Data Mesh Radio Patreon - get access to interviews well before they are released
Episode list and links to all available episode transcripts (most interviews from #32 on) here
Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.
Winfried's LinkedIn: https://www.linkedin.com/in/winfried-adalbert-etzel/
MetaDAMA podcast (episodes in both English and Norwegian): https://podcasts.apple.com/us/podcast/metadama-en-helhetlig-podcast-om-data-management-i-norden/id1572639573
In this episode, Scott interviewed Winfried Etzel, Information Strategy Consultant at Bouvet, Board member of DAMA Norway, and host of the MetaDAMA podcast.
Some key takeaways/thoughts from Winfried's point of view:
Winfried shared that in the data mesh meetups for Norway, most of the discussion thus far has been about domain driven design for data, data discoverability, and data as a product. If you are not sure exactly how to approach those topics or what they mean for your organization, you are not alone. And no one is quite sure what federated computational governance means either.
In order to do data mesh, Winfried believes people should consider the emerging pattern of Transformation Teams. These are teams that help domains by collaborating to build out their first data product - or possibly more - while also upskilling the team. Scott Hawkins at ITV (episode 48) discussed something similar. The transformation teams also are a great source for finding reuse as they are working with the domains and can more easily recognize patterns. They also can have an excellent perspective on how your implementation is going thus far. We need people that can enable change while giving guidance. A concern though is that the number of transformation teams is limited - how can you go broadly in the organization quickly? Or do you need to be in a rush to do so?
In Winfried's view, we need to take far more concepts from software engineering in general in data. Zhamak has stated this multiple times as many, many software engineering practices inspired parts of data mesh. Both mentioned Team Topologies as a crucial tool for change management. But we also should look to other areas outside software too. Winfried sees history as providing a lot of inspiration for data literacy. Political science can be a good way to think about organization design and communication. Law has millennia of examples of good ways to present arguments / one's context. Manufacturing can give us good insight into how we think about products including lifecycle - as Alla Hale discussed in her recent episode.
There are many data maturity models, most of which are not that differentiated, according to Winfried. They are still useful when assessing your readiness to do something like data mesh - at the macro and the micro/domain level. Are you organized correctly? Do you have the capability to do what's needed? Do you have the capacity - both meanings: the amount of time and the capability - to change? Are your teams ready and willing to share? Etc.
Per Winfried, in general in data, we have not adapted and adopted change management techniques all that well. We need to focus on providing strong learning paths so individuals can change to being "data citizens" as we evolve the overall organization. To inspire people to want to change and get better with data, we need to share with them how data is important to their role. Otherwise, it is homework with no purpose in their heads.
Should we go for a big bang approach or incremental, domain by domain? Winfried thinks it shouldn't be only one or the other. When it comes to things like upskilling or even changes to ways of working, big bang might be a better approach so you aren't having to constantly fight against the inertia of the historical patterns and working across so much time to get everyone to a new way of working. But Scott asked about trying to do a big bang approach to new responsibilities and both agreed that can lead to issues.
And we need to make sure we keep ethics as something top-of-mind when people learn about data - just because you can doesn't mean you should... Winfried doesn't believe data ethics as a practice has really matured yet. There is of course AI ethics but that is typically about biased inputs, not as much the "should we really even be trying to figure this out?" Companies don't really see the business value in data ethics - which is a challenge that everything has to be about driving business value. There are many cases, especially in the US now, where we need to think about potential harm much more than just potential risk of data exposure.
Winfried shared his view on a few things regarding data mesh and domain data maturity. The most data mature domains typically want to do everything themselves - that includes not just building but even things like defining data products. That can lead to challenges when trying to integrate their work into the broader mesh. The domains you should look for are the ones that "understand what they need to do even if they can't do it yet." Those domains have the capacity - the willingness and the ability - to change.
Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him at community at datameshlearning.com or on LinkedIn: https://www.linkedin.com/in/scotthirleman/
If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/
If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here
Data Mesh Radio is brought to you as a community resource by DataStax. Check out their high-scale, multi-region database offering (w/ lots of great APIs) and use code DAAP500 for a free $500 credit (apply under "add payment"): AstraDB