Any digital marketing leader will tell you that data and marketing strategies go hand-in-hand. In this episode, Michael Olaye, EVP and Managing Director of Hero Digital, shares his journey and practical strategies for success, drawing from his career path that began with door-to-door job hunting and led to spearheading major digital initiatives.

Michael emphasizes the central role of data in digital marketing, from informing internal business decisions to enhancing customer experiences, and discusses the dual focus of AI in driving internal efficiency while offering robust public-facing tools.

He highlights the critical interplay between data governance and AI ethics, stressing the importance of businesses being 'AI ready.' By exploring customer journeys and leveraging data for innovation, Michael demonstrates how insights can shape product development and business strategies.

As a forward-thinker, he shares his enthusiasm for emerging technologies like learning agents and multimodal models, envisioning a transformative future for business operations. Through candid anecdotes and expert advice, Michael delivers actionable insights on harnessing data and AI to drive innovation and customer satisfaction.

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“Some clients do not know that they're sitting on gold, they do not know that. They have tons of data that they've never done anything with and then they focus on the most simplistic things: media, SEO, social media content, website content. Then you come in and you're like, ‘Hey, we can help your customer service be more efficient by understanding how the data, how long it takes a call to go through. We can help you process products more better by understanding the transaction from seeing something online to going in store, to buying it, to returning it.’ Looking at those data sets and seeing patterns or bringing them together to see journeys, that's where the secret lies.” – Michael Olaye

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Time Stamps

*(03:37): How Michael uses data for customer experience

*(11:39): AI in marketing today: The role of data

*(20:54): The dangers of bad data in AI

*(23:30): How do you find high-value data?

*(30:17): Understanding data and the brand-loyalty debate

*(38:16): David’s takeaways

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This podcast is presented by Alation.

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Links

Connect with Michael on LinkedIn

Learn more about Leonardo.AI

Learn more about Waldo

Learn more about Firefly

Learn more about Google Graveyard

If you’re a history buff in the data world, you know that there’s a complex interplay between data, statecraft, and machine learning. The history of data visualization is entwined with societal governance and technological advancements, starting from the usage of statistics for statecraft in the 18th century to the transformative innovations during World War II that birthed computation and data science as we know it. And because of the subjective design choices that underpin data gathering and analysis, there’s an inherently political nature of deciding what data to collect and how to utilize it, which is critical in understanding both historical and contemporary data practices.

As we move into the modern applications of data science and the advent of AI technologies, deep reinforcement learning and the integration with generative AI models, these technologies are reshaping the field by enabling computers to process and interact with unstructured data in unprecedented ways. Satyen and Chris discuss his book How Data Happened, the origins of data science and the role of Alan Turing in the creation of digital computing, and the challenges generative AI brings around model interoperability.

*Satyen’s narration was created using AI

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“In the last two years, one of the major techniques for advancing the most eye-popping products has been RLHF, Reinforcement Learning from Human Feedback. There's innumerable subjective design choices happening there, which eventually become encoded in a product. But, the presentation of it as though it's somehow unbiased and free from any subjective design choices is illusory.” – Chris Wiggins

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Time Stamps

*(01:36): How did Chris come to write How Data Happened?

*(10:33): World War II as the springboard for data science and digital computing

*(18:37): The tension between objectivity and subjectivity in data today

*(25:36): What is Reinforcement Learning from Human Feedback (RLHF)? 

*(36:03): How has Gen AI impacted data science?

*(44:53): Satyen’s takeaways

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Sponsor

This podcast is presented by Alation.

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

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Links

Connect with Chris on LinkedIn

Order Chris’s book How Data Happened

What does it take for data leaders to deliver real business value? In this episode, Taylor Culver, founder of XenoDATA, shares practical strategies for success, including:

Focus on the right problems: Taylor explains the importance of refining problem statements for actionable, data-driven solutions.

Engage like a salesperson: Actively listening to stakeholders and identifying pain points is key to building impactful use cases.

Adopt a product management mindset: Taylor emphasizes weaving governance and architecture into customer-centric data strategies.

While the path of the data leader is fraught with obstacles, success is possible. Taylor offers time-tested strategies to help data and business leaders alike make a measurable impact.

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“What data leaders should just own is the path to me is probably going to be fraught with failure, but I need to be able to pivot and I need to be agile. I can very much serve myself by adhering to a common set of principles, which I'm going to practice consistently and continually adapt and adjust in the way I engage with my stakeholders and identify their problems and lean in or lean out on data management techniques or delivering certain solutions. It comes down to intent. Do you genuinely want to help people in your business solve problems with data? Do you genuinely want to grow? Do you genuinely recognize that there's not a magic bullet to doing this? Those are the data leaders who will be successful despite facing adversity.” – Taylor Culver

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Time Stamps

*(07:01): The data-business people problem 

*(17:50): How data leaders can tackle business problems in 3 steps

*(26:22): Is data a strategic function or an enablement function? 

*(33:50): Strategy: Data offense vs. data defense

*(40:45): Data is a people business: the value of trust

*(46:20): Our takeaways

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Sponsor

This podcast is presented by Alation.

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* Satyen’s LinkedIn Profile: 

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

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Links

Connect with Taylor on LinkedIn

About 15 years ago, organizations knew they needed data governance but faced a branding problem. People hated the term. Stewart Bond coined “data intelligence” to describe intelligence about data and shift the governance conversation – and a category was born. Today, data intelligence represents a $9B+ market. 

This concept has given rise to the "data intelligence stack," which includes data cataloging, data quality management, and data product hubs, all of which play vital roles in AI model development.

Looking ahead, big changes are coming. IDC predicts that by 2028, the Chief Data Officer’s role will rival the CIO’s in shaping technology investments. In this episode, Satyen and Stewart dive into the components of data intelligence, the growing importance of data products, and key insights from IDC's recent MarketScape evaluation.

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“We talk about the modern data environment as being highly distributed. Data is all over the place. It's very diverse. There's so many different kinds of data that we're dealing with today. It's also very dynamic. That data is always moving and it's always changing. I think data intelligence as a category, as a capability, there's always going to be that need to have the intelligence about the data that the organization manages in the modern data environment available. That is visible across all the different places the data lives in that modern data environment.” – Stewart Bond

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Time Stamps

*(04:54): What is data intelligence?

*(09:49): The rise of the data marketplace

*(13:01): How will AI impact the data intelligence market?

*(25:54): Is the Chief Data Officer role in trouble? Or is it growing in prominence?

*(38:40): What is the IDC MarketScape?

*(41:14): Satyen’s takeaways

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Sponsor

This podcast is presented by Alation.

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Links

Connect with Stewart on LinkedIn

Learn more about IDC MarketScape

The transformative potential of AI is going to affect all of us, regardless of what industry you’re in. While AI has the capability to democratize high-demand professions through specialized copilots, it also presents potential positive and negative societal impacts, including misinformation and political discourse manipulation. Today, we’re taking an in-depth look at the evolution of AI copilots tailored for specific professional fields and the need for critical thinking and transparent AI systems to ensure ethical deployment and improved outcomes in sectors like healthcare and finance.

There’s a growing need for federal guidelines to prevent fragmented AI governance (think the EU AI Act). However, differing approaches to regulations across regions can lead to unbalanced directives. Politics are also influencing this new AI landscape. From potential deregulatory pushes under a Trump administration to sustained regulatory efforts under a Harris-led government, AI regulation will look different depending on who wins the US election. And it’s not just politics, total war is a significant worry when it comes to the use of AI. From military strategies to disrupting democracy, AI has the power to impact innovation, ethics, politics, and society. Satyen sits down with Jeremy to discuss his book Mastering AI, the importance of AI regulations, and the impact of AI on the job market.

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“In the US, we have this issue where the states are starting to take action because of the lack of action by the federal government and I think that's problematic. I don't think you want a system where you have every state with its own AI act and different laws to comply with in every state. I do think we need to have some action at the federal level. When we're going to see that happen, I don't know, because there has been a lot of lack of will. Even though there was some bipartisan efforts in Congress that looked like they were maybe going to pay off last year. I think there's some agreement on both sides of the aisle that there should be some rules and regulation passed around AI.” – Jeremy Kahn

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Time Stamps

*(02:37): The future of AI: A boon for the middle class, a threat to democracy

*(12:18): The case for federal AI regulation in the US

*(20:11): The impact of the US election on AI regulations

*(30:29): What is the Pigouvian or robot tax? 

*(36:39): What is total war? How will AI play a role?

*(46:35): Takeaways

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Sponsor

This podcast is presented by Alation.

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* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/

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Links

Connect with Jeremy on LinkedIn

Order Jeremy’s book, Mastering AI

Learn more about SB-1047

Learn more about Anthropic’s AI Constitution

Learn more about the FASTER Act

Legacy systems in the financial services industry are notorious for being slower to adapt to big data. But for good reason. Because banks are complicated and intersect across networks, it’s difficult to implement new processes without disturbing the rest of the system. Yet, GXS Bank is proving it doesn’t have to be this way. The digital bank is leveraging data to drive financial inclusion and innovation. Their strategic use of data from parent companies and partnerships, helps create financial products for underserved communities in Singapore.

Not only is GXS Bank driving inclusion, but they’re also exploring the impact of generative AI on customer service, fraud detection, and operational efficiency. The technology is transforming the financial sector by offering valuable insights for improving data quality, governance, and stakeholder engagement. Satyen and Geraldine discuss the importance of creating better products and credit risk profiles, the intricacies of data sharing agreements and operational challenges, and strategies for leveraging AI to enhance customer service and fraud detection.

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“If you think about AI being part of a product manager, product creation, so you go to different segments of your consumers, see what their pain points are, do the summarization, and then say, ‘Hey, AI, can you create a new product that would match the needs of 50% of my segments in the consumer business?’ The AI quickly generates some AI product, a banking product for you with such features and you iterate and iterate and iterate. These are some of the tools that a product manager could really leverage on to create a new product from scratch.” – Gerladine Wong

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Time Stamps

*(01:24): About GXS Bank

*(10:04): Gen AI at GXS

*(15:35): Supporting financial inclusion with alternative data sources

*(31:34): Playing data offense vs defense at GXS

*(40:51): Takeaways

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Sponsor

This podcast is presented by Alation.

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* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/

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Links

Connect with Geraldine on LinkedIn

The Data Radicals podcast returns on October 23rd! 

This season, we're exploring how trusted data drives value, whether that’s building game-changing AI or transforming data into dollars – and business results. You’ll hear from data leaders, journalists, and AI trailblazers like Dr. Geraldine Wong, CDO of GXS Bank, New York Times Chief Data Scientist, Chris Wiggins, and Jeremy Kahn, AI Editor at Fortune Magazine. And so many more! Get ready to discover groundbreaking strategies from some of the most innovative minds in data today. 

Welcome to season three of Data Radicals! Powered by the team here at Alation.

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This podcast is presented by Alation.

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* Satyen’s LinkedIn Profile: 

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

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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“On 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.” – 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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This podcast is presented by Alation.

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Links

Read Rules for Radicals

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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“We 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, ‘Oh, what's relevant? And I know how to bring the right information to bear so that I can reason about it.’ This ping pong between reasoning and retrieving the right knowledge is what we need to get good at.” – 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 AI’s black box: The challenge of understanding complex systems 

*(37:02): Striking a balance between AI innovation and thoughtful regulation

*(40:01): Satyen’s Takeaways

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Sponsor

This podcast is presented by Alation.

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

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Links

Connect with Edo on LinkedIn

Watch Edo’s TED Talk

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.

Learn more:

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Links

Connect with Sanjeevan on LinkedIn