Metrics are one of the most important and durable primitives in a company. And if data teams are looking to create value FAST… they have to learn how their businesses work. To figure out how you business works, you have to find the right starting point; back to the METRICS. Join Tim, Juan and special guest Abhi Sivasailam from Levers Lab on this week’s episode of Catalog & Cocktails to hear more about the most valuable use cases. Episode highlights: [00:09 - 02:45] Introductions & Cheers [02:49 - 03:42] Warmup: What are things in life that you wish happened faster? [03:49 - 04:57] Abhi's talk about data teams and company growth models [04:59 - 07:21] There's too many data people [07:24 - 08:57] What are people wasting their time on: variance and standardization [09:01 - 11:07] Challenges to understanding the business and growth models [11:12 - 13:03] Metrics form the core of well-defined end states [13:05 - 15:03] What Abhi thinks of as a metric, and how it ties into the knowledge graph [15:04 - 16:37] A plan of attack improves semantic knowledge and avoiding building as you go [16:47 - 17:51] The accretive development problem [17:53 - 22:48] A core structure of standards with uniqueness layered on top [22:53 - 25:30] Focus on specificities instead of repeated work [25:46 - 27:24] Not a bridge too far to say the semantic layer doesn't need to be that creative, but more standardized [27:32 - 29:09] Abhi thinks automated integration is impossible [29:19 - 32:29] Arbitrary uniqueness vs being coupled to an open standard [32:31 - 33:40] SOMA [33:42 - 36:22] How does SOMA affect a data person's life, and how does a company adopt the standard? [36:23 - 40:09] Building a web of metrics for growth [40:12 - 42:06] The relationship between metrics, SOMA, and OKRs [42:31 - 49:56] Lightning round [50:00 - 55:27] Takeaways [55:29 - 57:59] Three final questions

Juan and Tim give their ultimate takeaways from Gartner in Orlando with a TON of guests. Guests like: Cindi Howson, Chief Data Strategy Officer at Thoughtspot, and Host of the Data Chief Podcast Wayne Eckerson, President at Eckerson Group Sean Anderson, Host of Sources & Destinations Podcast, and Head of Product Marketing at SelectStar Benn Stancil, CTO + Founder @ Mode Malcolm Hawker, Head of Data Strategy, Profisee Malcolm Chisholm, President, Data Millennium Kuldeep Sharma, Sr Manager Data Governance, Enterprise Data Vassil Momtchev, CTO at Ontotext Anthony Colichio, Senior Value Engineer, Reltio

In this episode of Catalog & Cocktails, Kristin Schooley from Learning Care Group sits down with Juan Sequeda and Tim Gasper to discuss the importance of teamwork and building relationships when it comes to scaling an analytics team within a large organization. The conversation covers a lot of ground, including the need to understand what reporting is needed and what story is being told, how to ensure scalability and compliance, as well as the importance of measuring usage and interpreting why certain data may not be utilized. Key Takeaways: [00:00 - 01:10] Introduction & Cheers [01:15 - 03:11] What was your favorite class in school and why? [03:16 - 06:34] A data team of five [06:36 - 11:29] Partnering with business units and thinking about what is being learned [11:43 - 14:07] Data as a product and lifecycle management [14:22 - 15:13] Data literacy isn't necessarily the phrase we should be using [15:15 - 18:13] Business analytics and understanding how to present data [18:16 - 22:51] How organizations organize their data teams with checks and balances [22:52 - 26:18] Business literacy, centralized teams, and scaling beyond a bottleneck [26:20 - 30:31] Incentivizing for having documentation up to date [30:40 - 34:57] Lightning round [35:00 - 39:46] Tim & Juan's Takeaways [39:46 - 43:06] Three questions

Are we doing data and analytics correctly? Self service, centralization vs decentralization, analytics vs operations… so many aspects that data teams need to consider. Join this week’s episode of Catalog & Cocktails with hosts Juan and Tim as they speak with special guest Bethany Lyons to discuss how we should have a separation between data operations an data analytics Key Takeaways: [00:10 - 02:36] Meet Bethany Lyons, Chief Proctor Officer at KAWA Analytics [02:40 - 04:23] What's your favorite board game? [04:24 - 05:29] Encouraging self-service analytics is why Bethany thinks we are doing data analytics wrong [05:30 - 08:04] How the self-service analytics problem started [08:10 - 11:12] How business operators utilize data, and examples with Tableau deployment [11:13 - 14:09] Tying business logic to an analytics tools and what could go wrong [14:10 - 15:55] "Is this an analytics request or an operational request?" [15:55 - 16:35] Changing parameters and asking the right system [16:35 - 19:35] Evolving from self-service analytics to self-service governance programs [19:39 - 21:14] Spreadsheet data, security, and incentives [21:22 - 24:25] Self-service governance, shared accountability paired with great security [24:26 - 25:50] Data stewards and ownership over business areas [25:51 - 27:04] Sharing, auditing, and tracking [27:12 - 28:50] Decentralizing accountability and mindsets when dealing with data [28:50 - 32:40] How to make the switch to prioritizing decentralizing accountability over who writes SQL queries, and KAWA's role [32:41 - 35:15] Centralized policy frameworks and definitions [35:22 - 39:05] Thoughts on self-service operations and the centralization of analytics [39:06 - 40:52] Critical business logic and Excel spreadsheets [40:53 - 42:06] Centralizing business logic vs decentralizing business logic [42:06 - 44:33] Avoiding business logic being owned by one individual, and looking at decision models [44:33 - 45:45] Critical documents, and the idea of a Google Drive for data [45:49 - 52:41] Lightning round [52:42 - 57:26] Takeaways [57:44 - 01:00:16] Three questions

This week, we are having a throwback elixir with some very special surprise guests. Tune in with hosts Juan and Tim to find out who. Key Takeaways: [00:26 - 01:51] Episode intro: Data Day and What keeps you up at night in data? [02:04 - 02:54] Joe Reis, the economy and showing value [02:59 - 03:30] Omar Khawaja: Anything that ends with board, data dashboards [03:54 - 04:05] Vip Parmar: Jet lag & underutilized data [04:16 - 04:51] Laura Ellis: Data governance and change management [04:59 - 06:18] Mohammed Syed: Change management and data solutions [06:22 - 06:49] Tim: Determining focus in a sea of technology opportunities [06:50 - 07:58] Juan: Disconnect between executives and how data teams work with the business

Increasing adoption of data products, by design Look no further than Brian T. O’Neill’s bio to tell you that’s what he does best. Our special guest this week knows that low adoption of data products are enterprises biggest enemy. Not just in terms of quantity, but also quality of these investments. Why are teams so often creating technically right, effectively wrong data products? Why do people fail to adopt when it’s them post crucial part of becoming data driven organizations? These burning questions have answers. Join hosts Juan, Tim, and guest Brian T. O’Neill on this weeks episode of Catalog & Cocktails. Key takeaways: [00:06 - 03:30] Intro & Cheers [03:33 - 04:54] What is your instrument of choice and what would your band name be? [04:59 - 07:55] Honest no BS definition of a data product [07:59 - 11:17] Alternative definitions of data as products [11:18 - 14:11] Data products can be many things, and definitions are broad [14:11 - 19:39] How human centered design interplays with defining data products [19:47 - 25:17] Data therapists, knowledge scientists and engineers [25:21 - 33:48] Where does the burden lay, and with whom [33:49 - 38:35] Brian's perspective on data product management versus software product management [38:38 - 43:59] How do you achieve good adoption? [44:05 - 46:39] What is the best way people can start learning about human-centered design? [46:42 - 47:20] Can dashboards be data products, and can machine learning be data products? [47:32 - 49:45] Should companies be investing in data managers [49:45 - 52:45] Value and adoption, should companies track data ROI [52:58 - 58:20] Takeaways [58:23 - 01:02:09] Three questions