Episodi
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Saul Alinsky's book Rules for Radicals held significant lessons for grassroots movements and political activism when it was published in 1971. It also inspired the title and theme of this podcast. In his book, Saul outlines approaches for unifying people and motivating them to work toward a common goal. Today, these same strategies can be used for data culture change management, triggering transformative action within organizations.
Saul was an American activist and political theorist who lived from 1909 to 1972. His work organized impoverished communities and gave them tools to drive social change, and won him national attention and notability. In the final episode of the season, Satyen sits down with a ChatGPT-infused version of Saul to discuss the role of data in driving social change, the application of community organization tactics in corporate settings, and the key principles from Rules for Radicals.
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âOn the global stage, data analytics and humanitarian efforts is akin to having a crystal ball. It's not about predicting the future, but about making informed, timely decisions that can save lives. The possibilities are indeed limitless. With data and analytics, we're not just changing the game. We're rewriting the rules and designing a better playbook for humanity.â â Saul GP Talinsky
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Episode Timestamps:
*(03:57): Bridging community organizing with data science
*(12:46): Empowering teams: Upskilling organizational agility
*(14:35): Democratizing data and crafting a culture of insight and empowerment
*(17:17): The timeless role of data in driving social change
*(20:54): How to unlock societal transformation with data and analytics
*(26:26): The evolution of radical change
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
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* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Read Rules for Radicals
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The rapid progress in AI technology has fueled the evolution of new tools and platforms. One such tool is a vector search. If the function of AI is to reason and think, the key to achieving this is not just in processing data, but also in understanding the relationships among data. Vector databases provide AI systems with the ability to explore these relationships, draw similarities, and make logical conclusions. Understanding and harnessing the power of vector databases will have a transformative impact on the future of AI.
Edo Liberty is optimistic about the future where knowledge can be accessed at any time. Edo is the CEO and Founder of Pinecone, the managed database for large-scale vector search. Previously, he was a Director of Research at AWS and Head of Amazon AI Labs, where he built groundbreaking machine learning algorithms, systems, and services. He also served as Yahoo's Senior Research Director and led the research lab building horizontal ML platforms and improving applications. Satyen and Edo give a crash course on vector databases: what they are, who needs them, how they will evolve, and what role AI plays.
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âWe as a community need to learn how to reason and think. We need to teach our machines how to reason and think and talk and read. This is the intelligence and we need to teach them how to know and remember and recall relevant stuff. Which is the capacity of knowing and remembering. The question is, what does it mean to know something? To know something is to be able to digest it, somehow to make the connections. When I ask you something about it, to figure out, âOh, what's relevant? And I know how to bring the right information to bear so that I can reason about it.â This ping pong between reasoning and retrieving the right knowledge is what we need to get good at.â â Edo Liberty
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Time Stamps
*(03:13): How vector databases revolutionize AI
*(14:13): Transforming the digital landscape with semantic search and LLM integration
*(28:10): Exploring AIâs black box: The challenge of understanding complex systems
*(37:02): Striking a balance between AI innovation and thoughtful regulation
*(40:01): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Connect with Edo on LinkedIn
Watch Edoâs TED Talk
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Episodi mancanti?
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Folks in the data space are familiar with the concept of data literacy. However, a new idea is on the rise: business literacy. Whether folks sit in product, marketing, or commercial, there needs to be a productive balance between understanding business context and technical expertise of each department. This shared comprehension means ideas are more likely to be deployed and productionalized because everyone has deeper domain knowledge and business understanding.
Sanjeevan Bala is making business literacy a top priority at his organization. He is the Group Chief Data and AI Officer at ITV, an Alation customer. There, he is responsible for driving the digital data and AI transformation and leading an offensive growth strategy that enhances how they produce, promote, distribute, and monetize content. Sanjeevan is an international thought leader, has won numerous awards for his work, and was named the most influential person in data by DataIQ. Satyen and Sanjeevan discuss the idea of a Data Product Manager, the importance of business literacy, and the power of experimentation.
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âI think because we went down the data as a product notion, that leadership role was a Data Product Manager. Incorporate product thinking in the way in which data is developed, designed, and used. I think what's beautiful about product thinking is it's very well adapted and equipped for understanding competing objectives and competing needs. Creating methods by which you're trying to either align or prioritize those needs. But, critically allows you to prioritize around the right things because you're constantly looking at how do you make sure you can productionize and scale and realize the full value? What does it take to do that? That goes way beyond what you're doing in data. That gets into organizational change, that gets into last mile technologies that you may not have thought about.â â Sanjeevan Bala
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Time Stamps
*(05:16): How to define your organizational identity
*(07:26): The art of storytelling and data-driven leadership
*(18:20): Harnessing experimentation to drive organizational change
*(25:10): Data literacy versus business literacy
*(42:17): Balancing innovation and regulation in AI
*(44:41): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile: https://www.linkedin.com/in/ssangani/
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Links
Connect with Sanjeevan on LinkedIn
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Whether you work in retail, healthcare, or CPG, data analytics is key to making your business stand out. Youâre able to find new sources of data, synthesize them, and then work with business folks to get better and better insights. Even with all of the advantages analytics offers us, sometimes thereâs hesitancy to invest in data. In sports, itâs the exact opposite. The use of data is felt immediately in game wins, player selection, and gate revenue.
Known as âThe Real Moneyball guy,â Ari Kaplan has revolutionized sports through analytics and is a leading influencer in the area, as well as in AI and data. He helped create analytics departments for the Chicago Cubs, Los Angeles Dodgers, and Baltimore Orioles. Ari is now Head of Evangelism at Databricks where his team helped the Texas Rangers clinch their first World Series title. Satyen and Ari discuss data analytics in sports, how data intelligence platforms are shifting the landscape, and the concept of generation AI.
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âEven if we change nothing else, to be able to make better predictions of player development, finding what skills are better in the draft, predicting injuries and so on, that's part of the competitive advantages. How can we ingest this data? It's a ton of data. Terabytes of data every game, multiply that by dozens and dozens of teams at all levels around the world. Right now, teams are struggling to store it, process it on a daily basis. Teams that could do that faster will be an advantage. For listeners, if you're not in the baseball world, same idea. If you're in retail, CPG, healthcare, it's finding new sources of data, proprietary, nonproprietary. How could you synthesize it? Then, how can you start working with the business people to get better and better and better insights?â â Ari Kaplan
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Time Stamps:
*(03:00): The birth of Moneyball
*(10:11): How the Texas Rangers hit a data home run
*(15:49): The next evolution: data intelligence
*(27:17): Partnering for success in the ecosystem
*(38:54): The role of AI in building the future
*(41:29): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Learn more about Raoul Wallenbergâs fate
Connect with Ari on LinkedIn
Follow Ari on X
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When it comes to our relationship with technology, be like philosopher Friedrich Nietzsche and practice mindfulness. We usually think mindfulness means setting boundaries like screen time limits. However, we should think about the goals and values we want from technology, like greater human connection, improving efficiency, or driving knowledge. This introspective thinking enables us to be intentional about how and why weâre using technology. Without mindfulness, instead of you driving the tech, the tech may be driving you.
Nate Anderson lives by and continues to share Nietzscheâs philosophies today. Nate is the Deputy Editor at Ars Technica, where he covers technology law, politics, and culture. He combined his high-tech background with a love of writing to freelance at publications like The Economist and Foreign Policy. Nate is also the author of In Emergency, Break Glass: What Nietzsche Can Teach Us About Joyful Living in a Tech-Saturated World. Satyen and Nate discuss forming positive connections with technology, saying âyesâ to life, and what Nietzsche would have to say about tech.
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âConnection to other people is important. We use technology to create that connection. That might mean a Friday night game group over Zoom or Twitch or multiplayer with your friends. As long as you have the goal in mind, that's where it requires your creativity. That's where you're using the tools creatively to produce outcomes that you want in life. The problem with not thinking in a goal-directed way is that technology itself is not completely neutral. Technology has no goals of its own. It was created by people and companies who have plenty of goals and some of those don't necessarily take you to places where you would choose to go. That's why if you don't have a goal-driven approach to technology, you may find technology is actually driving you.â â Nate Anderson
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Time Stamps:
*(04:25): Why Nietzsche? Why now?
*(15:07): Offer agency, not just prescriptive rules
*(24:17): The loneliness of technology
*(27:51): Seeking that goal-driven place
*(35:44): Producing actual value
*(38:35): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Read In Emergency, Break Glass
Connect with Nate on LinkedIn
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Thanks to GenAI, we have an overabundance of tools, models, and capabilities. However, the use and impact of these advancements is yet to be known. Thatâs why in the age of technological innovation, traditional skills like fact-checking are more important than ever to ensure that the technology and predictions are correct.
Guy Scriven, U.S. Technology Editor at The Economist, is on the frontlines of the AI explosion. In his tenure at the publication, he has served as a researcher and climate risk correspondent, and has grown his affinity for telling data-driven stories. Satyen and Guy discuss the role of data in journalism, instilling a culture of debate, and the unsexy â but critical â side of AI.
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âWe've had this long period of experimentation and excitement. That's been basically marked by the supply side of AI just really ramping up. You've had loads of model makers releasing new models. You've had the cloud players buying enormous amounts of specialized AI chips. You've had thousands of AI application startups who are going to build on top of the model makers, who then use the AI chips from the cloud providers. You've had this boom in the supply side of AI. Now, the big question is whether the enterprise demand meets that and what shape it takes. I think we don't really have a good sense of that until at least the first couple of quarters of next year.â â Guy Scriven
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Time Stamps:
*(02:22): Less reporting, more commentary
*(13:32): Dataset discovery
*(22:34): ChatGPTâs hallucination problem
*(34:38): AI headlines on the rise
*(41:48): Whatâs the next big AI story?
*(46:10): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Connect with Guy on LinkedIn
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The best kind of data radical is one who knows how to balance their technical expertise with their fuzzy side. Skills like storytelling, empathy, and ethics are becoming invaluable in the tech space. The ability to balance both enables data folks to recognize patterns where others might miss them. This type of integrative thinking can guide people on their next investment, whether theyâre investing time, money, or resources.
Scott Hartley is a global early-stage investor and author of The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World. His passion lies in emerging markets and big ideas that improve lives, particularly in financial services, health, supply chain, and logistics. Scott has served as a Presidential Innovation Fellow at the White House and has co-founded two venture capital firms: Everywhere Ventures and Two Culture Capital. Satyen and Scott discuss the techie and fuzzy sides of Silicon Valley, the advancement of tech, and how Scott chooses his next investment.
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âI love this thought around data collection and big data is one thing, it's collecting information. But, then turning that information into knowledge and into wisdom. In one part, can be done through unstructured to structured data, through things like LLMs that are enabling us to move out of the information noise into a bit more knowledge noise, and then maybe into wisdom specificity. I still think that there's a leap there that's going to be human-driven. Whether it's a person sitting there interpreting or it's a team of engineers thinking about the sensitivities, the data tagging. There are human decisions in the mix somewhere along that chain, as we're taking on structured data and turning it into structured knowledge and wisdom. All these things to say, that even these deeply technical infrastructure-level technologies, have elements of humanity in them.â â Scott Hartley
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Time Stamps:
*(10:55): The genesis behind The Fuzzy and the Techie
*(18:11): Subjectivity, structure, and bias
*(20:17): Scottâs investment focus
*(30:09): The âtables-stakes economyâ
*(38:11): AI and public policy
*(47:43): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Read The Fuzzy and the Techie
Visit Scottâs website
Connect with Scott on LinkedIn
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Precision in technology is powerful. When it comes to services like Uber, people know the exact location of the driver and how much the trip will cost. Precision helps banks lend money to folks with bad credit, but who took the initiative of telling a bank when they would miss a payment. Precision can even help deliver urgent medical supplies via drones in countries that need it most. Precision in technology means users have total visibility on location, price, and competitors, and theyâre able to achieve better outcomes.
Maddy Want is the VP of Data for Betting and Gaming at Fanatics. Maddy has over a decade of data product experience spanning diverse web and app services, and has served companies like Audible, upday, and Index Exchange. When Maddy joined Fanatics, she was responsible for creating the data strategy, hiring the data team, and partnering with tech. Satyen and Maddy discuss her new book, Precisely, data governance, and why precision matters.
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âWe've gone to total visibility on location, total visibility on price, and ability to shop across competitors. To me, the big theme out of all of those things is it's not about the technology itself, it's not about drones, or it's not about auction mechanics like that power Uber. Those things are cool, but it's about the capability that it's given to the customers, or the patients, or whoever. The theme there is that they have more precision. They can be more precise about what kind of change they're requesting or they're affecting, and they can have an outcome that's much more tailored to them.â â Maddy Want
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Time Stamps:
*(05:45): The disconnect between public policy and tech
*(13:09): The focus on precision
*(20:18): Writing Precisely
*(29:50): Maddyâs role at Fanatics
*(39:27): Structuring the team
*(47:19): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Read Precisely: Working with Precision Systems in a World of Data
Connect with Maddy on LinkedIn
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With the rise of GenAI, LLMs are now accessible to everyone. They start with a very easy learning curve that grows more complicated the deeper you go. But, not all models are created equal. Itâs critical to design effective prompts so users stay focused and have context that will drive how productive the model is.
In this episode, Matthew Lynley, Founding Writer of Supervised, delivers a crash course on LLMs. From the basics of what they are, to vector databases, to trends in the market, youâll learn everything about LLMs that youâve always wanted to know. Matthew has spent the last decade reporting on the tech industry at publications like Business Insider, The Wall Street Journal, BuzzFeed News, and TechCrunch. He founded the AI newsletter, Supervised, with the goal of helping readers understand the implications of new technologies and the team building it. Satyen and Matt discuss the inspiration behind Supervised, LLMs, and the rivalry between Databricks and Snowflake.
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âThis idea of, âHow does an LLM work?â I think, the second you touch one for the first time, you get it right away. Now, there's an enormous level of intricacy and complication once you go a single step deeper, which is the differences between the LLMs. How do you think about crafting the right prompt? Knowing that they can go off the rails really fast if you're not careful, and the whole network of tools that are associated on top of it. But, when you think from an education perspective, the education really only starts when you are talking to people that are like, âThis is really cool. I've tried it, it's awesome. Itâs cool as hell. But how can I use it to improve my business?â Then it starts to get complicated. Then you have to start understanding how expensive is OpenAI? How do you integrate it? Do I go closed source or open source? The learning curve starts off very, very, very easy because you can get it right away. Then, it quickly becomes one of the hardest possible products to understand once you start trying to dig into it.â â Matthew Lynley
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Time Stamps:
*(04:21): The genesis of Supervised
*(11:34): The LLM learning curve
*(21:35): Time to build a vector database?
*(31:55): Open source vs. proprietary LLMs
*(41:35): Snowflake/Databricks overlap
*(47:47): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Read Supervised
Connect with Matthew on LinkedIn
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The art of medicine happens when physicians combine data and knowledge to deliver better patient outcomes. A physician that relies both on guidelines and their learned experience is creating a culture of data and insights and improving the lives of patients. Whether youâre a doctor or a data leader, knowing how to balance data and intuition will always drive better results.
Dr. Bapu Jena is an economist, physician, and Joseph P. Newhouse Professor of Health Care Policy at Harvard Medical School. He bridges his professions to explore the economics of healthcare productivity and medical innovation. Satyen and Bapu discuss leveraging data in healthcare, applying AI in medicine, and measuring the innovation of doctors.
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âWe have put a premium on the innovativeness of the technology. There could be a new molecule that attacks a pathway that has never been attacked before. If that molecule doesn't improve life expectancy or improve quality of life, then there's not a lot of value to me in that innovation, even though it's certainly innovative. I care more about whether or not it impacts patients' lives. The correlator to that is that you could have a medication which does not appear to be that quote, unquote, âinnovative,â at all because it's just a reboot, in some respect, of other medications. But, it's taken in a way that people are more likely to be adherent to. Those types of technologies are sometimes pooh-poohed on, but they could be very valuable because what ultimately matters is the outcome of whether or not a person gets better when they're on that medication, not how innovative it is. This is also a problem when it comes to data-driven interventions, as well. Because, there's a lot of interest in AI and non-medical technologies, or non-life science technologies. The key there is you've got to demonstrate that there's some outcome benefit.â â Bapu Jena
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Time Stamps:
*(03:23): Predictable randomness
*(12:13): Data points tracking intensity of care
*(25:48): AI in medicine
*(31:29): The politics of standards of care
*(38:41): The challenges of influencing change
*(51:18): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Read Bapuâs book Random Acts of Medicine
Random Acts of Medicine Substack
Listen to Freakonomics MD podcast
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Starting a revolution is no easy task. Just ask Dr. Michael Stonebraker and Andy Palmer, co-founders of Tamr, the enterprise data mastering company. Their path to innovation begins with a universal problem. They also collaborate with other data radicals who challenge them to think differently and help them grow.
Michael is a database pioneer, MIT professor, and entrepreneur. He has founded nine database startups over 40 years and won the A.M. Turing Award in 2014. Andy is a serial entrepreneur and founder, board member, and advisor for over 50 start-ups. Satyen, Michael, and Andy discuss Tamrâs tech evolution, third normal form, and probabilistic methods.
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âThere's a lot of work to be done in these big enterprises of getting all the data cataloged, getting it all mastered and curated, and then delivering it out for lots of people to consume. Early on at Tamr, we did a lot of stuff on-premise and those projects just took so much longer and you ended up doing a whole bunch of infrastructure stuff that's just not required. Weâre really encouraging all of our customers to think cloud native, multi-tenant infrastructure as the de facto starting point because that'll let them get to better outcomes much faster.â â Andy Palmer
âData products and data mastering are basically a cloud problem. And so you want to be cloud native, you want to run software as a service, you want to be friendly to the cloud vendors. Tamr spent a lot of time over the last two or three years doing exactly that. There's a big difference between running on the cloud and being cloud native and running software as a service. That's what we're focused on big time right now. After that, I think there's a lot of research directions we're paying attention to. Trying to build more semantics into tables to be able to leverage. You can think of this as leveraging more exhaustive catalogs to do our stuff better. I think that's something we're thinking about a bunch.â â Dr. Michael Stonebraker
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Timestamps:
*(04:47): The procurement proliferation
*(15:51): Solving data chaos
*(24:49): Probabilistically solving data problems
*(37:34): The future of Tamr
*(43:16): A great technologist versus a great entrepreneur
*(44:51): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Connect with Andy on LinkedIn
Connect with Michael on LinkedIn
Learn more about DBOS
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Over the last two seasons of Data Radicals, weâve seen that data experts have been promoted to leadership roles. Itâs proof that organizations are seeing the value of data and the significance of establishing a data culture.
In this episode, youâll hear from past guests like Stan McChrystal, Tricia Wang, and Paul Leonardi as they discuss traits of a successful data leader, adapting your data strategies, and the importance of soft skills.
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âI found that if I told somebody to do a task, they might try to do that task. But if I say, âCreate this effect,â they owned it because they felt a level of responsibility for what approach that they chose, and it made it much stickier.â â Stan McChrystal, Retired US Army General
âI think having gone through the valley of suffering myself, I have a massive amount of respect for founders because they carry a weight that most people will never realize. So it's hard for me not to like them.â â Jepson Taylor, Chief AI Strategist at Dataiku
âThose CDOs that are most successful quickly establish trust within business, with business sponsors. They work with the business sponsors to identify what are the one or two or three most important things to them and see if they can solve those questions, even if itâs with a very small subset of data, to begin to develop that relationship, that trust.â â Randy Bean, Author of Fail Fast, Learn Faster
âYou have to be able to have a learnerâs mindset. You have to understand what different teams and functions do and how they play into a bigger picture so that you can get into cause and effect. And then when you start to do that, you have a lot more ability to actually have impact.â â Wendy Turner-Williams, CDO at Tableau
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Time Stamps:
*(00:48): Randy Bean: Alignment with expectations
*(02:39): Jennifer Belissent: The diplomatic CDO
*(05:01): Taylor Brown: Lead by example
*(05:44): Ashish Thusoo: The DNA of a CDO
*(07:48): Stan McChrystal: The strength of humility
*(15:40): Paul Leonardi: Collaboration, computation, and change
*(17:50): Mike Capone: Tapping your network
*(18:39): Tricia Wang: The other vital âCâsâ
*(19:41): Bernard Liautaud: Setting your North
*(21:03): Jepson Taylor: Heroism and the human touch
*(22:45): Wendy Turner-Williams: Leading future leaders
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Listen to Randy Beanâs episode
Listen to Jennifer Belissentâs episode
Listen to Taylor Brownâs episode
Listen to Ashish Thusooâs episode
Listen to Stan McChrystalâs episode
Listen to Paul Leonardiâs episode
Listen to Mike Caponeâs episode
Listen to Tricia Wangâs episode
Listen to Bernard Liautaudâs episode
Listen to Jepson Taylorâs episode
Listen to Wendy Turner-Williamsâs episode
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How does the NBA use data to compete and improve? When it comes to driving business growth with data, transparent communication makes success a slam dunk. By sharing innovative ideas and best practices across the business, one all-star team elevates the success of others across the entire organization.
Michael James, SVP and Head of Data Strategy and Analytics at the NBA, is committed to creating a better fan experience and making better business decisions through collaboration. In this role, he bridges executive leadership and technical expertise to create a data-driven culture in constant pursuit of innovation. Satyen and Mike discuss the NBAâs digital transformation, the future of GenAI in the league, and attracting more people to sports business analytics.
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âWe have very active communication with our teams. You build up a relationship over time and you start to realize, âIf this person is sharing this thing that worked, we have a good sense of who else might be able to benefit from it.â We'll make sure to package that up in a way that is not only informative, âHere's what the team did,â but also has the tangible next steps. âHere's what you can actually do with this to drive the business.â And it's no different on the data side. We've built a ton of data products through the years at the league level for our teams, also for different departments within our league office as well. But, the goal of all of those products is to make sure that we are driving better business decisions, we're driving a better fan experience, and, ultimately, that's going to lead to more revenue.â â Michael James
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Time Stamps:
*(13:16): Sharing the (data) ball among the league
*(17:34): Establishing best practices across an enterprise
*(37:08): Measuring performance to measure culture
*(41:16): Improving DEI in data
*(46:57): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile: https://www.linkedin.com/in/ssangani/
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Links
Follow Michael on LinkedIn
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As data radicals, we want to deliver insights that enable our organizations to know more, which we often do by providing an answer. Instead, we should be thinking about frameworks we can implement to communicate our ideas in simple and consumable ways.
Dave Kellogg knows the importance of a framework. Dave is one of the leading enterprise executives in software today and currently serves as the Executive in Residence at Balderton Capital. His blog, Kellblog, is a highly regarded content hub for software leaders, drawing on his experience as an angel investor, board member, advisor, and thought leader. Satyen and Dave discuss the evolution of the data industry, problem solving with frameworks, and mapping your business in a complicated world.
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âPeople need the world simplified for them, and if you don't do it, somebody else will. A confused buyer is just going to buy from the market leader. If you're running a startup, you're definitionally not that. The burden of simplicity is on you. If you want to be successful, you need to have a very simple explanation of why someone should buy your stuff.â â Dave Kellogg
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Time Stamps:
*(03:58): The evolution of BI and the BI customer
*(22:25): Solving complex problems with simplifying frameworks
*(32:41): Defining âdata intelligenceâ
*(43:16): The DNA of a D&A career
*(47:44): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile: https://www.linkedin.com/in/ssangani/
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Links
Follow Dave on LinkedIn
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Read Daveâs blog
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If youâre on a journey of fitness, you know that perfectionism is your enemy. The same goes for data. There will always be another achievement that you wish to reach. Instead, focus on creating habits that will lead you to better data decisions and long term health.
Ameen Kazerouni knows this journey well. Ameen has spent his career at the intersection of science, data, and technology to create intuitive, data-driven experiences. In his role as CTO of Orangetheory Fitness, he is driving consumer wellness journeys by turning workout data into feedback and personalized recommendations. In this episode, Satyen and Ameen discuss data-driven exercise, keeping humans in the feedback loop, and AI data governance.
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âWe make a lot of investment in demystifying the Orangetheory workout. And there's a lot of parallels to data, and I love that because when you think about data in an organization, âWell, it's going to be a multimillion dollar investment.â And it can get so overwhelming that instead of being like, âLet's start piece by piece,â the instinct becomes, âLet's just keep guessing instead.â Which is never a good idea. You should never, âLet's just revert to not using data at all because it's going to be really difficult to use data perfectly.â It's the same thing with fitness. You don't revert to doing nothing at all because meeting all the requirements will be hard. Showing up and getting started is what gets you going.â â Ameen Kazerouni
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Time Stamps:
*(04:42): AI in the workout
*(13:56): Data as a habit
*(25:45): AI data governance
*(38:36): The future of connected health
*(44:08): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Follow Ameen on LinkedIn
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Weâve all heard the term âdata literacyâ by now. Although there is general consensus regarding the importance of knowing how to read, write, and communicate with data, some folks may take issue with the term itself. Wendy Batchelder, CDO at Salesforce, wants to reframe the conversation and focus on how people can leverage data to do their jobs better.
Wendy is a technology executive who has spent her career tackling business problems with technical solutions and transforming diverse team members into leaders. In her current role at Salesforce, Wendy is helping the right people access the right data at the right time â with the right controls. In this episode, Satyen and Wendy discuss the problems with data literacy training, the power of answering âso what?â questions, and the value of advocating for DEI in tech.
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âYou have to drop the jargon and get down to what are you trying to explain? If you're trying to help people to use more data for decision making, then just introduce the data. Don't sit down and say, âWe're gonna talk about data literacy,â because everyone's eyes gloss over and you lose their interest and their attention. It just doesn't give you a lot of respect. Part of our job as data experts is to help people to use data better and that's the conversation that should be had. But, the second you say things like data literacy, the tone of the conversation totally shifts.â â Wendy Batchelder
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Time Stamps:
*(06:41): Data strategy at Salesforce
*(26:37): Keeping up with connectivity
*(28:40): Data literacy denial
*(35:22): DE&I in data
*(47:18): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile: https://www.linkedin.com/in/ssangani/
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Links
Follow Wendy on LinkedIn
Read Wendyâs book Data Governance Handbook
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Before search engines, we had to rely on memory and investigation to answer our questions. Then, search engines made answers instantly available. Now, in the age of AI, we have to engineer our questions to get the best results.
Frank Farrall, Deloitte's Strategy & Analytics Ecosystems and Alliances Leader, knows that asking the right questions is just as critical as knowing the answers. Frank is a global business builder with 20 years of experience helping clients and startups become billion-dollar businesses through AI and digital transformation.
In this episode, Satyen and Frank discuss identifying worthy investments, the sexiness of prompt engineering, and efficient engagement with AI.
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âI think in a lot of cases, prompt engineering will at least become a skill that knowledge workers, creative workers use to get an outcome from the technology. I think some people will be highly, highly specialized. I actually think prompts are going to have value in organizations and I think prompt libraries and how you manage prompts will become a set of IP and something that's highly valuable inside organizations. I think prompt engineering has a very significant future ahead of it. I think all of us are going to have to learn some level of prompt engineering to be effective in the future.â â Frank Farrall
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Time Stamps:
*(02:42): Defining the AI ecosystem
*(07:55): How to identify a worthy investment
*(23:23): How âsexyâ is prompt engineering?
*(41:44): The future of generative AI
*(44:43): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Follow Frank on LinkedIn
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As folks in the data space, weâre introverts by nature. But, getting out of our comfort zone can open you up to endless possibilities. As one person whoâs gone from CIO to CEO can tell you, the key to growth is getting comfortable with feeling uncomfortable.
That person is Mike Capone, CEO of Qlik, where heâs revolutionizing the business intelligence landscape through data. In this episode, Mike shares with Satyen how his decades of experience in product development, data science, and go-to-market operations influence his role as CEO today. Satyen and Mike discuss transitioning from CIO to CEO, navigating economic downturns, and stepping out of your comfort zone.
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âNow is the time to get closer to your best customers. They're the ones who sustain you through these periods of economic ups and downs. The reality is for both of us and both of our companies, companies need data and analytics now more than ever. How are you going to navigate this uncertainty? You're going to navigate it through data. The conversation like, âHey, we don't want to spend any more money on data and analytics because the environment is tough right now,â is actually counterintuitive. The reality is you need data and you need real-time data to get through it because your old data models are useless.â â Mike Capone
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Time Stamps:
*(02:51): The growth of Qlik
*(08:37): The relationship between private equity and software
*(20:00): From CIO to CEO
*(27:29): Navigating rough economic times with data and analytics
*(33:10): Maintaining long-term landscape leadership
*(40:24): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
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Links
Follow Mike on LinkedIn
Follow Mike on Twitter
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As human beings, weâre not accustomed to talking about data. In order to learn about new subjects, we traditionally use stories. However, bridging the gap between data and stories allows us to cross that barrier and create data-driven organizations.
In this episode, Satyen interviews Ashish Thusoo, GM of AI and ML at AWS. Previously, Ashish was the Founder and CEO of Qubole, a pioneering cloud data lake platform. He also served Facebook as the Engineering Manager of Data Infrastructure where he co-created Apache Hive with the aim to democratize data access and analytics. Satyen and Ashish discuss the accelerated push to the cloud, building a data culture, and how the economic climate is impacting customers.
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âYou have to remember, human beings are trained from the get-go to talk about stories, not data. That's how we learn. It takes special discipline to bring the conversation back to data, saying that, âYou have this anecdote somewhere. Get me the data that proves or disproves it.â That specific mindset has got to be inserted in the organization, and that's how it becomes data-driven. It's a very fine line, but if you cross that line, essentially you become a data-driven organization. But, if you stay on the side of anecdotes and stories, then you can't bridge that.â â Ashish Thusoo
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Time Stamps:
*(02:33): The SQL excitement that powered Hive
*(13:42): The evolution of Quboleâs founder hypothesis
*(22:48): Navigating Amazon with AI/ML
*(31:41): The future of AI/ML investment
*(42:01): People are the foundation of the data culture
*(45:57): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile: https://www.linkedin.com/in/ssangani/
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Links
Follow Ashish on LinkedIn
Learn more about AI/ML services on AWS
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When building a data platform, itâs important to stay true to your vision. Whether that's through creating a definitive user experience or an open platform that allows people to build upon it, youâre constructing a cathedral. This cathedral is sophisticated and dependable, and allows for a bazaar of business intelligence, machine learning, and AI use cases.
In this episode, Satyen interviews Matei Zaharia, Chief Technologist and Co-founder of Databricks. Matei is an open source trailblazer and the creator of Apache Spark, a widely used framework for distributed data processing. He is also an Associate Professor of Computer Science at Stanford University where he leads various data management and machine learning projects. Matei and Satyen discuss the Databricks and Alation partnership, exploring how platforms can help companies own their data, and consider the value of democratizing open source large language models.
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âOne of the early stories about open source has been this thing about the cathedral and the bazaar. The cathedral is the thing that's all designed by one person, maybe. It's extremely coherent and so on, but also takes forever to build. And when you go there, there's one message you're hearing. And then the bazaar is the open thing. You don't know who's going to show up each day, but there'll be some really interesting goods and things that you just wouldn't see anywhere else. If you just want to get started and get stuff done, follow the defaults in the product and it'll work. But, we want to be open to some of that innovation and let people bring that in.â â Matei Zaharia
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Time Stamps:
*(01:33): The story behind Spark
*(11:56): Solving for user problems versus product vision
*(20:12): The cathedral and the bazaar of open source
*(24:04): Matei explains the Databricks Unity Catalog
*(31:04): The Databricks and Alation partnership
*(43:36): The data culture at Databricks
*(48:21): Satyenâs Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alationâs LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyenâs LinkedIn Profile: https://www.linkedin.com/in/ssangani/
--------
Links
Follow Matei on LinkedIn
Follow Matei on Twitter
Learn more about Databricksâs Unity Catalog
Learn more about Alation + Databricks
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