In this episode, Scott interviews Angelo Martelli, Group Leader of Data Services at logistics company Vanderlande.
Angelo laid out his framework for driving data mesh buy-in internally at Vanderlande which helped them take the idea from a small group to a company-wide initiative:
Start with proving there is a problem that you are trying to solve - if everything is functioning well, why focus your efforts on that area instead of trying to fix another? Your proof should be as fact-based as possible, e.g. how long does it take to make a change to your data warehouse. Focus on proving that your incremental investments are driving sub-linear returns. Other areas to look to prove problems: how many people are involved in a change to your data warehouse, percent of time spent on regression testing versus development, mean time to resolution of challenges, etc.
Once you have some proof, you need to work towards understanding the problem you are trying to solve. It's not "deploying a data mesh", it's scaling the organization to be agile relative to data and be able to make more (and better) data-informed decisions.
Next, you need to understand your organization. Who are the right people that can help you? How does your organization work relative to culture and process? Which domains are struggling and how? Tie the implementation goals to the actual business challenges.
Then, you need to demystify data mesh, make it easy to understand for people not well versed in data - what are we actually trying to accomplish and why?
Last, make it concrete / prove it out. Make a few data products, make a simple platform for folks to use.
Angelo then recommends that once you have momentum, sharing a very clear vision is crucial. Not just sharing in a document but actually having conversations to really make sure the context and vision is understood. Data mesh is about collaboration, you must work together so it is imperative to make expectations very clear.
Similar to Abhi Sivasailam, Angelo also stressed the importance of the domain data model and abstracting that away from the application model(s). The business model is what matters for data.
All of that and so much more. Also, Angelo gives a shout out to the usefulness of the Data Mesh Learning community. 😎