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:
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
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:
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.
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:
Figure 2 – Data Strategy Framework Overview
 Scott Kurth , Edd Wilder-James and John Akred. Developing a Modern Enterprise Data Strategy, online training offered by SafariBooks available at https://learning.oreilly.com/library/view/learning-path-developing/9781491985472/