pretty different approaches to defining data management principles. The DAMA-DMBOK approach in the second edition deviates from the approach in the first. In the first edition, the data management principles were more generic. The second edition defines principles per Knowledge Area. I like the approach taken by The Open Group in the TOGAF® Standard. In my practice, I apply the method that connects business drivers with data management principles and analyzes the consequences of applying principles.

Figure 5 demonstrates this approach.

Figure 5: The approach to formulating data management principles.

Data management principles must be formulated with a key focus on the feasibility of their implementation. The analysis of consequences must include potential benefits and challenges and required actions.

Practice

I found the formulated principles in four of the five referenced strategies.

Let us review these principles that I grouped into several categories related to:

Data governance:

• “Data are an asset” (Strategy 4)
• “Data must have clearly defined accountabilities” (Strategy 4)
• “Data must follow rules and regulations” (Strategy 4)
• “Data should be managed consistently” (Strategy 4)

Ethics:

• Principles of professional ethics (Strategy 2)
• Ethical use (Strategy 4)
• Ethical governance (Strategies 3 and 5), including:
• “Uphold Ethics” (Strategy 5)
• “Exercise Responsibility” (Strategy 5)
• “Promote Transparency” (Strategy 5)

Decision-making

• Conscious decisions (Strategies 3 and 5), including:
• “Ensure Relevance” (Strategy 5)
• “Harness Existing Data” (Strategy 5)
• “Anticipate Future Uses” (Strategy 5)
• “Demonstrate Responsiveness” (Strategy 5)

Culture

• Data-informed culture (Strategy 4)
• Learning culture (Strategies 3 and 5), including
• “Invest in Learning” (Strategy 5)
• “Develop Data Leaders” (Strategy 5)
• “Practice Accountability” (Strategy 5)

Data management-related

• Data-centric principles for IT architecture (Strategy 2)
• Governance and effective management (Strategy 4)

As we can see, these strategies have a lot of similar principles.

Topic 5: The data management framework

Theory

I wrote multiple articles on the differences between viewpoints on data management structures of leading data management guides, DAMA-DMBOK2 and DCAM. Years ago, this unalignment brought me to the idea of developing the “O.R.A.N.G.E.” data management framework to solve the issues I found in the leading guidelines. This framework is a set of methods and models to establish an operational data management function.

One of this framework’s foundational models is the data management capability model, presented in Figure 6. The data management capability consists of several core sub-capabilities, each of which plays a different role in delivering business value from data management.

Figure 6: The “O.R.A.N.G.E.” model of a data management capability.

Data lifecycle management is the core capability that delivers business value for an organization’s stakeholders. Business architecture and data governance are strategic capabilities that define the direction of data management development. Data governance is a special capability. The title “data governance” incorrectly reflects this capability’s real role. This capability governs data management, not data. Its core task is to establish data management as a business function applying a data management framework and then control data management function operational efficiency and effectiveness. Data governance does it by controlling the establishment of the data management organizational structure, processes, policies, and tools and ensuring resources for all data management sub-capabilities.

Practice

Several strategies (1, 3, 4) refer to themselves as a framework. It looks like the organizations did not use any industry framework and tended to develop their frameworks to meet their goals.

Topic 6: The scope of the data management capability

Theory

The “O.R.A.N.G.E.” data management model demonstrates the core data management sub-capabilities. The most important thing is that all data management sub-capabilities are interrelated. Organizations that started implementing data quality as their first initiative have a high probability of failing. In order to properly manage data quality, an organization must have data governance and data and application architectures, including data modeling and metadata management, including data lineage, etc. This is something that many data management professionals do not realize. This situation is partly due to the DAMA-DMBOK2 approach. This guideline creates the impression that all Knowledge Areas can be implemented independently. However, I have to give it my due that on page 38, the authors stated the following: “None of the pieces of existing DAMA data management framework describe the relationship between the Different Knowledge Areas.”

The rule of scoping the data management capabilities is simple: the business drivers identified in the strategy section “WHY?” will define the set of required sub-capabilities. The level of development of these sub-capabilities will depend on an organization’s resources.

Practice

Read further: https://datacrossroads.nl/2024/03/18/a-data-strategy-theory-vs-practice-part-2/