Catalog & Cocktails: The Honest, No-BS Data Podcast

Takeaways with Jane Urban

A conversation with the father of the data warehouse, Bill Inmon

Those who don’t know their history are doomed to repeat it. If there is someone who can speak to data history, it’s Bill Inmon. How did data warehouses start? Why is the computing profession still immature?Join Tim Gasper, Juan Sequeda and Bill Inmon, the father of the data warehouse and Founder of Forest Rim Technology, to learn about the past and present of data, and where things might be heading in the future. Key Takeaways[00:05 - 01:00] Introduction to the Father of the data warehouse, Bill Inmon[01:11 - 02:28] Cocktails & Cheers[02:31 - 03:33] What actor would you cast for yourself?[03:39 - 06:47] Running in circles in the data world, a result of the profession's maturity[06:49 - 10:20] Bill's recent post about loyalty to technology versus loyalty to business[10:22 - 12:58] Specialists in computer technology, how programming has changed over time[13:06 - 15:08] Continuing education and certifications with programming paths[15:10 - 24:40] The history of computing: from army, to mafia, to your pocket[24:40 - 32:22] Key moments and milestones Bill has observed throughout his career in networking[32:28 - 33:59] Understanding the customer and looking after them[33:53 - 38:00] Microsoft Chatbot[38:00 - 41:04] A solution in search of a problem, showing business value[41:10 - 43:41] The Inmon and Kimball approaches[43:58 - 45:50] What's next for Bill?[46:11 - 58:46] Lightning round[58:53 - 01:04:16] Takeaways

Takeaways with Bill Inmon

Season 4 Finale

Well, another incredible season of Catalog & Cocktails concludes this week with hosts Tim Gasper and Juan Sequeda.Join in for the ultimate takeaways of the takeaways as Tim and Juan recap best moments, favorite hot takes, and the most controversial opinions over the last season. Listeners, please submit your feedback: https://forms.gle/FdjMfarUaVnJ3SzB9Key Takeaways[00:05 - 02:40] Introduction, end of year growth[02:43 - 04:54] Themes in culture, team structures, education opportunities[04:55 - 06:11] Training and Enablement[06:11 - 06:51] Shiny object syndrome, magpie syndrome[06:53 - 07:55] Loris Marini and culture, genuine conversations[07:56 - 09:09] Data problems with Laura Ellis, celebrating what people do with data[09:11 - 10:49] Culture, self-service, and shadow IT[10:54 - 11:09] Shared KPIs and alignment[11:12 - 12:40] Follow the money, and ask why you're working on what you're working on[12:40 - 14:29] Data projects don't fail for technical reasons, aligning business value, ROI is the key[14:35 - 15:09] How the business makes money, and where does it flow to organizationally[15:26 - 16:59] Who are the right people, and how do you continue to motivate and empower them[17:01 - 18:13] Ask for opinions and anecdotes, starting small[18:15 - 19:39] Loris on emphasizing connection and relationships, curriculum-driven development[19:40 - 21:35] Laura Ellis on data user experiences[21:39 - 23:19] The Chief Data Officer and managing data[23:24 - 26:17] What does real time streaming mean, and its impact on business, cost, reporting[26:45 - 29:59] AI and how it is already affecting data, knowledge, and more instant feedback[30:01 - 30:45] Where is AI funding coming from?[30:46 - 33:19] Horizontal and Vertical AI, value and use cases[33:19 - 34:00] Putting brain power toward ads and clicks instead of something like solving cancer[34:02 - 35:24] More education for people with diverse backgrounds, AI teams creating their own feature sets[35:27 - 37:16] "What should a catalog do?"[37:17 - 38:50] The spectrum of search, a lifecycle of data[38:52 - 40:19] Cross-functional collaboration and business expertise, strategies for metadata transformation and integration[40:21 - 41:32] Data modeling, cataloging, and semantics[41:34 - 43:29] Semantics with Dan Bennett, and data layers[43:32 - 45:58] Jumping into the pool with Allison Segraves[45:59 - 49:21] DGIQ, data governance, and closing out semantics[51:38 - 54:21] Predictions for the next year

2022 State of Data Governance

What better place to discuss the current state of data governance and what should the industry be focusing on than a live episode at the Data Governance and Information Quality (DGIQ) Conference.Join Tim Gasper and Juan Sequeda LIVE from DGIQ in Washington, D.C. with special guests Anthony Algmin and Shannon Moore to have an honest no-bs discussion data governance and how it's all about people.Key Takeaways[00:09 - 03:39] Introduction, Cheers & Drinks[03:40 - 04:38] Is Die Hard a Christmas movie or not?[04:38 - 06:06] The State of Data Governance in 2022, making people care[06:06 - 08:28] Making data governance about people and how they measure success[08:29 - 10:54] The biggest opportunities for getting people to care more about data governance[10:54 - 12:56] Data governance is a means to an end for corporations, a hotel analogy[13:02 - 15:03] Think like a marketer, and data as a product[15:04 - 17:18] Providing a value proposition for data governance[17:18 - 19:37] We need to talk to our marketing friends[19:38 - 21:08] Making data governance an ongoing business function, understanding the inherent value[21:12 - 23:33] Governance is still very project-based, and not often holistic as a function of business[23:41 - 26:20] Data privacy regulation to comply with[27:31 - 30:41] Education pathways in data governance[30:42 - 34:46] Strategies within organizations, business and leadership goals[34:59 - 37:50] The most important priority for governance in 2023[37:59 - 40:16] What vendors should be doing differently in 2023[40:21 - 45:43] Lightning round[45:54 - 50:17] Takeaways50:18 - 53:40[] Three final questions

Getting into the Pool without Drowning. w/ Allison Sagraves

Think back to when you were first learning to swim. How’d you do it? Chances are, you weren’t thrown into the ocean being circled by sharks. We sure hope not, anyway. You probably picked it up under the watch of a lifeguard in most cases.But once it became second nature, the lifeguard didn’t hold SO much power. Well, data engineers are the lifeguard, and if there isn’t a checks and balances system between them and the business teams, the engineers will be determining your every move in AND out of the water.Join Catalog & Cocktails hosts Juan and Tim, with special guest Allison Sagraves as they discuss how you get in the water by yourself without drowning.Key Takeaways: [00:06 - 04:15] Introduction & Cheers[04:18 - 05:31] Beach, lake, or swimming pool?[05:35 - 07:17] Entering the data pool without drowning[07:28 - 09:40] The pool is like the new sandbox[09:42 - 13:53] Cutting through confusion and BS[14:00 - 17:03] Data literacy and understanding that everyone needs to be trained on all things[17:09 - 18:10] A dog paddle towards more technical areas of focus during a transition[18:16 - 20:36] The idea of data products[20:37 - 23:52] Why Allison cannot stand the phrase "data is the new oil"[23:54 - 29:34] Building data products is not an easy thing to do[29:37 - 32:33] Culture and a digital mindset[32:38 - 36:25] Allison's experience as the CDO at M&T Bank for 30 years[37:28 - 41:40] The data pool analogy, and the lifeguard for that data[44:14 - 51:08] Lightning round[51:12 - 56:00] Takeaways[56:06 - 01:02:21] Three questions[01:02:28 - 01:03:27] Next week's episode

Takeaways with Allison Sagraves

Are data teams keeping up with AI teams? w/ Theresa Kushner

You have to have a lot of data to get AI to work. But the data folks are not jumping on it as fast as they should. So what happens when data teams aren’t up to speed, companies are hiring more data scientists than they are engineers, AND current data teams are focusing too much on biz reporting and not supporting AI?This week on Catalog & Cocktails, join hosts Tim Gasper and Juan Sequeda as they chat with special guest, Theresa Kushner, Head of North America Innovation Center at NTT Data Services to discuss how the AI train is leaving the station and data teams can only run so fast. Key Takeaways[00:10 - 02:25] Introduction & Cheers[02:28 - 04:12] What's your favorite way to travel and why?[04:15 - 07:01] Are data teams keeping up with AI teams?[07:01 - 08:50] Are data teams and AI teams helping each other or avoiding each other?[08:54 - 13:09] AI teams become a data set in themselves[13:10 - 14:45] Data ownership and control[14:51 - 17:20] Thoughts on purchasing data[17:20 - 20:53] Data products and observations[20:53 - 24:52] CDO versus the CDAO, definitions and comparisons[24:53 - 27:05] Should there be a CDO or a CDAO?[27:03 - 30:04] Data makes AI work[30:02 - 34:58] If you want results you have to collaborate[34:59 - 37:29] Creating culture tied to data quality[37:27 - 42:01] The skill sets for managing data products[42:02 - 46:09] Theresa's message of advice to data teams[46:12 - 52:36] Lightning Round[52:36 - 58:16] Takeaways[58:17 - 01:00:31] Three questions

Takeaways with Theresa Kushner

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