Sustainable Data Transformation and Driving Towards Data Mesh

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

In this episode, Scott interviewed Stefan Zima, Data Transformation Lead at RBI (Raiffeisen Bank International AG). To be clear, he was only representing his own views on the episode.

Some key takeaways/thoughts from Stefan's point of view:

1. No one has data mesh all figured out. Go talk to each other. But also don't be ashamed that you are running into challenges. So is everyone else. Data mesh implementers also need to share more of the anti-patterns they are finding.
2. Agile transformation really focuses a lot on communication and transparency. Both are very crucial to really any successful transformation initiative. Humans struggle with uncertainty and change so giving them a lot of information especially about the why prevents unnecessary pushback.
3. Relatedly, there are many things we can take from Agile transformation practices to apply to data/data mesh transformation. It's not a copy/paste but there's still much that is very relevant with some tweaks.
4. Many organizations are still focusing on technology-led transformation, whether data or digital in general. You must also change the mindset and organizational approaches if you want to be successful.
5. In banking, the rise of fintechs (financial technology companies) has made it clear that being nimble and quickly acting on data is crucial. Being data driven is required to remain competitive.
6. Data mesh can mean far less friction in getting to serving use cases. Instead of fighting against the data protection office, they are involved from the start. That time to market is especially crucial in banking now.
7. If you can, look to make your data sharing policies and approaches generic enough to only create friction when there truly is something different that should be examined further.
8. If you really want to be 'data-driven', if you really want to be a data company, you have to find and address the friction points in your data processes. Stop trying to simply get better at processes that stopped scaling and have high friction, find new ways.
9. Don't try to sell everyone on flying to the moon when you're struggling to get the garbage taken out every night. Get grounded in people's day-to-day challenges and sell them on getting better and better on delivering on them so they actually can start to get buy-in to the bigger picture.
10. Relatedly, talk to the vision and the goal but show the pathway to getting there. You can't just drop product thinking on the organization and expect overnight uptake.
11. Communication is incredibly important to transformation. You need top-down support, not just behind the scenes but in day-to-day internal communications. People need to understand transformation is a priority. Leverage your communication department.
12. To maintain mesh momentum, you really do have to prove out value and then get further buy-in from upper management - you don't just get to build without proving value. That proof of value will add more positive pressure on other domains to get on board.
13. To get people bought in, focus on incentives. Words and asking are all well and good but really, incentivization is the key to getting participation and buy-in.
14. In data mesh, it's easy to get too focused on either the small scale - only focusing on use cases - or the overall implementation. As a leader, you need to split your focus between the execution and the big picture. At some point, mesh needs to scale to make it worthwhile so that big picture matters even early.
15. ?Controversial?: When doing an Agile transformation, the role of Agile coaches was quite helpful. Is there something similar that could work for data mesh?
16. Take the data product concept and find a clear definition for your own organization. And it's okay to have something that extends the concept. For example, at RBI, a dashboard is a business product, not a data product.
17. When it comes to risk mitigation, the Data Protection/Privacy Officer can just say no to every request and there is no risk taken, so job done, right? As data people, our job is to clearly explain the risks and how we are mitigating them to make it easy for the DPO to say yes. Talk to them early to design compliant solutions.
18. ?Controversial?: Self-service is more than just providing access to the data and it's certainly more than just tooling/the platform. You have to teach people how to find, access, consume, and especially understand the data. And then teach them to also share what they've learned via sharing data in a safe and scalable way.
19. While incentivization is crucial, it's also often hard. E.g. what is the incentive for someone to put really complete metadata around a data product into the catalog. Can't any users just come to the owners with questions?
20. When you start your data mesh journey, make sure to start with FinOps. So much of data work is opaque, get your arms around costs and also cost savings. This will prevent challenges down the road 😅
21. Simply put, transformation takes time and energy/effort. If you try to do it as a big bang, all you do is blow things up.

Stefan started with a bit about his background and current focus. From having worked in data and transformation, the current transformation needs - data and digital in general - for many organizations are accelerating. There is a need to move towards more modern platforms and a self-service orientation of course but the organizational change especially around mindset cannot be overlooked.

As for why the banking industry is embracing data mesh and being data driven, Stefan mentioned that over the last five years or so, there have been many major industry changes. One aspect is the rise of fintechs that are built from day one with data in mind - to stay competitive, more traditional banks need to be able to move extremely quickly and the best - only? - way to do that intelligently is with data. Another aspect is simply the domains are getting more and more capable around leveraging information to gain competitive advantages so they need to feed that with quality data to win in the market.

When the bank started to see more and more friction - e.g. more pushback from the data protection officers to comply with ever advancing and increasing regulation - Stefan and team realized they needed a new approach. Instead of trying to make improvements to the existing processes, there needed to be new ways to get things done instead of relying on existing bottleneck creating interdependencies. That's part of what led them to data mesh - taking points of friction and shifting them left and reducing handovers meant faster time to market with new information and use cases.

Stefan talked about how many people in transformation and data push too far, too fast with their vision. When people are stuck in their day-to-day, getting them to imagine the company that could be in 5 years is at best inspiring but often not and it certainly doesn't help them today. Help them connect that vision of that 'data-driven future' to what you will do for them now. Don't expect people's mindsets to shift overnight.

Relative to transformation, Stefan discussed how important internal communications is. There is of course the messaging to drive understanding but also the communication by the leaders to show support. If you lack visibility of your top-down support, it's much harder to get people to take your efforts - data mesh or otherwise - seriously.

At RBI, Stefan noted that they are through their data mesh PoC phase. They were able to prove out significant value to upper management and get upper management to further buy-in. With that buy-in, there was more communication internally, getting more and more people aligned that data mesh was the way forward. But of course, data mesh is in some ways just a label and approach, it's not the point.

Stefan talked about what can we take from Agile transformation and successful business transformation in general to use for data/data mesh transformation. A big focus in Agile is on communication and transparency. When people feel informed and heard, they are far more likely to buy-in. Humans struggle with uncertainty. When it comes to data mesh, there will be all kinds of new roles and responsibilities so you need strong communication to keep people informed and not feeling lost. Even if that is about trying something and seeing if it works. That honesty and transparency will have far more people leaning in than trying to issue proclamations. And of course, be ready for some politically driven issues because we are changing the way people operate.

The ever present 'what is a data product?' conversation also happened at RBI according to Stefan. And it was important to define it in their own world - every organization has their own needs, understandings, and requirements/restrictions that will mean data products look slightly different. Specifically for RBI, a dashboard isn't a data product but is a business product, meaning there is still a clear ownership model. They also focused a lot on automation and risk assessment/mitigation. In a heavily regulated industry, risk is always a crucial factor. When it comes to working with the Data Protection Officers, it's important to make it safe and worthwhile for them to say yes when saying no is easy and prevents all risk.

Stefan then went into a bit about where RBI is headed around self-service and why that's so crucial to the company's data mesh ambitions. For him, self-service is far more than just giving people access to data. Yes, you need a platform but you need upskilling and data literacy, an understanding of compliance, an understanding of your overall data ecosystem, and an understanding of the tooling and processes. You also need to build a platform that can be leveraged by non-experts. You need to make it easy for the general populace of your organization to actually consume, understand, and produce data.

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All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf