Where are the semantics in the data dictionary? w/ Dan Bennett

Machines and people. Why can't we just speak the same language? The truth is we can, and doing so could make life demonstrably better for data scientists. Yet here we are, living in a world of rows and columns that few people outside of the data owner understand.Join this weeks episode of Catalog & Cocktails as hosts, Juan Sequeda and Tim Gasper with special guest, Dan Bennett, tackle semantics and how to get everyone -- machines and people -- on the same page.Key Takeaways:[00:01 - 02:47] Intro & Cheers[02:49 - 04:57] If you were the picture for a word in the dictionary, which word would it be?[04:58 - 08:35] The Greatest Sin of Tabular Data[08:40 - 11:02] Examples of semantics missing inside of tabular data and their utility[11:03 - 12:39] Adding context and profiling data[12:43 - 14:55] How are constraints and semantics being defined, and what is a scaleable approach?[15:02 - 16:24] Data producers and enriching data[16:30 - 17:58] Enrichment that travels with the data[18:00 - 20:14] What are the tools we use, the data dictionary, and standardizing[20:16 - 22:34] Metadata and the bridge to the semantic world[22:36 - 24:57] Innovation and Dan's thoughts on relational model table relationships[24:57 - 28:09] Solving the same problems over and over again[28:12 - 30:19] Network effect, the marketplace of ideas and social spheres[30:21 - 33:56] Diving into the network effect and the semantic world[33:58 - 36:48] Why redefine if an option exists that can be used, and thoughts on simple ideas being the best solutions[36:52 - 40:11] Figuring out supply and demand curves for S&P Global[40:12 - 44:34] The business value of data and data literacy in accurate findings[44:44 - 46:19] Advice to data leaders and vendors[46:37 - 50:14] Lightning Round[50:29 - 56:48] Takeaways[56:52 - 59:44] Three final questions