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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.
Marcie’s LinkedIn: https://www.linkedin.com/in/marcie-stoetzel/
DGIQ Conference (Marcie and Samia Rahman will be speaking in June; in San Diego, CA, USA): https://dgiq2023west.dataversity.net/registration-welcome.cfm
Square hole video Scott mentioned: https://www.youtube.com/watch?v=cvwN_O5ypTE
Article about the “leaf sheep” sea slug Marcie mentioned: https://www.bbc.com/travel/article/20210324-the-odd-sea-creature-powered-by-the-sun
In this episode, Scott interviewed Marcie Stoetzel, Principle Product Manager of Enterprise Core Data at Seagen. To be clear, she was only representing her own views on the episode.
Before we jump in, Seagen might be a bit of a special case in how much the domains can leverage each other’s data around a key record type. There is a lot to learn but you might not be able to find use cases that are as broadly impactful to many domains at once.
Some key takeaways/thoughts from Marcie’s point of view:
- ?Controversial?: In data mesh, building a data culture focused on engagement, learning, and upskilling might be as important (more?) as doing the data work – if teams aren’t willing to engage, what’s the point of doing the work? Scott note: Marcie doesn’t explicitly say this but it sure feels like an undercurrent. It’s crucial to make your data culture something that can embrace data mesh.
- When going to domains, come with a target value proposition. Why would they get value from participating in your data mesh initiative? Scott note: If there isn’t a value prop for the domain, you will almost certainly struggle with incentivization. There are very few ‘good Samaritans’ willing to do a lot of data work with no discernible direct benefit to that domain.
- Seeing is believing. Don’t just tell people what you are going to do, get demos in front of them and show them more about what you plan to do.
- Make sure to solicit feedback as you build. It’s far easier to pivot after a short period of work than after a longer period. You can find misaligned expectations far earlier and work on appropriate prioritization. Basically, you spend less time building things that don’t matter and more time on things that do.
- Look for ways for domains to share about what information they have that might be useful in the organization. Oftentimes, domains operate so independently, they aren’t in the habit of finding ways to collaborate. Scott note: Communication is the easiest path to high-value, cross domain use cases – the mesh won’t find them for us.
- To actually get to cross-domain use cases, you need domains to consider things from another domain’s point of view. So extract more information about each domain to literally show to other domains to spark conversations. Be someone that connects the dots for domains and then connect people across domains.
- Once there is more visibility to cross-domain use cases, business goals can be realigned to focus on building to common goals. That can help with some incentivization and alignment. Scott note: I hadn’t previously heard this one articulated but it really does map with goal realignment where many organizations are seeing success with data mesh.
- When you are early in your journey, look to create a “thin slice of an end-to-end experience for [a] domain.” Really don’t try to do too much. Scott note: this is what pretty much everyone who has worked on at least two data mesh journeys circles back to. It’s VERY important to understand the thin slice concept.
- Interoperability requires a certain level of trust. You can decide by your use-case how strong that trust has to be. If you need it to be essentially perfect for regulatory reasons, that’s far different from the needs of many other use cases.
- Focus, especially early in your journey, is very useful. Again, thin slice. Don’t take on too much while you build initial momentum and buy-in.
- ?Controversial?: Find ways to humanize your data work, add a bit of levity and humor. It will make people connect to it more and internalize it more. Plus, it’s just more fun.
- ?Controversial?: Similarly, “explore, discover and mature data together.” It’s okay to be vulnerable, especially as we learn. Transparency is also crucial.
- Community and things like workshops are crucial to see wider adoption and engagement in a data mesh implementation.
- Consider if you want to use the phrase master data management (MDM) at all. Master has slavery connotations and MDM feels far too heavy as a phrase. It can scare off the exact people you want leaning in to dealing with Enterprise Core Data.
Marcie started off with a bit about her background as a teacher and as well on the commercial side of the healthcare space which has shaped her view of teaching/learning and also healthcare data needs. Part of Seagen’s goal in using Enterprise Core Data instead of Master Data Management (MDM) as a phrase was the moving away from the connotations with slavery (master) and that this data is core to the enterprise, that this is crucial to the organization, not just a data management practice or task. Enterprise Core Data at Seagen is about creating a way to make data that many domains leverage the same to prevent lots of domains doing the same work and make interoperability FAR easier. MDM also is typically managed centrally instead of enabled centrally and managed at the domain level (federated governance) so you have to rethink a lot to do Enterprise Core Data instead in a data mesh setup. Trying to map 1:1 to MDM in a historical data approach won’t work well.
At Seagen, Marcie and team decided to tackle one type of core data record first – healthcare professionals – rather than trying to unify every type of record across healthcare – don’t boil the ocean or bite off more than you can chew. They are working on creating the platform for domains to manage their core data records which creates more sharing opportunities and even higher quality data – teams can better cross-reference information. While they are still pre-production – it’s early days – even bringing this to domains’ attention is sparking conversations between domains about potential collaboration and new use cases.
Marcie and team are winning converts by showing them what the platform will be able to do for their own domain but also keeping an eye on that interoperability and leverage provided to other domains. That means that domains get some value from participating even if no other domains participate. If other domains do participate, then everyone gets more value from each other. They started with a simple value proposition – this will make handling your own data easier – and then created a group collaboration incentive – the more domains that participate, the better the information and the less work everyone has to do to get to better outcomes 🙂
When asked about incentivization complications around domains wanting to focus on their own goals, Marcie mentioned that as the domains are starting to find cross-domain use cases, the organization can realign goals to be about focusing on those common goals where everyone wins. What drives the most value for the business and how do we incent that kind of outcome/behavior? Scott note: this is an interesting nuance that I haven’t heard before.
How they found the cross domain use cases was also interesting. Marcie and team met independently with different business domains to extract what each team felt could be a good output of working with a common enterprise core data platform, as well as tying these individual value props back to the larger goal of helping more patients. The data team then literally took all those outputs and showed each business domain that uses HCP (healthcare professional) data the value across Seagen of having an enterprise core data platform. This sparked collaboration ideas between domains for how to drive even more value from the core data platform.
Marcie is seeing that domains have to spend a LOT of time to cleanse and match data. And downstream consumers of their data have to do the work too as they don’t know it’s already been done upstream. The quality requirements for most use cases are pretty high. So creating a way for domains to much more easily interoperate data will save them a LOT of time and effort. The core data platform will hopefully prevent lots of domains from having to do a lot of that quality checking work and it will also increase quality by having more sources of information to verify data is high quality / correct – basically, the checking of quality becomes far less arduous.
One thing that is working well at Seagen for Marcie and team is doing lots of demos and small proofs of concepts. Similar to doing sprint demos, teams are buying in because seeing is believing. They are showing the business real, realized value that will come from their participation. So teams are leaning in. Similar to what Karolina Henzel mentioned in episode #104, there can be a LOT of value in addressing data quality issues for domains.
Marcie talked about the value of maintaining focus on a thin slice. Instead of trying to get many domains bought in on the data mesh concept, there is a specific use case and they are only working with a few domains at the start. There are clearly defined and scoped benefits. Again, thin slice. And focusing on the end-to-end solution to working with this data for the domains has also helped to get and keep everyone on the same page.
Look for ways to share knowledge in fun and interesting ways. Upskilling can be a bit intimidating, make it more gamified and less high pressure. Humanize (or in Kye’s case, dog-ize) it a bit.
Really embrace an attitude of learning – be vulnerable and transparent. “Explore, discover and mature data together.” “Engaging conversations, exploration, curiosity, and a safe space” are crucial.
Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him on LinkedIn: https://www.linkedin.com/in/scotthirleman/
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