Metadata management has been a topic for a while now. Lately, the industry is pushing that metadata is a knowledge graph problem. What does metadata in a pre and post graph world look like? Join Juan Sequeda and Tim Gasper with special guest Mohammad Syed, Head of Data Architecture & Engineering at Capco to chat about metadata and knowledge graphs. Key Takeaways: [00:06 - 02:40] Introduction & Cheers [02:43 - 03:55] What's your go-to karaoke song? [03:57 - 05:38] Metadata is a graph problem, yes or no? [05:55 - 08:23] What would we call metadata in a pre-graph world? [08:29 - 13:06] The biggest inflection point that has moved us collectively from a pre-graph world to a post-graph world [13:06 - 15:10] Use cases for metadata in finance [15:14 - 17:43] Different contexts in which you use data, [17:46 - 19:33] Metadata historically as a means of documentation [23:29 - 24:59] How we should be taking advantage of the graph structure [25:01 - 29:18] Applying basic graph techniques and algorithms to identify data use cases [28:11 - 30:30] The process of data governance and data protection [30:33 - 32:47] Outcomes of metadata graphs [33:04 - 35:19] Metadata management [35:26 - 39:59] How to get started with metadata mapping [40:05 - 43:30] Governance can't be living in an ivory tower [43:57 - 49:52] Lightning round [50:01 - 55:56] Takeaways [55:58 - 59:44] Three Questions

Entity resolution.Is it a single source of truth? Is it multiple sources of truth? Is it multiple data sources that refer to the same real-world thing? How do we make sense of it? How can we harness the value of data through entity resolution? Talk to Jeff Jonas from Senzing on this week’s episode of Catalog & Cocktails and you’ll understand. Key Takeaways [00:33 - 05:14] Episode intro & cheers [05:20 - 06:53] What is something you thought to be true in life that ended up being false? [06:58 - 08:15] Why don't we realize entity resolution if it is everywhere? [08:18 - 09:55] Tech stacks and tools for entity resolution [10:01 - 11:55] Record matching, link detection, fuzzy matching, and duplication [12:14 - 14:12] Master Data Management as the old school approach versus modern technology and its limits [14:18 - 18:38] Entity resolution is for the elite [18:48 - 21:11] Commoditizing entity resolution to the equivalent of a spell checker [21:14 - 25:41] Principles and Guidelines [25:51 - 28:48] The number of principles should fit on a single screen [28:50 - 33:03] Different levels of abstractions, and thinking about what kinds of things can solve similar problems [33:09 - 35:25] How does metadata tie into entity resolution? [35:32 - 40:45] Ontologies, semantics, and mapping [40:54 - 42:10] Thousands of transactions per second [42:14 - 46:23] You shouldn't have to tinker with any settings to be effective [46:28 - 47:44] ChatGPT [51:54 - 56:17] Takeaways [56:21 - 59:40] Three Final Questions

If there were a tombstone for this episode it would read “here lies Master Data Management,” But would you agree? In this episode, join Tim, Juan, and special guest Malcolm Hawker from Profisee, will discuss the history of master data management, the buzzwords surrounding it, and where it’s headed, assuming it’s still alive. Key Takeaways: [00:01 - 00:54] Introduction [02:31 - 04:55] Toast to sun on the beach [05:21 - 10:09] What's something in the world that is popular or trending that really needs to fade away and RIP? [10:36 - 13:33] How do we define MDM today? [16:43 - 19:52] How did we get here? Understanding the history of how MDM began [20:50 - 24:04] Governance: Where do we start? Approaches to figuring out how to decentralize [26:52 - 29:51] With the single version of the truth and for each context, how do we preserve the data integrity across independent or interconnected functions? [30:45 - 34:35] How data modeling and MDM apply to new capabilities such as data sharing and DBT [39:43 - 43:13] What does data ownership mean? [46:27 - 56:08] Lightning round [59:20 - 01:01:56] Advice, and important resources

Data modeling isn’t new. So why is it still a problem? Maybe the problem isn’t data modeling itself, but rather there is no modern solution for companies, or the incentives are not well understood. It’s a learn-as-you-go type of thing, but that’s where the trouble lies. Do you hire one data engineer and have them do everything? Do we encourage training in data modeling? OR… do we throw in the towel and keep doing things as they’ve always been done? Join Tim, Juan, and special guest Anna Abramova from SqlDBM to answer these burning questions on this week’s episode of Catalog & Cocktails. Key Takeaways [00:11 - 01:03] Introduction to Anna Abramova [01:03 - 04:03] Champagne vending machines & cheers all around [04:11 - 05:27] If you could model your home after one famous building or monument what would it be? [05:30 - 06:35] What's the deal with data modeling at SqlDBM [06:37 - 09:09] The origin story of SqlDBM [09:09 - 10:58] A modern approach to data modeling [10:58 - 12:04] What modern means in the context of data modeling [12:08 - 14:30] Why is data modeling now becoming such a hot topic? [14:33 - 17:24] Data modeling trends: startups and small businesses [17:25 - 20:41] Data modeling trends in medium-sized companies [20:43 - 21:32] Data modeling trends in large organizations [21:33 - 26:16] Summarizing data trends and real world applications [26:48 - 30:40] Metaphors around data modelings and architecting, problem solving [30:51 - 35:37] The biggest value triggers around data modeling [35:40 - 37:21] The pearl of the ocean [37:25 - 40:39] How are people learning data modeling? [40:42 - 43:17] A role or a skill? [43:18 - 45:10] Data modeling isn't the sexiest topic [45:12 - 46:16] A good foundation and resilience [46:26 - 49:39] Lightning round [49:55 - 55:19] Juan & Tim's Takeaways [55:37 - 58:51] Three questions about data and life

Imagine planting seeds and not watering them. Or putting water on the stove but not turning on the gas. Sounds silly, right? Now take your organizations data and think about not leveraging it as an asset. It’s like taking the initial step of planting something in the garden and deciding not to provide nourishment. Join Tim, Juan, and special guest Jane Urban from Takeda to understand the difference between a data asset and a data product and why companies still struggle finding the real value. Key Takeaways [00:06 - 02:14] Introduction & Cheers [02:15 - 03:46] What New Year's resolutions have you stuck to so far? [03:49 - 05:14] Leveraging data as an asset [05:20 - 07:06] Definitions and examples of data as a product [07:13 - 09:42] Business needs and data value [09:43 - 11:49] The transition from something smaller to something growing, and lessons learned [11:50 - 14:58] Metaphors Jane uses to make communication around data value simple and relatable [15:00 - 16:42] A manufacturing process metaphor [16:46 - 20:46] Data stewards and the idea of finalizing a system versus ongoing system maintenance [20:46 - 24:35] Data value in the pharma perspective [24:45 - 27:02] Outcomes for data assets to data products, or are they unrelated? [27:05 - 28:57] Data as an asset on the balance sheet [29:01 - 32:30] How digital technology its in an organization, and potential as a revenue generator [32:46 - 38:08] Navigating how to be agile in pharma, which is not often agile [38:24 - 40:08] Find the astronauts [40:09 - 42:35] Leadership and executive evolution toward digital growth [42:45 - 47:01] The good, the bad, and the ugly of starting small and working towards global teams [47:15 - 55:47] Lightning round [55:51 - 01:01:16] Takeaways [01:01:24 - 01:04:08] Three questions