#269 Panel: Leading a Data Mesh Implementation (2nd Iteration) - Led by Vanessa Eriksson w/ Stefan Zima, Duncan Cooper, and Sid Shah

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Vanessa's LinkedIn: https://www.linkedin.com/in/vanessaeriksson/

Sid's LinkedIn: https://www.linkedin.com/in/siddharthin/

Stefan's LinkedIn: https://www.linkedin.com/in/stefan-zima-650229b7/

Duncan's LinkedIn: https://www.linkedin.com/in/duncan-cooper-1113722/

In this episode, guest host Vanessa Eriksson, the first CDO in Sweden and the head of data advisory company Vanessa Eriksson AB facilitated a discussion with Duncan Cooper, Chief Data Officer for Northern Trust Asset Servicing, Sid Shah, Head of Data Monetization and Platform at Airtel (guest of episode #258), and Stefan Zima, Data Transformation Lead at Raiffeisen Bank International AG (guest of episode #270). As per usual, all guests were only reflecting their own views.

The topic for this panel was about the leader's role in a data mesh implementation and what these four panelists have learned in that role. This was the second iteration of a panel we will likely have about every six months or so - the first was episode #215 from April of 2023.

Scott note: I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views individually.

Scott's Top Takeaways:

1. Regarding data mesh: get going but don't rush. Essentially, get started now but don't be in a hurry to try to get to some picture perfect end state. You need to take your time to make sure you are transforming instead of making changes that will unravel. Be brave and move forward into some uncertainty!
2. Relatedly, you will absolutely get many things "wrong" but wrong in a data mesh world can simply mean not right yet. We have ways to adapt/adjust and evolve as we learn and grow. Data mesh provides you the ability to iterate towards better constantly.
3. You _really_ should look to take inspiration and learnings from other data mesh implementations but also understand that your journey will look _considerably_ different, especially your starting point and initial focus areas. If you aren't prepared to adjust for your own situation, you aren't ready to do data mesh. Sid especially has focused on learning from other implementers and leveraging them to create credibility internally.
4. Stop treating data problems as technical problems. At least start to treat them as business problems first but really, they are sociotechnical problems - social/business challenges married to technical challenges wrapped in trying to share understanding. It's people/process/technology and people most importantly.
5. Relatedly, as Stefan said, data mesh is much more about mindset shifts than it is about technology shifts. You need to show people new ways of working and guide them to value. If you don't, you very likely won't change how people work with data and won't deliver the promised value of data mesh. And then you will lose your sponsorship.
6. While data mesh can be a powerful paradigm, one of the best things about it is giving us common framing and ways of speaking about what you are trying to do and accomplish. Scott note: this is partly why I created the data mesh community, this podcast, and my company - to make it easier to share information with each other about how to do data mesh, both with other implementers and within organizations.
7. There will be pushback from many parties to many aspects of data mesh. Be prepared for that and help people understand the reasoning behind the changes - what do these changes enable you to do? E.g. you aren't pushing data ownership onto the domains simply because as a central data team you're lazy - it's that they are the ones with the knowledge and skill to actually own their data well.
8. There are many big challenges in data mesh but the biggest is probably getting and then _retaining_ buy-in across many different groups. If you don't have leadership buy-in, what happens when you hit challenges? If teams won't take on ownership, can you move forward?

Other Important Takeaways (many touch on similar points from different aspects):

1. Duncan mentioned that in Agile, there is a tendency to focus on a specific problem. But the biggest value of data comes from reuse, not just that focus on one specific problem/use case. So be prepared to adapt methodologies like Agile to data where circumstances are far different.
2. If you aren't able to deliver incremental value - and prove/communicate that incremental value - you are highly likely to lose your credibility and/or sponsorship of your data mesh journey.
3. As is often mentioned, your business colleagues probably - almost certainly? - don't care about data mesh. They care about getting good value from data. Data mesh can help us accomplish that but it's not the point. Talk to them about what you are trying to achieve and why, not about the inner workings of data mesh. What changes for them and why?
4. Everyone already has ways of working with data. Instead of trying to change the way they work, look to intersect and gently nudge them in a better direction. The key to that is helping them understand what is changing and why.
5. If you treat Zhamak's writings - or any other content around data mesh - as gospel, you are headed for failure. Zhamak has said this herself. We are figuring out how to do data mesh and you need to adjust for your own organization to do data mesh well.
6. Be prepared for your organization to be even more messy/varied than you likely expect once you start to dig deeper into your ways of working in general and how those could be better improved by data. Your organization likely grew organically - different parts of a large forest behave quite differently even if it's one forest.
7. ?Controversial Scott Note?: Someone made the point that to get buy-in, you can show people why it isn't sustainable for the organization to continue doing data the way we've been doing it. That generally isn't going to sway most people. They understand that but it's like saying everyone needs to do something like make their own clothes to fight climate change - you won't convince many…
8. Your organization's data culture needs to work like a moderately well-functioning society: you have rules and regulations that people need to abide by or face the consequences. Think about the phrase from "The Good Place": 'What do we owe each other?' Democratization/federation doesn't mean anarchy.
9. Your actual data mesh journey - maybe not the story you or others present at conferences - will be extremely messy. Organizations, people, life, etc. are all messy. Don't be worried about getting it perfect but worry about driving towards value.
10. ?Controversial?: You need a certain level of maturity to be able to tackle data mesh but you also, as Stefan mentioned, need urgency. If there isn't a need to really change your approach to data, is data mesh worth the effort? Probably not…
11. Much like with any transformation project, you need time to see results. That doesn't mean you can't deliver incremental value along the way but this is not a quick change at the overall organization level. That's why you transform pockets - domains/LOBs - and work on the greater organization over time.
12. In Agile transformation we have Agile coaches. Do we need similar in data? Do we need evangelists? It seems you need someone connecting the dots for people. Just because people can self-serve doesn't mean they will really consider the 'art of the possible' with data. Have someone inspiring and coaching them.

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