Data Strategy practices

But first, let's address the elephant in the room. What is data strategy?

According to Gartner, "A data strategy is a highly dynamic process employed to support the acquisition, organisation, analysis, and delivery of data in support of business objectives." (Gartner, 2024). In other words, a data strategy is more than just a plan or a framework. It’s a blueprint for a roadmap that guides a business toward efficient and effective data usage, which supports its vision and helps achieve key objectives. Effective data strategies focus on identifying data challenges and their solutions. The strategy transforms data from being a burden into a valuable asset that helps your organisation achieve SMART goals. A successful data strategy highlights the approach to managing your data and its monetisation while leveraging it through data management best practices.

What Does a Data Strategist Do? 

The scope of a data strategist role is broad and often confused with data analysts, scientists, or engineers. While these roles share overlapping technical skills and contribute to an organisation’s data capabilities, the Data Strategist focuses on creatively maximising data value through planning, business and communication expertise. Their responsibilities can vary by domain, e.g., a marketing data strategist differs from one in tech or AI. Additionally, a data strategist can work across domains as a generalist, depending on their knowledge of tech, the industry and tools. For example, I am a general data strategist with broad experience across various tools and technologies. While all aim to leverage data for business goals, their objectives and skills vary by role, allowing for adaptation to business needs.

In a nutshell, a data strategist is the data vision's architect. They work with business leaders, technical experts, etc., to make sure everyone is aligned and understands the business needs and what they're trying to accomplish. They then sketch out the big picture and piece it together by breaking it down into actionable steps, highlighting resources needed and addressing limitations for each step. They act as vision translators, facilitating understanding between the technical and business sides, while constantly managing the implementation, ensuring your data systems are built to meet real use cases and business needs to deliver measurable results.