Creating a data community is a step toward building a data culture, but the two are not synonymous. In practice, a data culture is larger than its constituent data communities in an organization. 

Data communities are structured entities with a process, scheduled meetups, and specific people. Data culture, on the other hand, is everyone’s way of working, whether they are part of a data community or not. Even if they don’t participate actively, people in a data culture see the value of working with data and they can do it effectively themselves.

For me, a data culture means that data becomes part of every decision people make when they have a business decision to think about. They say: Well, what does the data tell us about this? If we look at the numbers, what seems sensible? They also need to bring in experience and intuition, of course, because it's not all about the numbers. Again, a data culture is a way of working, always questioning the business decisions, the budget decisions, based on the data science.

A data community is possible without a data culture, but in my experience it will not thrive over the long term. Otherwise, a year after all the passionate people have done all the work – offered showcases, hung up posters, and educated people – there may be no lasting change around them at all. Ideally the community works in parallel with the culture as it grows.

Another important consideration is that you don't have to work in a database or IT company to make data important to you. A data culture can grow in any sort of company, in any industry, because everyone generates data. At my local zero waste shop, they know exactly what I’ve purchased and they can track that over time. They might use this data to customize specials to me in an email newsletter. The point is you can put data to work anywhere. You can even organize a community around it and develop a culture where it becomes so valued, it’s literally part of everyone’s way of thinking. That’s the ultimate goal.