Data strategy frameworks that you should be familiar with

Implement data strategy successfully with framework structures

Ever since the advent of big data and data science, everyone has been talking about data strategy. This is why we have conducted some research on this topic for you and examined the various possibilities for implementing a data strategy at the enterprise level. In the following, we will briefly introduce you to the two frameworks that we believe to be the most interesting:

Framework 1: Data strategy and the Enterprise Data Executive

The first framework we want to talk about is discussed by Todd Harbour and Peter Aiken in their book “Data Strategy and the Enterprise Data Executive” (Publisher: Technics Publications, June 2017). It is divided into two parts and focuses on removing limitations with an interactive approach.

Figure 1: Data Strategy[1]

In the adjacent figure, we see a brief summary of the framework. It starts by preparing the organization for change, hiring qualified data executives and data talents, and eliminating what the authors call the Seven Deadly Sins, which are:

  • Failing to understand data-centric thinking
  • Lacking qualified data leadership
  • Failing to implement a programmatic way to share data
  • Failing to coordinate the data program with its projects
  • Failing to adequately manage expectations
  • Failing to sequence data strategy implementation
  • Failing to address change management challenges 

The second part of the framework focuses on identifying the primary constraints in the enterprise keeping the data from fully supporting the overall strategy, and removing these constraints in an iterative manner.

Framework 2: Modern enterprise data strategy

The framework created by Scott Kurth, Edd Wilder-James, and John Akred is explained in detail in the online training course by Safari Books entitled “Developing a Modern Enterprise Data Strategy.” This framework also has an iterative approach. The difference is that it focuses on creating a roadmap that can be implemented and prioritizing workloads that impact the majority of use cases. 

The framework consists of seven steps:

  1. Identify strategic imperatives
    This not only applies to data-centric aspects of the company, but also to the overall strategy, which consists of the pillars “Global Coverage,” “Single Platform,” “Convergence,” “Value Chain Extension,” and “Operational Excellence.”

  2. Define business objectives
    The second step is about defining the overall business objectives for the different departments. Although these objectives may vary across different domains (sales units have different objectives than technical teams), they should always be aligned with the company’s overall strategy imperatives and it should be possible to trace back each objective to at least one of the pillars.

  3. Define data requirements
    The general business objectives are then used to assess the different data requirements and the different ways that the data can be used within the enterprise. 

  4. Identify gaps in current systems and technology
    The previous definition then makes it possible to assess the different gaps in current systems and technologies, and we see where the data will be used and how we will meet the requirements.

  5. Map business objectives to use cases
    Figure 2 summarizes the Data Strategy Framework described in this section. As we can see, the next step is to map the business objectives to specific use cases. In this context, only those cases are selected that relate to data, i.e. in which data is either used in a decision or generated, shared, and exposed to other systems.

  6. Rationalize use cases into workloads
    With a comprehensive list of data-related use cases, we rationalize these use cases into strategic workloads. These workloads offer the ability to reuse between different use cases, i.e. even if something is built once, it can be used multiple times.

  7. Action plan and roadmap
    The final step is taking all of these pieces and bringing them together into a roadmap that allows you to take action and execute the strategy once you have gone through the process of building it.

Figure 2 – Data Strategy Framework Overview[2]

[1] Todd Harbour Peter Aiken - Data Strategy and the Enterprise Data Executive (Publisher: Technics Publications, June 2017).

[2] Scott Kurth Edd Wilder-James  and John Akred. Developing a Modern Enterprise Data Strategy, online training offered by SafariBooks available at