Data and business teams become a convoluted intersection, and when they struggle to communicate, it leads to bigger problems than awkward water-cooler talk.So what comes first? Translation? Data literacy? Company culture? The chicken? The egg?Co-founders of Preql, Gabi Steele and Leah Weiss, join hosts Tim and Juan to discuss how to put the business in charge of their own data and how this leads to the answers AND massive alignment between data and biz teams. Key Takeaways [00:05 - 04:15] Introduction and Cheers to Tim[04:17 - 06:50] What is the wildest or weirdest thing you've seen while stopped at a red light?[06:55 - 10:29] Putting businesses in charge of their own data[10:29 - 12:04] Inviting the business into the process and building community[12:05 - 15:21] Developing a curriculum for data, teaching SQL and data visualization[15:22 - 19:17] Refining the model and curriculum, tailoring the fit[19:17 - 21:41] Identifying the people who wanted to be part of data modeling[21:43 - 22:55] Teaching interested parties another way to handle data, using an application process to find people[22:57 - 24:17] Stewardship in handling data[24:17 - 27:04] The process of engaging and completing data modeling: a brain for architecture, python, and sql[27:05 - 28:32] Interesting problems to solve, and you have to be creative to get there[28:36 - 30:40] Understanding technical debt and how to support engineering teams, skills for good data modeling stewardship[30:42 - 33:18] DBT and building robust analytics teams[33:30 - 37:35] Tooling solutions for helping business be in charge of their own data[37:42 - 38:52] Focus on the technical side, and a no-code semantic layer[38:53 - 42:31] Bringing in a technical analytics engineer to the business team[42:31 - 45:33] The knowledge gap and bridging it with a framework[45:33 - 49:39] Alignment and building the semantic layer[49:43 - 56:36] Lightning Round[56:38 - 01:01:30] Takeaways[01:01:32 - 01:05:50] Three Questions