What is a data strategy to you and why is it important?
“For me, a data strategy must be directly tied to the company’s business goals. It needs to provide a clear direction for how data will help the organization reach those goals, and it should define the use cases that deliver measurable value.
After defining the vision, goals and use cases I like to break it down into four essential components - technical capabilities, data governance, organization and data-driven culture.”
1. Technical capabilities
“This is the platform and tooling required to support the use cases. It doesn’t need to be overly detailed, but it should give a clear sense of the direction we’re moving in. A simple architectural overview is often enough.”
2. Data governance
“This is the foundation for using data responsibly and making sure it’s reliable. Without trust in the data, it just sits on a shelf. The key is finding the right balance, continuing to build valuable use cases while establishing governance that ensures long-term quality and maintainability.”
3. Organization
“This covers the skills and structure needed to deliver the vision, both today and in the future. Different organizations require different setups, but one principle I keep on coming back to is that the data function needs to be close enough to IT to work efficiently as a whole, yet not so embedded that it loses sight of business objectives.”
4. Data-driven culture
“This is the shift toward an organization that makes decisions backed by data. It’s often the toughest to achieve and requires clear leadership support. I usually start by mapping current roles against the competencies and tools we expect in a data-driven environment. It helps us identify where upskilling is needed and where additional support is required.”
What is the most critical factor when creating and implementing a data strategy?
“The most critical factor is having strong sponsorship from senior business leadership. Without that backing, the strategy won’t gain traction, no matter how well-crafted it is.”
What makes data strategy so hard - and how do you break through those challenges?
“The tricky part is getting the level of detail right. It can’t be so high-level that it becomes abstract, but it also shouldn’t be so detailed that it restricts discovery or becomes outdated quickly.
And of course, it needs to be concise enough to be practical.
Once sponsorship is secured, it’s all about communicating the strategy consistently until it becomes part of how the organization operates.”
