Data Mesh Radio #306 Building with People for People - Swisscom's Data Mesh Approach and Learnings - Interview w/ Mirela Navodaru
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Mirela's LinkedIn: https://www.linkedin.com/in/mirelanavodaru/
In this episode, Scott interviewed Mirela Navodaru, Enterprise and Solution Architect for Data, Analytics, and AI at Swisscom.
Some key takeaways/thoughts from Mirela's point of view:
- Specifically at Swisscom, it's not about doing data mesh. They want to make data a key part of all their major decisions - operational and strategic - and data mesh means they can put the data production and consumption in far more people's hands. Data mesh is a way to achieve their data goals, not the goal.
- When you are trying to get people bought in to something like data mesh, you always have to consider what is in it for them. Yes, the overall organization benefiting is great but it’s not the best selling point 😅 try to develop your approach to truly benefit everyone.
- Data literacy is crucial to getting the most value from data mesh. Data mesh is not about throwing away the important knowledge your data people have but it's about unlocking the value of the knowledge your business people have to be shared with the rest of the organization effectively, reliably, and scalably.
- ?Controversial? You really have to talk to a lot of people early in your data mesh journey to discover the broader benefits to the organization. That way you can talk to people's specific challenges to get them bought in. When designing your journey, it is important to get input from a large number of people.
- When talking data as a product versus data products, the first is the core concept and the second is the deliverables. Scott note: this is a really simple but powerful delineation
- "No value, no party." If there isn't a value proposition, there shouldn't be any action. You need to stay focused on value because there are so many potential places to focus in a data mesh implementation.
- You have to balance value at the use case level to the domain versus more global value to the organization. At the end of the day, everything you do should add value to the organization but sometimes use cases are much more focused at the domain and that's perfectly expected and acceptable.
- Data mesh, to really change the organization in the right way, needs top level buy-in. You can't only be the data team trying to head down the data mesh path.
- Everything in data mesh is about iterating to better. You need the space and room to learn as you go along. You can - and must - deliver value before you've got everything figured out perfectly.
- Relatedly, you will learn how to better iterate towards value throughout your journey. It will be tough at the start as with any learning journey.
- Obviously, data mesh is a large cultural change. You need to have empathy and give people the chance to grow instead of trying to move too fast. Upskilling, especially around data literacy, is crucial.
- There are two very valuable aspects of data mesh: the value you deliver via use cases along the way and the value you get from learning to do data better across your organization. The first is from integrating data into far more of your decisions and the second means you can react more quickly to new opportunities and build scalable and reliable approaches to data management.
- Something like data mesh is a big change. But it shouldn't be a shock to people. You can do it gradually and incrementally while you deliver value. One of the best ways to lose people is to thrust disruptive change on them instead of working with them through the change to prevent large-scale negative disruptions.
- There are so many areas where data mesh helps organizations, whether it is getting away from silos, reducing redundancy, improving quality and reliability, etc. It's not just about doing data management itself better, which has been the focus of most data approaches historically.
- Again, data work is not the point. The point is to make your colleagues better at their job through being more informed. That comes down to the data but it's never the actual point, it's the vehicle to delivering value.
- Transparency and managing expectations - and communication in general - are crucial to doing data mesh well. You need to have that space to learn and iterate. Let people know what you are doing and especially why you are doing it.
- Data modeling in data mesh is of course a challenge. But it's important to have some level of common language between the domains or you will have data silos. It's a balance but it's crucial to give domains flexibility but also create easy paths for people to combine data across domains.
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