AI Radicals

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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“I 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.” – 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): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

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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. You’re 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 there’s hesitancy to invest in data. In sports, it’s the exact opposite. The use of data is felt immediately in game wins, player selection, and gate revenue.

Known as “The Real Moneyball guy,” 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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“Even 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?” – 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): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

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* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s LinkedIn Profile: 

https://www.linkedin.com/in/ssangani/

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Links

Learn more about Raoul Wallenberg’s 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 we’re using technology. Without mindfulness, instead of you driving the tech, the tech may be driving you. 

Nate Anderson lives by and continues to share Nietzsche’s 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 “yes” to life, and what Nietzsche would have to say about tech.

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“Connection 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.” – 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): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://www.alation.com/podcast/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s 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. That’s 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 – but critical – side of AI.

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“We'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.” – Guy Scriven

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Time Stamps:

*(02:22): Less reporting, more commentary 

*(13:32): Dataset discovery 

*(22:34): ChatGPT’s hallucination problem 

*(34:38): AI headlines on the rise 

*(41:48): What’s the next big AI story? 

*(46:10): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://www.alation.com/podcast/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s 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 they’re 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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“I 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.” – 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): Scott’s investment focus

*(30:09): The “tables-stakes economy” 

*(38:11): AI and public policy 

*(47:43): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://www.alation.com/podcast/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s LinkedIn Profile: 

https://www.linkedin.com/in/ssangani/

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Links

Read The Fuzzy and the Techie

Visit Scott’s 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 they’re 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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“We'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.” – 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): Maddy’s role at Fanatics

*(39:27): Structuring the team 

*(47:19): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://www.alation.com/podcast/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s 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. It’s 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, you’ll learn everything about LLMs that you’ve 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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“This idea of, ‘How does an LLM work?’ 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, ‘This is really cool. I've tried it, it's awesome. It’s cool as hell. But how can I use it to improve my business?’ 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.” – 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): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://www.alation.com/podcast/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s 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 you’re 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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“We 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, ‘innovative,’ 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.” – 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): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://www.alation.com/podcast/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s LinkedIn Profile: 

https://www.linkedin.com/in/ssangani/

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Links

Read Bapu’s 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 Tamr’s tech evolution, third normal form, and probabilistic methods.

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“There'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. We’re 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.” – Andy Palmer

“Data 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.” – 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): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

Learn more:

* Subscribe to the newsletter: https://www.alation.com/podcast/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s 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, we’ve seen that data experts have been promoted to leadership roles. It’s proof that organizations are seeing the value of data and the significance of establishing a data culture.

In this episode, you’ll 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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“I found that if I told somebody to do a task, they might try to do that task. But if I say, ‘Create this effect,’ they owned it because they felt a level of responsibility for what approach that they chose, and it made it much stickier.” – Stan McChrystal, Retired US Army General

“I 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.” – Jepson Taylor, Chief AI Strategist at Dataiku

“Those 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 it’s with a very small subset of data, to begin to develop that relationship, that trust.” – Randy Bean, Author of Fail Fast, Learn Faster

“You have to be able to have a learner’s 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.” – 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 “C’s”

*(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/

* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/

* Satyen’s LinkedIn Profile: 

https://www.linkedin.com/in/ssangani/

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Links

Listen to Randy Bean’s episode

Listen to Jennifer Belissent’s episode

Listen to Taylor Brown’s episode

Listen to Ashish Thusoo’s episode

Listen to Stan McChrystal’s episode

Listen to Paul Leonardi’s episode

Listen to Mike Capone’s episode

Listen to Tricia Wang’s episode

Listen to Bernard Liautaud’s episode

Listen to Jepson Taylor’s episode

Listen to Wendy Turner-Williams’s episode

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