Composable Intelligence: Rethinking Customer Data with Seth Earley and Abhi Yadav
Guest: Abhi Yadav, Founder of iCustomer AI
Host: Seth Earley, CEO at Earley Information Science
Published on: June 15, 2025
In this episode of the Earley AI Podcast, host Seth Earley sits down with Abhi Yadav, founder of iCustomer AI—a cutting-edge composable decision intelligence company. With a deep background in enterprise technology, Abhi pioneered the Customer Data Platform (CDP) category and is now leading innovation in advanced multigraph frameworks and customer intelligence. He is also an advisor to leading data management organizations and is passionate about helping brands reshape how they connect with customers by making data truly "AI-ready."
Together, Seth and Abhi explore the evolving landscape at the intersection of AI, data, and enterprise maturity. They dive deep into the challenges and opportunities organizations face as they strive for higher levels of decision intelligence, highlighting both the technical and strategic shifts driving the industry forward.
Key Takeaways:
- The evolution from traditional MDM (Master Data Management) and CDP platforms toward a “knowledge engineering era” powered by AI and multi-graph architectures.
- Why a single source of truth is more about a unified, reconcilable semantic layer than a single data repository.
- The importance of developing robust taxonomies, ontologies, and knowledge graphs to contextualize customer data and orchestrate intelligent decisioning at scale.
- Clear definitions and distinctions between zero-party, first-party, second-party, and third-party data—and why they matter for privacy, compliance, and effective marketing.
- Challenges enterprise leaders face in scaling decision intelligence, especially in determining the right balance of human and machine-led decisions.
- The emerging need for "decision lineage": tracking not only what data was used, but how and why decisions were made, for compliance and transparency.
- Advice for founders, technologists, and enterprise leaders: avoid the hype and focus on solving real, specific problems before building platforms or dreaming of category creation.
Insightful Quotes:
"Don't follow the hype, follow the real problem. There are still so many real problems—and the more specific that problem is, the more impact you can make. Platforms and categories come later; start with solving measurable problems today." - Abhi Yadav
"A single source of truth isn't about having one database. It's about having a unified semantic layer where all your data can be reconciled and understood in context." - Abhi Yadav
"We're moving from a data management era to a knowledge engineering era. It's not just about storing data anymore—it's about making that data intelligent and actionable." - Abhi Yadav
Tune in to discover how to make AI practical, actionable, and intelligent for your organization.
Links
LinkedIn: https://www.linkedin.com/in/abhi123/
Website: https://www.icustomer.ai
Ways to Tune In:
Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
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iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
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Buzzsprout: https://earleyai.buzzsprout.com/
Podcast Transcript: Customer Data Platforms, Knowledge Graphs, and Decision Intelligence
Transcript introduction
This transcript captures a conversation between Seth Earley and Abhi Yadav on the evolution of customer data management and the rise of decision intelligence. Topics include the shift from traditional MDM and CDPs to knowledge engineering, the importance of semantic layers and ontologies, understanding different types of customer data, decision lineage, and practical advice for building AI-ready data infrastructure.
Transcript
Seth Earley:
Welcome to the Earley AI Podcast. I'm your host, Seth Earley, and today I'm thrilled to have Abhi Yadav joining us. Abhi is the founder of iCustomer AI, a composable decision intelligence company. He's a pioneer in the Customer Data Platform space and is now leading innovation in advanced multigraph frameworks and customer intelligence. Abhi, welcome to the show!
Abhi Yadav:
Thank you, Seth. Great to be here.
Seth Earley:
So Abhi, let's start with the big picture. You pioneered the CDP category. How has that space evolved, and where are we headed?
Abhi Yadav:
The CDP space has evolved tremendously. When we started, it was all about collecting and unifying customer data—creating that single customer view. But we've learned that's not enough. Having all the data in one place doesn't automatically give you intelligence or drive better decisions. So we're moving from what I call a data management era to a knowledge engineering era. It's not just about storing data anymore—it's about making that data intelligent and actionable.
Seth Earley:
What does that shift look like in practice?
Abhi Yadav:
It means layering on things like taxonomies, ontologies, and knowledge graphs. It means understanding relationships and context, not just collecting data points. It means being able to reason about your data, to make inferences, to orchestrate intelligent decisions at scale. This is where AI comes in—not just as a tool for analysis, but as a fundamental part of how we organize and use customer data.
Seth Earley:
You mentioned the single source of truth. That's a phrase we hear a lot. What does it really mean?
Abhi Yadav:
That's a great question, and I think it's often misunderstood. A single source of truth isn't about having one database where everything lives. That's not realistic or even desirable in large organizations. What it really means is having a unified semantic layer—a consistent way of understanding and interpreting data across all your systems. It means being able to reconcile data from different sources and understand it in context. So you might have customer data in your CRM, transaction data in your commerce platform, behavioral data in your analytics system—but you have a consistent way of relating all that data together.
Seth Earley:
That semantic layer piece is critical. And that's where things like ontologies and taxonomies come in.
Abhi Yadav:
Exactly. You need a shared vocabulary, a common understanding of what things mean. What is a customer? What is a transaction? What is an interaction? These might seem like simple questions, but in large organizations, different departments often have different definitions. The semantic layer provides that common understanding.
Seth Earley:
Let's talk about data types. There's a lot of confusion around zero-party, first-party, second-party, third-party data. Can you clarify these?
Abhi Yadav:
Sure. Zero-party data is data that customers explicitly and proactively share with you—their preferences, their intentions, their feedback. First-party data is data you collect directly through interactions with your customers—website visits, purchases, app usage. Second-party data is essentially someone else's first-party data that they share with you—like a partner sharing their customer insights. And third-party data is data you purchase from external providers. Each type has different implications for privacy, compliance, and how you can use it.
Seth Earley:
And in the current environment, with privacy regulations and changing consumer expectations, how is this changing?
Abhi Yadav:
There's a massive shift toward zero-party and first-party data. Third-party data is becoming less reliable and less acceptable from a privacy perspective. Organizations need to build direct relationships with their customers and earn the right to collect and use their data. This means being transparent, providing value in exchange for data, and respecting privacy preferences. It's a fundamental shift in how we think about customer data.
Seth Earley:
Let's talk about decision intelligence. What does that mean, and why is it important?
Abhi Yadav:
Decision intelligence is about using data and AI to make better decisions, faster. It's not just about having dashboards or reports—it's about operationalizing intelligence. Can you predict what a customer is going to do next? Can you recommend the right action at the right time? Can you optimize in real-time based on changing conditions? This requires not just data, but the ability to reason about that data, to understand context, to learn from outcomes.
Seth Earley:
And what are the challenges organizations face in building this capability?
Abhi Yadav:
Several. First, data quality and integration—you can't have intelligence without good data. Second, the semantic layer we talked about—you need consistent understanding across systems. Third, the right balance between human and machine decisions—not everything should be automated. Fourth, organizational alignment—different departments need to work together. And fifth, measuring outcomes—you need to know if your decisions are actually better.
Seth Earley:
You mentioned decision lineage. What is that?
Abhi Yadav:
Decision lineage is about tracking not just what data was used in a decision, but how and why the decision was made. This is becoming critical for compliance, for audit, for trust. If an AI system makes a decision that affects a customer—like approving or denying credit, or personalizing pricing—you need to be able to explain that decision. What data was used? What rules or models were applied? What were the alternatives? This transparency is essential, especially in regulated industries.
Seth Earley:
Let's talk about knowledge graphs. How do they fit into this picture?
Abhi Yadav:
Knowledge graphs are foundational. They allow you to represent not just data, but relationships and context. Instead of just knowing that a customer bought a product, you can understand the relationships—what category is the product in, what other products are related, what was the customer's journey, what was the context of the purchase. This rich representation is what enables intelligent reasoning and decision-making. It's the difference between having data and having knowledge.
Seth Earley:
What advice would you give to organizations that are trying to build these capabilities?
Abhi Yadav:
First, don't follow the hype. Follow the real problem. There are still so many real problems in customer data management—and the more specific that problem is, the more impact you can make. Don't try to build a platform on day one. Start by solving one specific, measurable problem. Second, invest in the foundations—data quality, semantic models, governance. These aren't sexy, but they're essential. Third, think about your organization, not just your technology. The best technology won't help if your organization isn't aligned. Fourth, start small and prove value before scaling. And fifth, measure outcomes, not just outputs. Did you actually make better decisions? Did you actually improve customer experience or business results?
Seth Earley:
What about for founders and entrepreneurs in this space?
Abhi Yadav:
Same advice, really. Focus on solving a real, specific problem before you think about building a platform or creating a category. Platforms and categories come later. Start with a problem you deeply understand, that you can solve better than anyone else. Build credibility by delivering measurable value. Listen to your customers. And be patient—building a category takes time.
Seth Earley:
Any final thoughts on where this space is headed?
Abhi Yadav:
I think we're at an inflection point. AI is making it possible to do things with customer data that weren't possible before. But it also raises the stakes around privacy, ethics, and trust. Organizations that can navigate this—that can build intelligent, trustworthy, customer-centric data capabilities—are going to have a massive competitive advantage. But it requires commitment, investment, and a willingness to rethink how you approach customer data.
Seth Earley:
Well, Abhi, thank you so much for joining us today and sharing your insights.
Abhi Yadav:
Thank you, Seth. It's been a pleasure.
Seth Earley:
And thank you to our listeners. You can find Abhi on LinkedIn and learn more about iCustomer AI at icustomer.ai. Thanks for tuning in to the Earley AI Podcast, and we'll see you next time!