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.

Attitude toward the necessity to develop a data strategy.

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

The results are very encouraging. Only 2% of respondents stated that their organizations did not need a data strategy. 35% 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.

Challenges with a data strategy.

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.

The content of a 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.

Comparison between DAMA-DMBOK2 and Gartner data strategy 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 the 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


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


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

  • “Enable to manage funds, inform policies and programs, and promote transparency” (Strategy 1)
  • “For mission value and insight at speed and scale” (Strategy 2)
  • “Realizing the full potential of data to improve educational outcomes” (Strategy 3)
  • “Support the agency´s mission; data-centric approach” (Strategy 4)
  • “Chart the course for future mission successes” (Strategy 5)

Topic 2: Critical business needs or drivers for establishing data management


The organization should identify the critical business reasons why it needs data management. Business drivers link the organization’s business strategy and a data (management) strategy. Defining key business drivers helps balance critical business needs and the organization’s resources.


Below is the summary of key business drivers that led the agencies to develop data strategies:

  • Making better-informed decisions (Strategies 1,2 and 4)
  • Strategic competition (Strategy 2)
  • Needs in digital transformations (Strategies 2 and 5)
  • Changes in the external environment (Strategy 3)

So, as we can see, improving decision-making and adapting to changes in the external environment are the key drivers.

Topic 3: The value proposition of data management for different stakeholder groups


We often speak about the value proposition of data management for an organization. I think that this approach is not entirely correct. An organization consists of multiple stakeholders. Different stakeholders may get various benefits from implementing data management. Sometimes, expected benefits can contradict each other. So, it is essential to link the identified business drivers with the corresponding groups of stakeholders and then assess the data management value proposition per stakeholder group.

The first section of the strategy should clearly articulate the key business areas and drivers for which data management can deliver the most significant value.


The public stakeholder groups of the agencies differ; therefore, the value propositions vary per strategy as well. Let me demonstrate the key value propositions indicated in the strategies one by one:

  • Strategy 1: “Better supporting American farmers, producers, and ranchers,” “Maximize the impact of citizen-facing programs,” “Solve national problems and spark innovations”
  • Strategy 2: “Enable secure discovery, access, and use of IC data for mission value and insight at speed and scale”
  • Strategy 3: “Improve education outcomes and leading the nation in a new era of evidence-based policy insights and data-driven operations”
  • Strategy 4: “Delivering public value that enables our communities to thrive COVID-19 Recovery, Public Safety and Criminal Justice, Housing and Homelessness, Workforce Development and Economic Opportunity, Health, Education, Environment and Natural Resources, Good Government”
  • Strategy 5: “Unleash the full power of data to accelerate NASA’s ability to execute its missions and expand knowledge of the universe”

Part 2 of this article  will consider two other sections of a data (management) strategy