5 Essentials to Build a Data Strategy

The success of an AI-based technology revolution all begins with a solid data strategy

A successful data strategy ensures that the organisation can use, share and move data resources easily and efficiently where data is managed and used effectively and efficiently to deploy and ensure analytical models continue to perform successfully over time.

There are five core components of a data strategy:


Identify data and understand its meaning, regardless of structure, origin or location. One of the most basic constructs for using and sharing data within a company is establishing a means to identify and represent the content. Whether it’s structured or unstructured content, manipulating and processing data isn’t feasible unless the data value has a name, a defined format and value representation.


Store data in a structure and location that supports easy, shared access and processing. As organisations evolve and data assets grow, many have problems with the size and distributed nature of their data landscape. The goal is to store the data once and provide a way for people to find and access it. A good data strategy will ensure that all data is available for future access without requiring everyone to create their own copies.


Package data for reuse and distribution while providing guidelines for access. Traditionally data has been organised and stored for the convenience of the application, and the problem is that most application systems are not designed to share data. The logic and rules required to decode data are rarely documented or even known outside of the application development team.

Today, IT manages dozens of systems that rely on data from multiple sources to support individual business processes. Application systems and long IT processes can no longer hold data hostage. Packaging, sharing and democratising the data is a critical shift to support the organisation’s success.


Move and combine data residing in disparate systems to provide a unified, consistent data view. Data generated from applications is a treasure trove of knowledge, but you still have to prepare, transform or correct it before it’s fit for analytics and business use. Data users need self-service tools to process data and easily deploy models while adhering to data governance and standards – without IT involvement.


Establish, manage and communicate information policies and mechanisms for effective data use. A governance process ensures all data constituents understand and respect the rules for shared data use. This means organisations can consistently manage data without limiting or interfering with its use. Good data governance promotes easier access, use and sharing. It also establishes trust in the data you use for the analytical and decisioning process.

Companies that include these five components in their data strategy, will have the foundation for delivering the best data for analytics and decisioning excellence. 

About the Author

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Dr. Iain Brown, Head of Data Science at SAS UK & Ireland. SAS is the leader in analytics. Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence.

Featured image: ©Peshkova

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