#298 Effective Partnering With Business Execs - Learnings from Another Data Mesh Journey - Interview w/ Jessika Milhomem

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Jessika's LinkedIn: https://www.linkedin.com/in/jmilhomem/

In this episode, Scott interviewed Jessika Milhomem, Analytics Engineering Manager and Global Fraud Data Squad Leader at Nubank. To be clear, she was only representing her own views on the episode.

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

1. There are no silver bullets in data. Be prepared to make trade-offs. And make non data folks understand that too!
2. Far too often, people are looking only at a target end-result of leveraging data. Many execs aren't leaning in to how to actually work with the data, set themselves up to succeed through data. Data isn't a magic wand, it takes effort to drive results.
3. Relatedly, there is a disconnect between the impact of bad quality data and what business partners need to do to ensure data is high enough quality for them.
4. Poor data quality results in 4 potential issues that cost the company: regulatory violations/fines, higher operational costs, loss of revenue, and negative reputational impact.
5. There's a real lack of understanding by the business execs of how the data work ties directly into their strategy and day-to-day. It's not integrated. Good data work isn't simply an output, it needs to be integrated into your general business initiatives.
6. More business execs really need to embrace data as a product and data product thinking. Instead of a focus on only the short-term impact of data - typically answering a single question - how can we integrate data into our work to drive short, mid, and long-term value?
7. ?Controversial?: In data mesh, within larger domains like Marketing or Credit Cards in a bank, it is absolutely okay to have a centralized data team rather than trying to have smaller data product teams in each subdomain. Scott note: this is actually a common pattern and seems to work well.
8. Relatedly, the pattern of centralized data teams in the domains leads to easier compliance with regulators because there is one team focused on reporting one view instead of trying to have multiple teams contribute to that view.
9. When you really start to federate data ownership, business execs can now partner far easier with other business execs in other domains leveraging data. Instead of having the central data team trying to translate, there is a focus on what needs to get done and the data work flows from that instead of the data work being the focus. It's the engine that powers their collaboration but it's no longer 'the point'.
10. Partnering with those who "are closer to the reality" of the business, it's easier and more likely to drive good outcomes. Meaning: not the senior execs. But the senior execs often have to be on board with the work and the target results. So work on communicating up but closely collaborating at lower levels.
11. Data for regulators often has a LOT of potential reuse for your own organization. Lean into finding those areas where you can do the data work once and get value twice :)
12. ?Controversial?: Really consider role titles in data mesh. Data product owner might be too nebulous and quickly accumulate too many responsibilities. Data product manager is easier to understand the scope of responsibilities and the specific areas of focus. Scott note: this comes up A LOT and is generally starting with data product owner and moving to data product manager.
13. ?Controversial?: Data leaders need to understand product management. To really scale data work, we have to start treating all aspects as a product practice. CTOs down to software engineers need to understand product management, it's time for the data org to as well.
14. Data leaders need to have significant communication skills while maintaining their understandings of data best practices. It's all a delicate balance but the data work doesn't speak for itself.

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