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

Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.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:There are no silver bullets in data. Be prepared to make trade-offs. And make non data folks understand that too!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.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.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.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.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??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. 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