Lulit Tesfaye: Semantic Architectures for the AI Era – Episode 52

Lulit Tesfaye Generative AI has prompted a flurry of experimentation in enterprises, resulting in a parade of failed pilots, unproven PoCs, and unrealized return on investment. Lulit Tesfaye helps companies improve and optimize their AI capabilities by adding semantics to their enterprise architectures. This puts their precious knowledge assets in a context that machines can actually work with. A crucial part of that context is the interoperability that standards like RDF enable. We talked about: her work at Enterprise Knowledge as Partner & Vice President, Knowledge, Data, and AI Solutions the publication of Bridging Knowledge, Data, and AI: Harnessing the Semantic Layer Framework to Drive Intelligence, which she co-authored with colleagues at EK her professional path from software engineering and applications development to her current role how generative AI has increased data teams' workloads, leading to numerous pilots and PoCs, which has exposed the need in enterprises for semantic context her definition of a semantic layer "knowledge assets," a foundational concept that she uses to talk about data, content, information, expertise, and any other assets to which can append metadata how she and colleagues see the distinction between the two types of semantic layers: data and analytics layers and semantically modeled layers, and the benefits of the latter the framework she uses to convey the benefits of a semantic layer to executives the swing of the enterprise architecture pendulum back towards monoliths, as enterprise solutions providers like ServiceNow acquire semantic tool companies the importance of interoperability standards like RDF in semantic systems examples of use cases that are best suited to labeled property graphs and RDF knowledge graphs and how she sees the debate about which to use going away soon two common failure points in semantic layer projects: starting with tools, not competency questions insufficient skillsets and lack of leadership support the use of multiple levels in a semantic layer in large, complex organizations emerging trends she sees in semantic architectures: the ubiquity of AI the need to design for machines the need for investment in a context layer the disappearance of the artificial lines between structured and unstructured knowledge assets the evolution of enterprise operating models the importance of staying focused on our knowledge assets and adopting solutions that are based on standards and enable interoperability Lulit's bio Lulit Tesfaye is a Partner and the Vice President at Enterprise Knowledge, LLC., the largest global consultancy dedicated to knowledge and data management. Lulit brings over 15 years of experience leading global initiatives, specializing in information and data management solutions and integrations. Lulit is most recently focused on applying practical knowledge management, data governance, and semantic data foundations to optimize organizational information assets – providing the structure, context, and explainability needed to support responsible and effective enterprise AI. Connect with Lulit online LinkedIn Enterprise Knowledge Video Here’s the video version of our conversation: https://youtu.be/E6Yz6GEop7M Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 52. Knowledge management and data engineering were already changing even before ChatGPT arrived. Now, with the proliferation of AI, the boundaries are blurring between structured and unstructured data and other knowledge assets. As a partner at a prominent knowledge consultancy, Lulit tess fai sees every day the implications of this change, chief among them the need for semantic layers built for interoperability on a solid foundation of industry standards. Interview transcript Larry: Hi everyone. Welcome to episode number 52 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Lulit Tesfaye. Lulit is currently the Partner & Vice President, Knowledge, Data, and AI Solutions at Enterprise Knowledge, the big famous consulting agency in the DC area. She's also the co-author of the recent book, or maybe forthcoming, I'm not sure, but I've seen it online so I know it's coming. Bridging Knowledge, Data, and AI: Harnessing the Semantic Layer Framework to Drive Intelligence, which she wrote with her co-authors, her colleagues at EK. Well, welcome, Lulit. Tell the folks a little bit more about what you're doing these days. Lulit: Thank you, Larry. Thanks for having me. I'm happy to be here and have been looking forward to this conversation. That's a very kind introduction, so I appreciate that. So, maybe a good way to start would be a little bit from the journey over the, without dating myself, I'll say I've been in this space close to 20 years at this point and it's been not a direct or linear path given how things are evolving, but the short version of it is started in working within software engineering and applications for data and information in that space, which led to me joining Enterprise Knowledge after I met the co-founders about a decade ago. Lulit: And then our focus was in knowledge management primarily, which traditionally is considered as learning and development or training or relegated to librarians, so to speak, information architecture and so forth. But over the past five years specifically, we've been seeing a big shift towards the silos in a way dissolving between the different types of these groups within organizations and where information or knowledge sits. So, we think in terms of structured and unstructured data, unstructured being content, PDFs, and all the things that you have in your files and folders, whereas structured data has been traditionally for data and analytics teams. Lulit: And the biggest swing that we have seen thanks to especially generative AI, is that unstructured data, which is really 80%, 90% of your organizations, I'll call it knowledge asset, came into the purview of data teams, data and analytics teams because of generative AI. And that was what led to this big focus on knowledge management for data as well as handling structured data. So, I think we've all gone through the pilot phases for POCs and pilots for AI with the pilot purgatory, all the failed efforts. That has been the journey, especially over the past, I would say five years is working with organizations that have been experimenting and embracing AI or today trying to wrangle what we call the AI pilot sprawl, scenario that exists. Lulit: So, to give you an example, one of our clients recently said we just cataloged over 150 pilots, AI pilots, and we haven't seen the return on investment as we should. Good experimentation, good learning, but we need to put an end to it and have a strategic path. So, this is where we are today. All the pilots and POCs for many different reasons that I can get to either have failed or have stalled. And at the same time, there are a lot of things in production that I would commend the organizations leading that. But the biggest failure and a big part of our journey has been the backbone, the core, the semantics or the context for AI as the lens that AI sees your organization, that has been the core of it. Just to answer maybe in a long way what we've been working on over the past few years. Larry: Yeah, I think the first time I met you, you did a talk about the semantic layer at that data summit in London, the one that Henry Stewart Events puts together. And I've been following your work about semantic layers since then. And what you just talked about, that's kind of the classic sort of framework that people use to connect this, like the data stuff you're talking about, the KM. But I'm really curious, in the knowledge graph world, everybody talks about knowledge engineering. And what you just described seems like the setting the stage for a big shift from knowledge management to knowledge engineering. Does that make sense? And if so, how does it fit in how you see with things right now? Lulit: It does. And I think maybe the best way to answer that would be to take a step back and really define what we mean by semantic solutions or specifically a semantic layer, because I think there's a lot of confusion in this space. It's funny, we start the book with it's all about semantics, right? It is truly about semantics. And the key definition that I would make here distinction is semantic layer is not something new. In fact, we're just chatting. It celebrated its 25th year anniversary this year. It's come a long way I think. And what we mean by semantic layer is we are talking about the shift from the focus from the physical data itself to the data about the data, so the meta aspect of it. Lulit: So, this means abstracting the physical data with definitions, business glossaries, metadata, the aboutness of the data, taxonomies to control some of these vocabularies that are very unique to your organization, what fits under our product, list of product, list of services, ontologies to be able to relate those concepts, these vocabularies that you have to your real world entities. This customer owns this product, has bought this product and needs this type of marketing material or training material. How do you connect to these pieces? That's where ontology allows you to explicitly make that machine-readable, places things. Lulit: And then when you apply this model, this semantic model on your assets, and I am collectively going to call going forward data content and information and expertise, anything you can append metadata to, knowledge assets, then you have your graph, your Knowledge Graph. So, this is what we mean when we talk about a semantic layer and the components that sit within semantic models....