Intro

Alan Morrison After 20-plus years of industry analysis, Alan Morrison has developed a keen sense for how knowledge graphs can help enterprises. Even though he has focused on advanced tech and emerging IT practices and is deeply immersed and invested in current tech developments, much of his advice for enterprises looking to develop their data maturity involves pragmatic baby steps and basic mindset shifts. We talked about: his work in the consulting world and his organizing work around the knowledge graph community to improve awareness of the technology the need to find "foxes instead of the hedgehogs" in enterprises when you're trying to promote adoption of new tech the relationships between different AI tech, like LLMs and knowledge graphs, and the common connection they share: data the importance of having mature data practices in any enterprise how even simple metadata practices in common tools like spreadsheets can support better enterprise data practices how sidestepping the formal org chart and forming guerrilla teams can advance data practice the benefits of starting small in any knowledge graph project how representing organization knowledge at a high level in a knowledge graph can help solve big enterprise problems how a knowledge graph gives you a multidimensional Tinker Toys set to model and understand your org's data the benefits of moving from tabular thinking to graph thinking his frustration with the current framing of AI as being solely about machine learning his observation that practices across any org - content, knowledge management, data management, business people - could benefit from long-standing standards and proven technologies (that might not be as sexy and topical as LLMs) Alan's bio Alan Morrison is a longtime analyst, writer, advisor and podcaster on advanced data technologies and emerging IT. For 20 years at PwC's R&D and innovation think tanks, Alan identified emerging technologies on the cusp of adoption, assessed their business impacts, and advised PwC's clients on innovation strategy. Before PwC, he was a semiconductor industry market analyst and forecaster, a retail site location analyst, and a US Navy intelligence analyst, Russian linguist and aircrewman. For the last five years, Alan has been a contributor on knowledge graph and related topics for Data Science Central. His writings over the years have covered dozens of different technologies. Connect with Alan online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/TXOWWjM-DBc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 5. You might think that the lofty perch of multiple decades in industry-analyst roles would inspire grand visions of tech transformation with leading-edge technology. Quite the opposite in the case Alan Morrison. He shows how enterprises can advance their data maturity by cultivating basic graph thinking in their organizations and by taking small, pragmatic steps like adopting established standards for interoperability or simply adding metadata to a spreadsheet. Interview transcript Larry: Hi, everyone. Welcome to episode number five of the Knowledge Graph Insights podcast. I am really happy today to welcome to the show Alan Morrison. Currently, he's a contributor at Data Science Central, a well-known publication in the field. He's a freelancer and consultant around knowledge graphs and a lot of other areas as well. His background, he comes out of the consulting world. Most recently before his current role as a freelancer and consultant, he worked for many years at PriceWaterhouseCooper, the big consultancy as a senior research fellow. So welcome, Alan. Tell the folks a little bit more about what you're up to these days. Alan: Hey, Larry. Great to talk with you and folks should know that you and I have some history together as a part of the Data Worthy Collective, which is like an informal meetup group, collaborative thinking going on every week, and it's been great to know you over the years. What I'm doing currently is trying to help the knowledge graph community, in particular, gain more visibility, gain more traction in the enterprise, and it's a long haul. Enterprises are slow to change. It's like turning an oil tanker on a dime. It's very, very hard kind of thing to do, and there's so much legacy involved. And so when I was at PWC, we worked with a lot of large companies and you'd look for pockets of innovation and you'd look for the foxes instead of the hedgehogs, because the foxes were the ones that were curious about doing things different ways. The hedgehogs were the ones who were expert in doing things in one way. Alan: So we had this kind of guerrilla approach to innovation, and we also worked with the centralized innovation group inside the firm to try to help the firm itself modernize. And so in my last five years at the firm, I was plugged into the AI efforts that were emerging because machine learning was becoming much more feasible. And so I've taken this knowledge that I have of AI and the semantic web so-called, an old term, but it still has utility, together, and I'm just trying to help enterprises see the advantages of these things and adopt them to the extent they can be. Larry: You just said how hard, notoriously difficult it is to get enterprises to think and act differently, but they're all jumping all over AI like it's the best thing since sliced bread. But there's a lot of opportunities there, it seems like, to not ride the coattails, but to enter the conversation around these new technologies. And there's a lot of interplay between generative AI and machine learning and LLMs and all that world and the knowledge graph world. Can you kind of stitch those worlds together for us a little bit? Alan: Yeah. I think it's good to do that. It's good to step back and say, "What are we trying to do here? How are we trying to do it?" We've got some piece parts that are talked about in the media at infinitum. Generative AI is just all the time in the conversation because it's a powerful interface technology, as it stands, and some enterprise providers are using GAI as basically a front end, and then they will connect their own backend. And so I think you have to think about generative AI and other kinds of AI in the context of this bigger picture, and it's all driven by data. And when we say data, we don't just mean binary bits, I think we mean ideally contextualized information, knowledge, getting wisdom and decision-making capability to the right point where it's actionable at the right time for the right purpose. Alan: And so it's a distribution problem of knowledge, essentially the right kinds of knowledge. And so you really have to think about data, and this is where I get passionate about it, because the information, the heart of it is in this contextualized environment that should be being built, and it should be an organic kind of effort that involves both humans and machines. And I'm a woodworker, so I think about machine learning as a kind of a table saw. And so the knowledge is the wood that you're working with, it's an organic thing. And so you're using all this different kinds of tooling. I got a lot of different kinds of tooling in my workshop, and I'm not a great woodworker, but I think that the source of the wood is really important, what kind of wood you're working with. And we could do all sorts of things if we had the right resources inside of enterprises. Alan: Enterprises are really starved for good data. They just are not in the habit of collecting it very well. I was in intelligence in the Navy when I started, just collecting voice traffic and analyzing it. And it was so systematic about how the data collection happened. There was this whole data lifecycle environment that we were a part of, and everybody was managing according to the needs of that. And I just think that that was a very effective way that enterprises could take advantage of to really collect what they need to and just understand if you're going to digitize things, you have to have this continual process of collecting and analyzing and managing this information. And it has to be organically constructed so that it's scalable and it does what you need it to do. So that's basically where I've been focused over the past years. Larry: Yeah. The way you described that is so evocative of, I love the word working analogy, but also your military experience. I think any enterprise would argue that they would claim to attribute the same importance or similar level of importance as naval intelligence data about whatever you were researching. And yet, they have these sloppy data practices, or if not sloppy, at least not thought-through and sort of suboptimal. Can you talk about two things? One, how could they be better at that data hygiene and that data practice that you just described that was so well entrenched in your Navy days? And then in particular, how knowledge graphs and the whole world of semantic tech can help you do more with that data, and is it a prerequisite to have that good data hygiene before you can do the cool stuff with knowledge graphs? Alan: Let me start with the last question first. It is a prerequisite to have a certain amount of data maturity. When I was at PWC, we had a data maturity curve, and it was frustrating to see that most of our audit clients were not terribly high on that maturity curve. I think the tooling gets in the way. There's so much siloing that has gone on over the decades, and so many folks, including me, are in the habit of just using certain tools. And so the learning curve for learning something new is a bit steep.