Jacobus Geluk: Use-Case Trees for the Data-Product Marketplace – Episode 26

Jacobus Geluk The arrival of AI agents creates urgency around the need to guide and govern them. Drawing on his 15-year history in building reliable AI solutions for banks and other enterprises, Jacobus Geluk sees a standards-based data-product marketplace as the key to creating the thriving data economy that will enable AI agents to succeed at scale. Jacobus launched the effort to create the DPROD data-product description specification, creating the supply side of the data market. He's now forming a working group to document the demand side, a "use-case tree" specification to articulate the business needs that data products address. We talked about: his work as CEO at Agnos.ai, an enterprise knowledge graph and AI consultancy the working group he founded in 2023 which resulted in the DPROD specification to describe data products an overview of the data-product marketplace and the data economy the need to account for the demand side of the data marketplace the intent of his current work on to address the disconnect between tech activities and business use cases how the capabilities of LLMs and knowledge graphs complement each other the origins of his "use-case tree" model in a huge banking enterprise knowledge graph he built ten years ago how use case trees improve LLM-driven multi-agent architectures some examples of the persona-driven, tech-agnostic solutions in agent architectures that use-case trees support the importance of constraining LLM action with a control layer that governs agent activities, accounting for security, data sourcing, and issues like data lineage and provenance the new Use Case Tree Work Group he is forming the paradox in the semantic technology industry now of a lack of standards in a field with its roots in W3C standards Jacobus' bio Jacobus Geluk is a Dutch Semantic Technology Architect and CEO of agnos.ai, a UK-based consulting firm with a global team of experts specializing in GraphAI — the combination of Enterprise Knowledge Graphs (EKG) with Generative AI (GenAI). Jacobus has over 20 years of experience in data management and semantic technologies, previously serving as a Senior Data Architect at Bloomberg and Fellow Architect at BNY Mellon, where he led the first large-scale production EKG in the financial industry. As a founding member and current co-chair of the Enterprise Knowledge Graph Forum (EKGF), Jacobus initiated the Data Product Workgroup, which developed the Data Product Ontology (DPROD) — a proposed OMG standard for consistent data product management across platforms. Jacobus can claim to have coined the term "Enterprise Knowledge Graph (EKG)" more than 10 years ago, and his work has been instrumental in advancing semantic technologies in financial services and other information-intensive industries. Connect with Jacobus online LinkedIn Agnos.ai Resources mentioned in this podcast DPROD specification Enterprise Knowledge Graph Forum Object Management Group Use Case Tree Method for Business Capabilities DCAT Data Catalog Vocabulary Video Here’s the video version of our conversation: https://youtu.be/J0JXkvizxGo Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 26. In an AI landscape that will soon include huge groups of independent software agents acting on behalf of humans, we'll need solid mechanisms to guide the actions of those agents. Jacobus Geluk looks at this situation from the perspective of the data economy, specifically the data-products marketplace. He helped develop the DPROD specification that describes data products and is now focused on developing use-case trees that describe the business needs that they address. Interview transcript Larry: Okay. Hi everyone. Welcome to episode number 26 of the Knowledge Graph Insights podcast. I am really happy today to welcome to the show, Jacobus Geluk. Sorry, I try to speak Dutch, do my best. His last name means happiness in Dutch, which you'll see a lovely bit of serendipity here. Jacobus is the CEO at Agnos.ai, which is a really prominent enterprise knowledge graph consultancy based in London. So welcome Jacobus, tell the folks a little bit more about what you're doing these days. Jacobus: Oh, thank you very much, Larry, for the opportunity. Well, we are a small, let's say, boutique consulting company, but focusing on the enterprise knowledge graph in combination with gen AI, LLM, of course. I think that's a match made in Heaven, these two technologies, they need each other and we jump on that completely all in because I think that's the future. And yeah, would love to talk about this topic- Larry: Yeah, well there's one topic in particular we've been talking about that I really want to air out today is the notion of both of those, all of AI, it runs on data and there's this emerging thing, the data product that is like, I don't know how clearly articulated it is, but you've worked on the DPROD initiative working on articulating an ontology for what a data product is. So you know as much about it as anybody. So what I'd love to do is just talk about, well first, what is a data product? For folks who aren't familiar or discovering this for the first time, how would you describe a data product? Jacobus: Yeah, well, we started talking about this, I set this up in September 2023, and I started talking to Tony Seale, who's a very famous, he's the The Knowledge Graph Guy. You'll find him on LinkedIn. He writes fantastic articles. And at that time, he worked on the largest knowledge graph project in the financial industry, I think at the moment, which is one of the largest which was at UBS. And I asked him to become the chair of a new work group that they wanted to set up in the context of the Enterprise Knowledge Graph Forum, which is part of the Object Management Group, which is a standards organization like W3C. And to my surprise, almost it went so well that within a year we basically hammered out this standard, or it's now an official OMG standard called DPROD that stands for the data product ontology. Jacobus: And we work with many, many people mostly from banks like JP Morgan and some other people from UBS, Credit Suisse was involved, London Stock Exchange Group, Bloomberg, you name it. Like a whole range of people, Amazon, British Telecom. So there's multiple different types of people working on it: specialists, data architects, et cetera. And the idea was we want to see the world moves towards data marketplaces, like large companies like London Stock Exchange Group for example, or Azure, Google, et cetera. They host data or they sell data. They are data vendors in a sense. So if you sell something, what is your product? Your data is a product or access to that data is a product. So rather than modeling things in terms of data sets, et cetera, which is what we did so far, that is the state-of-the-art data, cataloging, et cetera, using a standard called DCAT that is basically the data catalog standard. Jacobus: So we thought, "Okay, let's elevate that a little bit higher." We don't want to think in terms of pure data sets anymore. That's kind of more like an internal thing. The customer doesn't really care. The customer just wants to know, "What is your product, how can I access it, how can I buy it? What are the terms and conditions? What is the purpose? What are the use cases that we can support," et cetera. So I'm not claiming that we have all of that, but we have at least created a one step up from DCAT, literally an extension to DCAT that basically says, "Okay, now we define what the data product is, what are the inputs and the outputs. Input ports, output ports, basically how does it fit into a larger supply chain of data?" And that is already a step forward and companies are using it. Jacobus: Like there's already several companies that have told us that they are actually using it like JP Morgan and London Stock Exchange Group, UBS, they are all using it. I'm not sure if it's already in production or not, but they worked with us to create this and there's apparently a need to model the world in terms of your data products. Jacobus: But my own story to that is, okay, you have products, but you would say instead of a data marketplace, you could also say it's a data economy. Basically you want to look at the world as a data economy, your own enterprise, but also beyond the enterprise. It's a larger data economy where you have supply and demand. So we have now defined all these data products on the supply side of the data economy, but what is the demand side? That there's no real standard yet, I think, that defines what the use cases are. Jacobus: Like if you want to talk to the business, the business has a problem, has a budget, and they want us to build something every time. So what is that? That is a use case. That's the term that they use. The business is talking about use cases or apps or systems or whatever you call it, but most of the time they use the term use case. So we want to create a new thing, basically defining the stuff on the demand side of that data economy called use cases. And these use cases can cannot only serve the purpose of defining very precisely what the business wants and needs, but also how that links to data products with data contracts in between et cetera. Jacobus: And last but not least, before I stop talking, also the LLM, the gen AI basically needs to know what is the use case I am supposed to operate in. So that's the most exciting angle to this story basically is we want to control the LLM agents, of course, and make them useful, productive, producing high quality output in production, in mission-critical use cases. But what are those use cases? We want those use cases to be data and it's data as code almost. But let's stop talking about this now. Larry: No,