A Data Strategy - Theory vs. Practice: Part 1

The idea to write this article was triggered by the remark of one of the participants of my strategy workshop last summer. He mentioned that it would be nice to include the consideration of real data strategies in the workshop. I thought it would be “the mission impossible.” Many companies started dealing with data as their strategic assets. So, a data strategy and data management practices could be considered a competitive advantage and kept confidential.

So, when preparing a new workshop on integrating data and metadata strategies, I recalled this remark and started searching for examples of data strategies. As expected, I could not find any reference to a data strategy from commercial businesses. However, I was very positively surprised when I found more than ten examples of data strategies published by governmental agencies worldwide. In this article, I will reference the following publicly available strategies:

Strategy 1: United States Department of Agriculture, Fiscal Year 2020-2024   Data Strategy

Strategy 2: United States Intelligence Community, The IC Data Strategy 2023-2025

Strategy 3: US Department of Education, Data Strategy, 2023

Strategy 4: The State of Oregon, Data Strategy, 2021-2023

Strategy 5: NASA Data Strategy, 2021

This article will discuss the following topics:

• Organizations´ attitudes toward developing data strategies and associated challenges
• A data management structure recommended by the industry authorities
• A data (management) strategy content: recommended vs. presented in strategies mentioned above
• Recommendations for developing a (meta)data (management) strategy

Due to the large text volume, I split this article into two parts.

 PART 1

This is Part 1 of the Article “A Data Strategy: Theory vs. Practice.” In Part 1,  we discuss the following:

• Organizations´ attitudes toward developing data strategies and associated challenges
• A data management structure recommended by the industry authorities
• A data (management) strategy content: recommended vs. presented in strategies mentioned above

Organizations´ attitudes toward developing data strategies and associated challenges

The statistics I use in this article are based on LinkedIn polls I regularly perform. I cannot guarantee that these polls provide representative samples, but in any case, the results demonstrate specific trends.

Figure 1 demonstrates the attitude toward the necessity to develop a data (management) strategy.

Figure 1: Attitude toward the necessity to develop a data strategy.

The results are very encouraging. Only 2 percent of respondents stated that their organizations did not need a data strategy. 35 percent of respondents already have it, and the rest are in the process.

Figure 2 presents the results of another poll I performed in 2023 and 2024. The results demonstrate that those companies that develop strategies experience challenges.

Figure 2: Challenges with a data strategy.

As you can see, the number of respondents who do not realize the necessity of a data strategy correlates with the previous poll’s results. Those who proceed with data strategy have challenges defining the content and implementing the strategy. Figure 2 demonstrates the trend: the number of companies that experience both types of challenges has grown in 2024 compared to 2023.

A data (management) strategy structure recommended by the industry authorities

First, let us agree on the definition of a strategy.

According to DAMA-DMBOK2, “A strategy is a set of choices and decisions that together chart a high-level course of action to achieve high-level goals.” To decide on the content of a strategy, an organization must determine if it goes about a “data” strategy or a “data management” strategy. In my opinion, these two strategies have different focus and content.

The “data” strategy focuses on defining the role of data for an organization. It demonstrates the way the company will treat and use its data.

The “data management” strategy concentrates on how to handle data to obtain value from it. The data management strategy elaborates on the development of a data management framework.

In my practice, I use the content of the data (management) strategy developed by DAMA-DMBOK2. I grouped the proposed content into three categories; a strategy should answer the following categories of questions: Why? What? How? Figure 3 illustrates this structure of the data (management) strategy.

Figure 3: The content of a data (management) strategy.

Recently, I came across the “Data and Analytics Strategy and Operating Model” by Gartner. Interestingly, the content of Gartner’s models has much in common with the DAMA-DMBOK2 model.

Figure 4 demonstrates the comparison of these two models.

Figure 4: Comparison between DAMA-DMBOK2 and Gartner data strategy models.

You can see that the primary content is similar between these two models. Later in this article, I will compare the recommended structure and content with those used in real strategies.

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

I will use the DAMA-DMBOK2 structure to briefly explain a data (management) structure and compare this theoretical structure with the actual strategy examples I referenced above.

I want to draw your attention to the fact that all five strategies are data strategies, not data management. As discussed above, this fact may lead to the conclusion that organizations focus more on the role of data than how to handle it. So, let us see.

“Why?” section

An organization first must answer the question “WHY” it needs data management, a data (management) strategy, and function. It can answer this question by describing the following topics:

Topic 1: Defining an organization’s vision on the role of data and/or data management

Theory

We get used to the idea that data is a company’s asset or resource. However, the role of data in an organization depends on its business model. For some organizations, data is a saleable product. For others, it is simply a resource for achieving the organization’s goals.

Practice

Data is considered a strategic and operational asset in all five strategies to support the following:

READ FURTHER: https://datacrossroads.nl/2024/03/04/data-strategy-theory-practice-part1/