The data culture team at delaware has identified four foundations for a healthy data culture:
Does everyone in my organisation – from operators to finance controllers and C-level managers – have the mindset, skills and knowledge to fulfil their unique data needs?
How is data organised within the organisation? Is it centralised or decentralised? Who is watching over data accuracy and quality? How are the other roles and responsibilities concerning data divided? Bart: “Without ownership, it’s hard to extract value from data. For example, even if there’s a quality checklist, someone needs to have the mandate to stop production when defective items are produced. Also: make sure everyone has access to relevant data and safeguarding data quality.“
Does everyone know what data are available in the organisation, and where they can find it? Are we aligned on data taxonomy, i.e., do we agree on what constitutes a ‘customer’, ‘prospect’, ‘sale’, etc.? And what KPIs do we have? Bart: “This is crucial for measuring success. If you make the change from one tool to another mandatory, your user adoption rate doesn’t mean a lot. People might be using it, but are they using it right? And do they like using it?”
Is our approach to data widely-supported within the organisation? Do we have solid first-line support in place?
The latter also fits in with Bart’s ideal image of a healthy data culture: “You want to end up in a situation where data questions are coming from within your business community, instead of being imposed by your IT or data team. For example, some field service technician is noticing a certain pattern emerge, has an idea for a potential use case, and asks IT to point them to the right data. I always like to use the sailing metaphor, where the boat is the data community and IT is the wind in its sails.”