Reengineering Knowledge for the AI Era
Guests: Tony Baer, Industry Analyst and Advisor in Data, Cloud, and Analytics
Host: Seth Earley, CEO at Earley Information Science
Published on: April 29, 2025
In this episode of the Earley AI Podcast, host Seth Earley sits down with industry analyst and advisor Tony Baer, a seasoned expert in data, cloud, and analytics. With decades of experience guiding global tech leaders like AWS and Oracle, Tony brings a nuanced perspective on how knowledge engineering is evolving—and why context is the missing link in many enterprise AI initiatives.
Together, Seth and Tony explore the shift from static data models to dynamic knowledge frameworks, the renewed importance of governance, and how graph databases and generative AI are reshaping enterprise intelligence. This is a conversation packed with hard-earned lessons and actionable insight for data, IT, and transformation leaders aiming to make AI work in the real world.
Key Takeaways:
- Knowledge engineering today is about dynamic, adaptive structures—not static ontologies or rigid models.
- The role of the knowledge engineer is shifting: it’s less about technical mastery and more about bridging data, business, and domain expertise.
- Context is foundational. The five W’s—Who, What, When, Where, Why (and How)—unlock meaningful, actionable intelligence.
- Graph databases and AI are enabling real-time connections across data, turning static information into living knowledge.
- Generative AI delivers the most value when rooted in organizational context. RAG strategies demand clean data and strong information architecture.
- Successful AI initiatives are focused. Start with well-bounded, high-impact processes—avoid boiling the ocean.
- Core principles from previous data waves still apply. It’s about evolving governance, stewardship, and architecture for the AI era.
- Sustainable value comes from feedback loops, iteration, and alignment—not silver bullets.
Tune in to discover how to make AI practical, actionable, and intelligent for your organization.
Insightful Quotes
"Just because something is old does not make it wrong. There are a lot of disciplines we've built up over the years—governance, data stewardship—that still matter. The principle was right. We just adapt it and use our learnings from each cycle to become more knowledgeable and proficient." - Tony Baer
"Context is everything. Without the who, what, when, where, why, and how, you just have data. With context, you have knowledge." - Tony Baer
"Knowledge engineering is no longer about building perfect ontologies. It's about creating adaptive systems that can evolve with your business." - Tony Baer
Tune in to discover how to make AI practical, actionable, and intelligent for your organization.
Links
LinkedIn: https://www.linkedin.com/in/dbinsight/
Website: https://www.dbinsight.io
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Podcast Transcript: Knowledge Engineering, Graph Databases, and Context in AI
Transcript introduction
This transcript captures a conversation between Seth Earley and Tony Baer on the evolution of knowledge engineering in the AI era. Topics include the shift from static to dynamic knowledge models, the critical importance of context, graph databases as enablers of connected intelligence, RAG and generative AI best practices, focused AI implementation strategies, and applying timeless data principles to modern AI challenges.
Transcript
Seth Earley:
Welcome to the Earley AI Podcast. I'm your host, Seth Earley, and today I have Tony Baer joining us. Tony is an industry analyst and advisor with decades of experience in data, cloud, and analytics. He's worked with global tech leaders and brings a really nuanced perspective on how knowledge engineering is evolving. Tony, welcome to the show!
Tony Baer:
Thank you, Seth. Great to be here.
Seth Earley:
So Tony, let's start with knowledge engineering. It's a term that's been around for a long time, but it feels like it's having a renaissance. How has it evolved?
Tony Baer:
It absolutely is having a renaissance, but it's also evolved significantly. Knowledge engineering used to be about building these perfect, static ontologies—very rigid, formal structures. But that approach doesn't work well in today's dynamic business environment. Now, knowledge engineering is about creating adaptive systems that can evolve with your business. It's less about perfection and more about usefulness and adaptability.
Seth Earley:
What's driving that shift?
Tony Baer:
Several things. First, the pace of business change has accelerated. Your knowledge models need to keep up. Second, we have better technology—graph databases, AI—that can handle more dynamic, complex relationships. Third, we've learned from past failures. Those rigid ontologies were expensive to build and hard to maintain. We need something more practical.
Seth Earley:
You mentioned graph databases. Why are they important for knowledge engineering?
Tony Baer:
Graph databases are foundational because they model relationships natively. Traditional relational databases are great for structured data, but they struggle with complex, interconnected information. Graph databases excel at that. They can represent the who, what, when, where, why, and how—the full context of information. And that context is what turns data into knowledge.
Seth Earley:
Talk more about context. Why is it so critical?
Tony Baer:
Context is everything. Without the who, what, when, where, why, and how, you just have data. With context, you have knowledge. Think about it—knowing that a customer bought a product is data. Knowing who that customer is, what they bought, when they bought it, why they bought it, and how they're using it—that's knowledge. That context is what enables you to make intelligent decisions and predictions.
Seth Earley:
And how does AI fit into this picture?
Tony Baer:
AI, particularly generative AI, is incredibly powerful for working with knowledge. But—and this is crucial—it delivers the most value when it's rooted in organizational context. That's where RAG (Retrieval Augmented Generation) comes in. You're not just relying on what the AI learned during training. You're grounding it in your specific organizational knowledge. But that requires clean data and strong information architecture.
Seth Earley:
Let's talk about RAG. What are the keys to making it work?
Tony Baer:
First, you need good data—clean, well-organized, properly tagged. Second, you need clear context and relationships. Third, you need the right architecture to retrieve relevant information efficiently. Fourth, you need governance to ensure quality and compliance. And fifth, you need to measure and iterate. RAG isn't set-it-and-forget-it. It requires ongoing refinement.
Seth Earley:
What mistakes do you see organizations making with AI initiatives?
Tony Baer:
The biggest one is trying to boil the ocean. They want to transform everything at once. But successful AI initiatives are focused. Start with a well-bounded, high-impact process. Prove value. Learn. Then expand. The other big mistake is neglecting the fundamentals—data quality, governance, architecture. You can't build AI on a shaky foundation.
Seth Earley:
You mentioned governance. How has that evolved in the AI era?
Tony Baer:
The principles haven't changed, but the implementation has. We still need governance, data stewardship, quality controls. But we need to adapt them for AI. That means thinking about things like model governance, bias detection, explainability, decision lineage. It's an evolution, not a revolution.
Seth Earley:
There's a quote I love from you: "Just because something is old does not make it wrong." Can you expand on that?
Tony Baer:
Sure. There's a tendency in tech to throw out everything from the past and start fresh. But that's often a mistake. A lot of the disciplines we've built up over the years—governance, data stewardship, master data management—still matter. The principle was right. We just need to adapt it and use our learnings from each cycle to become more knowledgeable and proficient. Don't throw out the baby with the bathwater.
Seth Earley:
Let's talk about the role of the knowledge engineer. How is that changing?
Tony Baer:
It's becoming less about technical mastery and more about being a bridge. The modern knowledge engineer needs to understand data and technology, yes. But they also need to understand the business, the domain, and how to translate between technical and business stakeholders. They're facilitators and translators as much as they are technicians.
Seth Earley:
What skills should they be developing?
Tony Baer:
Domain knowledge is huge. Understanding graph thinking and relationship modeling. Being comfortable with AI and how it works. Strong communication skills. The ability to work iteratively and adapt. And perhaps most importantly, business acumen—understanding how knowledge engineering delivers business value.
Seth Earley:
What advice would you give to organizations starting their knowledge engineering or AI journey?
Tony Baer:
First, start small and focused. Don't try to do everything. Second, invest in the fundamentals—data quality, architecture, governance. Third, build a team that combines technical skills with business understanding. Fourth, create feedback loops so you can learn and improve. Fifth, be patient. This is a journey, not a destination. And sixth, don't look for silver bullets. Sustainable value comes from disciplined execution, not magic.
Seth Earley:
Any final thoughts on where this space is headed?
Tony Baer:
I think we're going to see continued convergence of different technologies—graph databases, AI, knowledge graphs, semantic technologies. We're going to see more emphasis on context and relationships, not just data. And we're going to see organizations that get this right pulling ahead of their competitors. Because in the end, the organizations that can turn their data into actionable knowledge fastest and most effectively are going to win.
Seth Earley:
Well, Tony, thank you so much for joining us and sharing your insights.
Tony Baer:
Thank you, Seth. It's been a pleasure.
Seth Earley:
And thank you to our listeners. You can find Tony on LinkedIn and learn more at dbinsight.io. Thanks for tuning in to the Earley AI Podcast, and we'll see you next time!