Implementing Data Mesh in an Organization: Challenges and Feasibility

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Iulia's LinkedIn: https://www.linkedin.com/in/iuliavarvara/

In this episode, Scott interviewed Iulia Varvara, Advisory Consultant in Digital and Organizational Transformation at Thoughtworks. To be clear, she was only representing her own views on the episode.

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

1. If you are greatly changing your general approach to something - which data mesh does in many ways - you need to focus some amount on actual transformation. These approaches are not a switch you flip, it takes time and concerted effort to make lasting changes that work well.
2. If an organization hasn't really broadly embraced product thinking, starting with data as a product/product thinking in data can act as a catalyst for other aspects of the business to embrace product thinking.
3. You don't change the organizational mindset through words - you start using new ways of working that change people's mindset as they see the benefit of those ways of working. At the end of the day, talk is cheap.
4. To do data mesh well and have it work for an organization, it's best to tailor to their existing ways of working. Yes, change is necessary but a revolution is far less likely to work than an evolution. How are teams working and where can we make smaller tweaks?
5. Because you need to tailor your implementation to your own organization, any data mesh blueprint that will supposedly work for all organizations is likely to be snake oil at best.
6. ?Controversial?: The first two principles of data mesh - domain data ownership and data as a product - have the most impact on the organizational operating model. Scott note: federated computational governance might be up there for some organizations too.
7. With data mesh - or really any change to your operating model - you need to make the goal(s) and measures of success visible to your people. That way, people can understand if progress is happening and what to prioritize.
8. In product thinking, everything really centers around user value. It will inform your strategy, vision, and business goals. Create feedback loops to constantly test against serving user value.
9. Create long-lived teams around your data products. If not, there is a significant risk of falling back to project thinking which will mean fewer innovations/experiments with the data and not enough subject matter expertise embedded into the data products to make them truly valuable.
10. Similarly: "stop funding the work, start funding the team." Stop trying to focus on exact tasks to be done instead of outcomes to achieve. That's key to product thinking.
11. Many people believe you have to completely change your company to start having domains own their data. Start with one or two domains, don't change the entire company upfront.
12. When funding the teams, there is an assumption that the team will experiment and find more and more things that are valuable to users. But that ability to _find_ value is crucial - you won't always know what is valuable ahead of time, give the team space to find value.
13. Also, by funding long-lived teams, they can learn more and more about what drives user value and keep an eye out for work that is no longer aligned to user value - you can shut down work that's no longer valuable. This is a rarer occurrence in data than people expect, shutting down work.

Iulia started with a few basics about general transformation and digital transformation, whether that includes data or not. To really be able to embrace a digital and data-driven future, organizations need to embrace product thinking across the entire organization. They need to align their strategy and operating model to be adaptable and flexible. If the organization has already embraced product thinking, few have really pushed that to data in her experience. But if the organization is new to product thinking entirely, then starting from the data side could create a strong catalyst because data is probably one of the hardest concepts to apply product thinking to - after taking on data, product thinking is far easier to grasp in other areas of the business.

In general, Iulia believes that mindset changes don't come from mandates. Instead, by implementing new ways of working, people's mindsets will start to shift once they see the impacts/benefits of those new ways of working. They see the benefit and change their mindsets. But that will of course take time and concerted effort - the mindset change by decree is faster but doesn't typically stick. Show don't tell.

As other guests have noted, Iulia pointed out to maximize the chance of a data mesh implementation succeeding, you have to take into account the existing ways of working, the organizational and team operating models. Yes, certain aspects will need to change but trying to completely change an organization's operating model is going to be too disruptive. Instead, align a transformation paradigm to how the organization already works so people can evolve and adapt. Don't throw them in the change deep end and don't throw the baby out with the bathwater. Of course, this also means there isn't some blueprint for data mesh that will work for all organizations.

In Iulia's book, the first two pillars of data mesh - domain-based data ownership and data as a product - are the two that have the biggest impact on the organizational operating model. She said, "When you start thinking about your data in terms of products, and put your user in the center of your attention, you try to organize all your efforts around the user needs. Right? You create this connection between the data team and the value." That is a big change to how most organizations work around data and it will take effort to make it happen.

In general, Iulia recommends that for any large operating model change, you really need to clearly communicate multiple things. What are the changes, why are you making them, what is the actual target outcome/goal, what are the measures of success, etc. That way, people can measure how well things are moving forward and more easily prioritize. "Because there would be so many things to be done at the beginning, that team really needs to have a clear understanding what to start with." Transformation will mean tens of changes, understanding where to start and why are crucial.

Specifically to product thinking, user value is your Northstar for Iulia. It will inform your strategy, vision, and business goals. Those business goals will be split into hypotheses of value for how you can reach the goals. This is where you start to allocate teams, to the actionable items from the hypotheses of value. But it all comes back to focusing on user value. Steer your work through feedback loops to focus on that user value and you have a great shot at implementing product thinking/focus well.

Iulia pointed to something many miss when it comes to treating your data as a product. If you don't have a long-lived data product team, it can cause many issues that significantly undercut the value of building data products. One is that you often lack the subject matter expertise in the data product team, so the information encapsulated is not nearly as deep or as relevant to the topic area for the data product. Another is that if the team isn't long-lived, will they really have the time and psychological safety to run experiments and innovate?

Similarly to data as a product, Iulia recommends going small, then sustaining, then scaling when it comes to domain ownership. Basically, start from one to two domains and go broader over time. Trying to reorganize your organization on day one so one or two domains can own their data in that time-frame, that's a TON of effort. Don't try to revolutionize your company to do data mesh, evolve and build the understanding as you go broader. You need to prove out value first too before you go broad.

In wrapping up, Iulia returned to the concept of funding the teams, not the work, and especially long-lived teams. When you fund the teams, they are able to focus on finding value. There isn't an expectation of the teams to be prescient, always knowing what will be valuable. And there isn't a need to simply react to tickets instead of finding what will be of value. The other aspect is that you can understand what should be decommissioned. Far too often, data work continues well past when it is valuable. But with a product mindset, teams can constantly be focused on user value and shut down things that no longer drive value.

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