Data Mesh in Practice

Chapter 6. Industry Practices

You have seen throughout this report that many other data practitioners have been in a similar position as you are right now. We conclude this report by discussing common pitfalls and suggesting best practices that different companies have come up with.

Common Pitfalls

In this section, we will introduce you to a set of traps that are noteworthy to avoid, as they can significantly harm your data mesh journey.

Overloading Your People

When talking to different companies that are just getting started, we’re often asked if additional resources are required to take the first steps, or if one can start with the teams and people that are already there. Especially for product managers, the answer is often the latter, since when starting to work with data products, a lot of methodology is indeed very similar to working with other technical products like microservices and applications. However, one important consideration frequently overlooked in these situations is whether the people in question have the capacity to take on such additional responsibilities. You have to keep in mind that in most cases we are considering pilot projects that can have a significant impact on the data-related future of our organization. Don’t make the mistake of pushing such important responsibility on teams and people that are already on low capacity. Even if you already have the required skill sets in your company, involving them in such a project still means they will need ...