Becoming a data-driven enterprise: Data strategy (Part 1 of 3)

This is the first article of three where I discuss what a data strategy is, where companies have gone wrong building data platforms, and the solution to becoming a data-driven company.

Why do companies develop a data strategy?
Large enterprises often have a stated policy that they will become data-driven. In principle, it means that all decisions should be based on data. The data can be their own or bought from a third party.

The motivation for using data is to improve the user experience, develop new products and services, and become more productive. Improving operational efficiency and gaining a competitive advantage are key goals and is equally applicable to small and large businesses.

Developing a clear data strategy can clarify the purpose of your data. Having a business focus supports efficiency and guides activities that facilitates your organizations stated goals. The end goal is to enable the organization to compete by driving innovation and growth of the business.

What is data strategy in 2023?
A data strategy is designed to support the goals and objectives of the business. The content of a data strategy should include a view or perspective on how the data will be used by the business. Finally, an outline or specification of what data will be collected, analyzed, and used is detailed in the corporate data strategy.

Companies often include the choice of applications and tools in the data strategy. This is a mistake I will discuss in the next article in the series when the implementation of data platforms is the issue. Rules for confidentiality, integrity and availability (CIA) is a well-known concept that you will see in strategy documentation for both data and also cybersecurity.

Becoming a data-driven company can be difficult. Strategic decision-making based on data analysis requires a mature strategy that creates processes for collecting, analyzing, and turning data into action. There are few rulebooks, and your best bet is to understand your own business and how you make profits.

What you need to know
The first thing you need to do understand the business problem you are solving. Years ago, I worked on a clearinghouse payment function in the telecommunications industry. The task was to automate settlements for international and other calls that were rated.

The success of the project boiled down to which system transactions we could bill for that would result in accurate charges. It was an effort that took months to identify, develop and evaluate. In the end, I completed the project and sold the new product to the telco operators that were my customers.

What we learned from the project is that you need to bring in the domain experts from the business to get diverse opinions. Once you have built the data products for a particular domain you can establish the single source of truth everyone in the business can share. 

From a leadership perspective, continuous tracking, testing and evaluating data-driven decisions is what’s going to keep your strategy on track. But understand that is a means to an end, you need to have the vision of where you are going — the vision and the mission.

What’s next
Next week I will discuss common approaches to data lakes and why they fail in delivering on the promise of the data-driven enterprise. In two weeks, the topic is how to approach the data-driven enterprise vision from a practical engineering perspective.


Data product: A data product, in general terms, is any tool or application that processes data and generates results. Businesses can use the results of such data analysis to obtain useful information like churn prediction and customer segmentation, and use these results to make smarter decisions.

CIA: The three letters in “CIA triad” stand for Confidentiality, Integrity, and Availability. The CIA triad is a common model that forms the basis for the development of security systems. They are used for finding vulnerabilities and methods for creating solutions.

Single source of truth: A single source of truth (SSOT) is the practice of aggregating the data from many systems within an organization to a single location. A SSOT is not a system, tool, or strategy, but rather a state of being for a company’s data in that it can all be found via a single reference point.