#248 Doing Data Quality Right by Building Trust - Interview w/ Ale Cabrera
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 if you want to chat data mesh.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.Ale's LinkedIn: https://www.linkedin.com/in/alejandracabre/In this episode, Scott interviewed Ale Cabrera, Senior Data Quality Product Manager at Clearbit. To be clear, she was only representing her own views on the episode.Some key takeaways/thoughts from Ale's point of view:A key part of understanding what data work will be impactful is a simple phrase: "Is my understanding correct?" Putting out there what you took in and making sure you're on the same page will save a ton of time and headaches!Her advice to her past self: In data, far too often, we try to jump to solutioning instead of really taking the time to understand the problem. Start from understanding the problem and assessing it first.It's very easy to make data say something that it's not actually reflecting. Quality isn't just about accuracy or similar metrics, sometimes there are intangible aspects around correctness that people get but usually can't measure.In data work, many people miss two crucial aspects - the voice of the customer and the why. If you build the greatest thing ever but it isn't what the customer wants, it won't be used. Similarly, if you focus on the work and not the target outcome, your results are likely to be subpar.If you want to prove data work return on investment, try to associate it to a key company metric and talk about how improving that metric will drive better business outcomes.When you want to prove out the value of data quality, attach quality issues to direct business challenges or goals. It’s easy if you are a company selling data but you have to understand why bad quality...