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

AI: No one wants your models. w/ Andrew Eye

You can’t buy a predictive model off the shelf. And if you COULD… would you? It’s the old “build versus buy” debate and with data in a constant state of change, building and deploying AI is more challenging than ever.Join Tim, Juan, and Andrew Eye, CEO at ClosedLoop.ai, to discuss the challenges of AI deployment, AIOps, and maintaining models as data changes.Key Takeaways[00:08 - 01:45] Cheers for AI[01:48 - 04:44] Taking advantage of incoming data and considering the data footprint[04:45 - 05:55] Building the most accurate and useful model, no best solution[06:01 - 09:26] AI strategy and developing prediction models[09:33 - 15:17] Should you build or should you buy?[15:21 - 20:02] Verticalized AI versus Horizontal data science and AI-oriented solutions[20:04 - 23:16] Build a custom predictive model[23:42 - 25:24] The concept of "feature drift" in AI[25:26 - 30:02] Vertical AI and the opportunity with healthcare[30:07 - 33:52] The concept of a click and the idea of semantic contracts[33:53 - 38:08] Great minds designing AI for Facebook instead of healthcare[38:12 - 39:41] Designing AI for good is addictive[39:47 - 41:27] The next generation and the promise of AI that can really impact lives[41:41 - 47:57] Lightning Round[48:02 - 52:03] Takeaways[52:05 - 53:24] Three questions

Takeaways with Andrew Eye

Knowledge: the missing piece to understanding your business

The most effective way to understand your business is the special balance between things like tribal knowledge and extracted knowledge, technical teams and business teams, confidence and skepticism.Join Tim, Juan, and Loris Marini, CEO of Discovering Data, as they zoom in to find that balance and the humility it may take.Key Takeaways[00:11 - 03:10] Introduction & Toasts[03:13 - 05:32] What's the weirdest place you've ever misplaced your keys?[05:44 - 08:01] The most effective way to follow the business is to follow the money[08:02 - 10:35] Thinking about business literacy and how to learn that perspective[10:37 - 12:35] How do we make change happen?[12:37 - 13:39] Having genuine conversations with people[13:44 - 18:19] Coaching, psychological safety, and communicating better at work[18:20 - 22:10] The definition of creating knowledge within an organization[22:11 - 25:34] Knowledge, insight, and levels of knowledge distributed in your team[25:35 - 28:52] You've got to spend time and you have to have a platform[29:10 - 31:48] We can do better than "just the smartest will survive"[31:57 - 35:56] Incentives are about subtle things like a team that listens, not just pay and flexible time[35:59 - 39:35] A data therapist and becoming more aware of issues[39:43 - 41:32] Advice to engineers who write the data pipeline, engineer the SQL queries[42:06 - 45:24] Lightning Round[45:28 - 51:10] Takeaways[52:06 - 53:57] Three questions

Takeaways with Loris Marini

Data empathy; you either got it or you don’t w/ Laura Ellis from Rapid7

Data needs to be in the hands of the people who really need it. Sounds simple, right? So where is the disconnect? If your teams aren’t partnering to get projects done, it’s often due to a lack of understanding of each others pain points. That’s where empathy is a must. Join Tim, Juan and special guest Laura Ellis from Rapid7 as they discuss supporting all the cooks in the kitchen when it comes to data projects, why that’s a hill to die on and HOW to actually get that done. Key Takeaways: [00:06 - 02:09] Intros & Toasts[02:09 - 03:49] What is the most controversial hill you'll die on?[03:52 - 05:30] Getting data into the hands of people who need it[05:30 - 07:01] Biggest problems for collaborating and making data easier to work with across the organization[07:05 - 09:32] Take a business problem, break it into a data problem[09:32 - 11:00] Breaking down problems, making it less intimidating[11:00 - 12:34] A user experience problem around data[12:30 - 13:52] Who is responsible for figuring out how the pieces of the puzzle fit together[13:55 - 17:22] Understanding centralization, decentralization, and embracing ownership[17:25 - 20:48] Identifying issues and opportunities research provides for enabling data access[20:48 - 22:31] Internal user research and Rapid7's "data therapist"[22:31 - 24:33] Business literacy and data literacy, cross-functional learning[24:28 - 26:39] More technology and more tools[26:37 - 33:14] Putting data into the hands of people who need it, making money and saving money[33:20 - 36:36] Gathering community and team content from passionate people[36:34 - 38:24] Brainstorming definitions of success[38:24 - 41:31] Start talking to people, do surveys, understanding the monetary impact[41:39 - 43:31] Dive into the details[43:36 - 47:23] Lightning Round[47:27 - 54:09] Takeaways[54:10 - 57:57] Three questions

Takeaways with Laura Ellis from Rapid7

AI/ML: where should the focus be? w/ Patrick Bangert of Samsung

AI and ML are both at the center of many applications today from autonomous vehicles to healthcare. But where is this ship heading?Join Tim, Juan, and Patrick Bangert, VP of AI at Samsung SDS where they will discuss where the focus of AI is today and where it should be tomorrow.Key Takeaways[00:04 - 01:06] Episode introduction/trailer[01:18 - 02:22] Drinks & Toasts[02:23 - 04:40] What is the most unfocused situation you've ever been in?[04:49 - 10:08] Where is the focus of AI today? Business application.[10:09 - 13:10] The technical side of AI, thinking about AI behind the wheel[13:43 - 15:15] The problems outside autonomous driving that AI can solve[15:16 - 20:09] AI and the impact on healthcare[20:15 - 21:35] AI will allow doctors to become better caregivers[21:45 - 27:08] Open issues and gaps in technology with AI currently[27:13 - 30:41] Keeping data clean, and identifying bias and context[30:46 - 36:18] Effective strategies companies are taking to label data better[36:37 - 38:56] Using AI to help build AI, "AI squared"[38:58 - 43:25] Inserting knowledge and representation, a solvable problem with better data[43:25 - 46:23] Chat bots, autonomous driving, and complexity of tasks[46:43 - 48:36] Doug Lennick, lessons from the 80s, and the state of GPT-3[48:47 - 52:16] The role of venture capital in AI development[52:31 - 57:34] Lightning round[57:40 - 01:05:58] Takeaways and the current state of AI[01:06:06 - 01:08:03] Three questions

Takeaways with Patrick Bangert, VP of AI at Samsung

Struggles of Setting up a Data Governance Program w/ Rupal Sumaria

You have been asked to start the Data Governance program in your organization. Sounds easy, right? How do you start? How do you define success? Who needs to be part of the team?Join Tim, Juan and Rupal Sumaria, Head of Governance of Penguin Random House UK to discuss the steps for a successful data governance program and what to avoid.Key Takeaways[00:06 - 03:16] Introduction and Toasts[03:17 - 04:51] If you could only keep three apps on your phone, what would they be?[04:52 - 07:33] Struggles in setting up a data governance program[07:40 - 09:42] Rupal's presentation at the data.world summit[09:46 - 13:03] Networking and communicating early while creating the data governance program[13:07 - 15:06] Who Rupal was collaborating with while setting up the data governance program[15:06 - 17:34] When was the last time you spoke to sales and marketing?[17:34 - 19:32] A tailored approach to reaching other departments[19:32 - 22:56] Tips for when you're struggling with engagement[22:56 - 26:42] Change the game, don't make it boring[26:48 - 32:14] Companies have to measure ROI[34:14 - 36:54] A four step process to data governance[36:54 - 39:10] Where to start, departmentally[39:13 - 40:28] Advice for those "stuck" in data governance roles[40:28 - 41:34] Is there anything Rupal would have done differently, two years down the line?[41:51 - 42:58] Data governance technology[43:24 - 45:41] Rupals tip's for navigating processing technology tools[45:43 - 48:59] Lightning round[49:02 - 56:51] Takeaways[57:03 - 59:25] Three questions

Takeaways with Rupal Sumaria