Building a Data Strategy

A data strategy articulates an organization’s plan for reaching high data maturity. Data maturity is the measurement of an organization’s expertise in applying data capabilities to achieve business value. It first requires establishing a data maturity baseline and identifying a target data maturity level. Progress needs to be measured frequently with course correction as needed. Continuous and iterative progress are the objectives of a data strategy. Achieving success with a data strategy requires a focus on data management, Data Governance, Data Privacy, Data Protection, Data Security, and other critical aspects for an organization to succeed in its digital transformation journey. There needs to be careful thought on how to execute and implement a data strategy.

In this chapter, we’ll guide you in creating a data strategy that is applicable across industries. It offers tips to create, execute, and implement a data strategy. It also...

Why create a data strategy?

There are many reasons why an organization needs a data strategy. The most obvious one is to have a plan on how to become data-driven in order to generate revenue and achieve cost savings. Enterprises have learned from the pitfalls of not managing data as an asset and data product. They have lost customer opportunities and revenue from business decisions made with untrustworthy data or experienced high costs when developing customer goods. In Chapter 2, Show Me the Business Value, we discussed examples such as costs associated with regulatory fines due to lack of Data Privacy enforcement or lost revenue from bad metadata and Data Quality. These factors lead us to the need for a well-thought-out and structured data strategy that manages data effectively and is aligned with the enterprise’s business goals.

Not having a data strategy document can lead to the following negative outcomes:

• An immature level of organizational readiness
• Incorrect...

Creating a data strategy

A data strategy document’s focus is driven by an organization’s business goals, current as-is data maturity level, aspired data maturity, and associated capabilities. As you have seen in this book, the common theme is always Data Governance to achieve data integrity, which is a required topic in a data strategy document. The other is overall data management. Data management can be broken up into different compartments such as Data Integration, data operations, data architecture, and so on. Regardless of which focus areas are added, what’s critical is to ensure it always ties back to the business. It needs to address how to achieve the desired level of data maturity and how the associated capabilities will accelerate an enterprise’s business objectives and vision. One common pitfall that I have seen over and over when working with brilliant technical practitioners is the inability to tie technology back to business value. It’...

Data strategy implementation

At this stage, a data strategy document has been created. The stakeholders are all on board and the sponsor(s) is rallying the implementation of the data strategy. The next step is agreeing on how to implement a data strategy. You can leverage the data maturity assessment results and target data maturity levels as a guide. These two parameters can be used to define new improvement initiatives and implementation approaches.

As an example, if the target state is to achieve a high level of data maturity in Data Quality and Data Integration to address company-wide data breaches, then these focus areas need to be aligned with potential solutions from people, process, and technical standpoints. Some of the questions to consider when selecting a solution are as follows:

• Can data practitioners collaborate better? Are the interactions needed between technical roles and the wider business addressed?
• What are the necessary process changes to drive faster...

Summary

A data strategy captures an organization’s starting point, journey, goals, solutions, and target destination in order to become a data-driven enterprise. Organizations first need to recognize and understand how and why to manage data as an asset and a Data Product.

In this chapter, we discussed the value of a data strategy document enabling organizations to have a vision and plan to achieve profitable revenue and cost savings by leveraging data. We provided guidance on creating a data strategy and emphasized that it must have a business point of view across people, processes, and technology. We highlighted three popular data maturity frameworks (DMBOK, DCAM, and Gartner’s) and recommended completing a data maturity assessment as input for a data strategy. We also covered the implementation of a data strategy document and offered an example that aligned DAMA’s data maturity areas with a Data Fabric. Data management, Data Governance, Data Privacy, and...