#163 Improving the User Experience for All Parties - Early UX Learnings from Data Mesh at DNB - Interview w/ Alice Parker

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!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 / Scott Hirleman. 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.Alice's LinkedIn: https://www.linkedin.com/in/aliceparker/No Silver Bullet: Essence and Accident in Software Engineering by Fred Brooks: https://www.cgl.ucsf.edu/Outreach/pc204/NoSilverBullet.htmlIBM Research paper mentioned: https://dl.acm.org/doi/10.1145/3290605.3300356Microsoft Research paper mentioned: https://dl.acm.org/doi/10.1145/2884781.2884783In this episode, Scott interviewed Alice Parker, Data Engineer at DNB.Some key takeaways/thoughts from Alice's point of view:It's easy for people to confuse user experience (UX) and user interface (UI). But UX is far deeper than most understand. We need to design systems and experiences that make working with data - as a producer or a consumer - far easier and more delightful.People are very willing to talk about their challenges - show some empathy and give them the space to talk about what is holding them back and what they could do if you worked with them to address those challenges.Data consumers need three major things to work well with data: 1) domain expertise, 2) time, and 3) to be able to "converse" with their data.Ensure your data quanta - or really any aspect of your data mesh implementation - are documented for all your user personas. There may be different needs for each persona type. A data scientist probably doesn't need as detailed of explanation of...