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Ashleigh FaithKnowledge graph technology can help content programs in many ways: to aid content discoverability, to discover valuable insights in existing content, and to build transparent personalization programs that build brand loyalty and foster customer trust.
Ashleigh Faith has worked with content and knowledge graphs for more than 15 years and has a knack for explaining the benefits of the technology, most notably via her very popular YouTube channel.
We talked about:
- how structured content aids in content discoverability
- how to help both machines and humans understand the content in documents
- the right way to benefit from machine learning in content practice
- the difference between AI and machine learning
- what a knowledge graph is, how it works, and it can help you infer and extract meaning from content
- how knowledge graphs enable linked data (not necessarily Linked Open Data)
- how a knowledge graph used as a data fabric or a data mesh can quickly connect disparate data and content sources
- how these technologies make humans more effective at what they’re already doing
- the differences between table thinking and graph thinking
- the trend of relational databases to add graph componentry
- how graph technology can help you find connections between assets that you could never find with traditional technology
- how knowledge graph technology can capture human knowledge and turn it into actionable business activities
- how knowledge graphs help with content personalization
- how a transparent “personal knowledge graph” – a collection of interests that your customers share and your content supports – can build brand loyalty and foster customer trust
- the importance in the midst of all of this fancy content, taxonomy, and metadata technology to stay focused on the human beings you are serving.
Ashleigh’s bio
Ashleigh Faith is the Director of Platform Knowledge Graph and Semantic Search at EBSCO, one of the largest global academic search engines. Her focus is bridging the gap between users and content. She has her PhD, focused on Advanced Semantics, and she has worked in the search and data community for over 15 years. Her main focus is knowledge graph, semantic search, and general information architecture.
Connect with Ashleigh online
Video
Here’s the video version of our conversation:
Podcast intro transcript
This is the Content Strategy Insights podcast, episode number 121. You may have heard about knowledge graphs from a friend in data science or enterprise governance, or maybe in a news story about the latest trends in scientific research. Ashleigh Faith wants you to know that content programs can also benefit from this powerful technology. Whether you’re trying to make your content more discoverable, looking for fresh insights in your existing content, or building a truly user-centered personalization system, knowledge graphs can help.
Interview transcript
Larry:
Hi, everyone. Welcome to episode number 121 of the Content Strategy Insights Podcast. I am really delighted today to welcome to the show Ashleigh faith. Ashleigh is a long time… Well she’s worked in publishing and content for a long time doing really fancy technical stuff and graph stuff. She also does a brilliant YouTube channel that explains the stuff that she works on all day. So welcome to the show, Ashleigh. Tell the folks a little bit more about what you’re up to these days.
Ashleigh:
Wow. Thanks, Larry. So, yeah, I’ve been working in content for quite some time. I’m at, I think 15 years now, and I’ve always worked in and around publishing. So, making sure you understand the importance of structured data behind the scenes of the content, right? Like you can’t do any machine learning without that. You can’t do any enhancements without that. I fought that good fight for a long time. Still am to a certain extent. And now I primarily focus on on search because content is fabulous, but if you can’t find it it’s not going to do you much good.
Larry:
Yep. Yeah. No. And you just said a lot right in there. I want to tease out one of the first things you talked about was you talked about data and the importance of having access to that for tasks like machine learning and optimizing search functionality and stuff. Can you talk a little bit about that? How you help make content more discoverable with . . .
Ashleigh:
Yeah, well, just starting with the schema itself, I know XML is not the thing nowadays that people really like a whole lot. It’s mostly JSON documents, but looking at Kurt Kaggle, he’s saying it’s coming back. So, maybe we got to look at it again. But all of this to say whichever version of schema you want behind your actual content, it’s incredibly important to have well structured information there because just as an example, if you’re doing any machine learning and let’s say you have a very large article or very large asset, it’s going to take you more time and money to process that. And you’re probably getting a lot of noise instead of the targeted information that you want the machine to really pay attention to. So instead, if you have good structured data behind the scenes, you can target. It’s called zoning. You can target your machine learning model to specific areas in the content that you want to pay attention to the most.
Ashleigh:
So for instance, in things in the journal space, like academic journals, the very first paragraph and the very last paragraph are usually the summarization of what are we trying to achieve? What did we find and why is it important to you? So the other stuff in the middle is really good, too, but from a machine learning standpoint, you probably only need to look at the first two things there.
Larry:
Right. But there’s also, and I’ll try not to go too far down this tangent because I’m a structured-content person. But you look at that first and last paragraph, and you can infer a lot about what that article’s about, but there would be, are there other… How do you evaluate? There’s probably data and tables and things built into that that would be of use to other people. Can you talk a little bit about how you tease out the helpful content that can help ML do its job better and help people do stuff better out of those kind of documents?
Ashleigh:
Yeah. I mean, that’s a very good point. I mean, if you’re looking at a table, depending on your use case for machine learning, you might not want it to look at those tables. It might get a little confused. Especially in academic publishing there’s a lot of things that you would think were data, like an equation, for instance, but it’s actually just an image. So you have to be careful of some of those things, because the machine’s not going to really understand that. We also like to make sure humans can understand what is on the page effectively. So sometimes we’ll have call outs or other things that are visually appealing in the full text. Well, machines don’t understand that. If you have maybe a multi-column layout and you don’t have it correctly tagged, then the machine is literally going to read the whole way across the page and not have a clue as to what the actual content is saying.
Ashleigh:
So those are just some examples. If you’re trying to pull out some of those images and do some image recognition, there’s a lot of great tools out there. A good one that I’ve used that you can just go and try out if you want right now is Amazon Rekognition. It does so much more in the last few years. It can identify a person versus a dog. It can identify if something is offensive or not, depending on which geographic region you’re in and what that means to you. There’s a lot of really cool stuff that you can do with machines, if you can extract those images. But if they’re not tagged appropriately, your machine is not going to be able to pick up on it. Machines are no different than very smart calculators, right. They’re just tools for us to do our jobs better.
Larry:
Yep. And all of what you’re… Not all, but a lot of what you’re talking about right there is like the difference between the data that’s embedded and encoded and included in a document and the document itself. And I think a lot of CMSs and a lot of publishing and a lot of online content stuff is still very like page-level, document-level stuff. And a lot of what you’re talking about, you kind of have to get below that and get down to the… So are we talking mostly about metadata or you’re talking about actually extracting facts from these documents themselves?
Ashleigh:
So both, both. So, having well structured data on this back end is incredibly important for all the reasons that we just described. But also if you’re trying to extract something that is meaningful, like what are the most impactful sentences or there’s this great tool that’s free out there. I think it’s called Consider that you can take a few documents that are about similar things and there’s a taxonomy, is super helpful, and assemble what are the main arguments from those articles and synthesize them. Now I am one that believes if anybody tries to sell you on a tool that can write human-like abstracts, they’re lying. Most of them are not very good and they seem little uncanny valleys. So just be careful of that. And you want to be transparent if you do use those because people get irritated when you don’t disclose that they are machine created.
Ashleigh:
But that’s the power of what we’re talking about here. I’m an advocate of saying that today in our world, content the way we would traditionally define it is just a wrapper of data for that particular asset. Everything inside of it is data. And it should be so that you can use it in multiple ways, which is going to give you a bigger bang for your buck.
Larry:
I got to say, I love that. I’ve seen a number of blog posts lately trying to define content and defining content as a wrapper for data. That’s a great one. But I think it’s one of the important roles of that. And to what you were just saying about the auto-generated thing, did you see The Economist about a week or two ago? They wrote their lead story with AI and were completely transparent about it and pointed out, I think to what you were just saying, that it did a serviceable job, but it had some facts wrong. It used a 2019 Wikipedia page to identify somebody who is no longer…
Ashleigh:
Oh, it’s getting so much more sophisticated. You’re right with that. But the way that I think about it is it depends on how you’re using that machine learning. So for instance, GTP-3 is something that a lot of people are touting. It can write poetry, but it’s not extracting things from human generated stuff. It’s actually using human generated content to learn from and then create something on its own. That’s very different than saying, “I am reading an article that is human written, and now I’m going to try to mimic that human in an abstract,” right. So it’s a little different linguistically. So yeah, I mean, if you want to go out and it’s really funny. I’ve seen some of the AI creating recipes. That’s really funny. And over the weekend I actually made one of the recipes created by AI. Wow, was it salty. That’s all I can say.
Larry:
As soon as you said that, I was thinking about The Economist example and then also in the news is shortly before we recorded this is that Google engineer who got in big trouble for thinking that the AI agent he was working with was sentient and yeah. It’s-
Ashleigh:
Yeah. So you’ll notice I say machine learning and not AI most of the time. So AI is a true form of science. Machine learning is a subset of it. In my mind and I know there’s a lot of other opinions out there, but I think that we over aggrandize what machine learning actually is. When you look at the stuff that’s on TV, everyone that’s in the content space wants to say, “Yeah, I do AI. Yeah. That stuff that you see on CSI that figures all this stuff out. I do that.” Now I’m being facetious here a little bit, but there’s a sexy piece to it, right? I want to do the sexy thing and it’s not as glamorous when you actually say, “Well, machines are no smarter than a three year old,” true story.
Ashleigh:
The smartest machines on the planet, which are these big brain, quantum computers are only as smart as a three or four year old, which is still pretty smart. But then when you also remember that adult cats are also as smart as a three or four year old, think about that for a second, right? The smartest computers are a bunch of cats. That’s kind of the analogy you can make here. No, it doesn’t mean that it’s not important. It’s incredibly important, but you have to make sure that you are setting expectations when you get into it.
Larry:
Yeah. No, and I think if you just read the media, you would have unrealistic expectations often. And to what you were just saying, a lot of this, the application of a lot of this stuff is like, I don’t know how much of it’s realistic now and how much of it’s for down the road, but you’ve got some very pragmatic examples of stuff that you’re doing today with these technologies and insights. Can you talk a little bit, because we talked before we went on the air about query expansion mapping, stuff like that. Can you talk a little bit about site, like what you can do here and now with this?
Ashleigh:
Yeah, for sure. So, one thing that is incredibly important to any kind of machine learning that you’re doing and just data in general is knowledge graph. So imagine a knowledge graph as a data format that actually gives either your search engine, your human engineers, or any of your machine learning context. That is what every machine learning algorithm is desperate for. And that’s why you see people feeding the machine tons and tons and tons of data and content is to give it more and more context. Or you could use a supplementary, right, which is knowledge graph. So what that means is when you have two separate things, right? So they could be a regulatory document and an R&D document that your staff is creating. Or if you’re in a taxonomy space, you have two taxonomy terms and they have some relation to each other. In both of those situations, humans understand how those two things are related, explicitly.
Ashleigh:
We completely understand. I need this regulatory document because when I’m developing this R&D thing it’s required. Got it. I know that. That’s where the data governance stuff comes in sometimes. And then if you’re on the taxonomy side, when you’re trying to make things discoverable in a search engine, well, let’s see. IoT and the Zigbee standard. Why are those two things related to each other? Oh it’s because Zigbee is a standard for cybersecurity, which is important in IoT. That’s not in the data though, traditionally, right? So that’s where knowledge graph actually shines is you can actually build into your ontology or your graph structure if you’re not using an ontology, all of that implicit information that we all have in our brains as humans so that first, the machine understands it better. It has that context. That’s great for query expansion. It’s great for recommendation engines, but it’s also good for the machine learning in general, because then it understands the difference between things and how they relate to one another.
Ashleigh:
But it’s also helpful for other humans, right? The knowledge acquisition of people synthesizing all of that into a readable document, which can be generated out of a knowledge graph helps you onboarding people, right, like you understand the connections between things that you can teach others, and you can take some of that business logic, bake it into your knowledge graph as rules, right? Why two things are related to one another and then you pass it on to your developer so they don’t have to worry so much about the business side. It’s something that they can just use the document, the data that you’ve given to them to make decisions on.
Larry:
Right. And when you talk about relationships like that, I think about linked data and the ability to swap information like that. Not just like not have to explain it to your engineers every time they do something, but also being able to do bigger stuff. Can you talk a little bit about how this technology helps with that?
Ashleigh:
Yeah. That’s really the second use case that is incredibly helpful to anybody dealing with data, because we all have data acquisition. We all have data migration. We all have data mapping. All of those things can actually be helped by knowledge graph. And a lot of it, as to your point, is using linked data. And this doesn’t necessarily mean linked open data. So if you look at the W3C standards on linked data, it is making a URI which is a pointer to a piece of data. And what happens with a graph database is it intrinsically knows how to use that linked data.
Ashleigh:
So if you have two different databases and they both have different information in it, instead of having to do this big lift and shift making this giant canonical model, which I’ve been there too, it’s no fun. I have been in projects where it spans over multiple years and the money goes very, very high.
Ashleigh:
You don’t even want to know the numbers on this stuff. Sometimes it’s some of these larger corporations. Instead of how you do all that, you can actually use this knowledge graph. You can call it a data fabric or a data mesh depending on which version of things you want to go with. But it basically is the connection between all the different databases. So you don’t have to keep picking up all the data. You can actually just keep pointing to it so at query time, you get the data you need when you need it, instead of having to completely recreate the wheel.
Ashleigh:
So, why does this matter? Because it actually shortens the time of migration and mapping. So in my past, I have used this from going from a three month period of time for a mapping project, basically a lift and shift and creating canonical model and do manual mapping and all of that, to a three day time period where we had instead of this giant army of people, we could then use those people to go and curate better content and do all this other stuff that uses their super brain power. And instead, we went to having one person do this, this process of mapping because we were pointing to the data that already existed with that linked data link.
Ashleigh:
The other thing that, and this is something that you can look up is, I believe it was… Who was it? It was Jaguar that came out recently that they had a query that would run on their supply chain information. And if you think about publishing, we have lots of supply chains, right? Like how do you get content through the pipeline? It’s a supply chain. So, Jaguar went from having a three month… Or no, I’m sorry. It was a three week time period to run one query on their supply chain information. After going to graph, I think it takes them 45 minutes.
Larry:
Wow. And what is it that makes things that much faster because you just have machines doing instead of human beings, trying to tighten things up, or is it more than that?
Ashleigh:
There’s some of that, there’s some of that, but again, I’m a huge advocate to say you don’t get rid of the humans. You make the humans more effective at what they’re already doing and allow them to do more things, right? That’s the specialness of being human. We can figure things out a lot better than a machine. But what the mapping does is, so for instance, if you know that one database is using, let’s just say, schema.org and another is using, let’s say JAX you can actually map those together so that when you’re bringing in the data every single time, it already knows the differences between those things. You might say, “Well, that’s mapping, Ashleigh.”
Ashleigh:
It is but every time you bring something new in to a traditional mapping, it’s a one-to-one mapping, right. So you have to keep updating it over and over again. Once you get past three data sets, it is insane how much maintenance that takes, or you have a central node, which is the class in a knowledge graph and you map them all to that one node, which makes everything you do way more effective and the maintenance is so much better.
Larry:
And everything you just said describes how machines can help us and free us up to do the fun and creative part of content work-
Ashleigh:
Exactly.
Larry:
… rather than all that mundane, because I’ve done lots of migrations and stuff where you’re… And the thing about that too, I think we’ve talked about this a little bit, but I’d love to just hammer home that the difference between table thinking and graph thinking, because everything I’ve ever done has been in a spreadsheet until the last year or two. And did you make that shift or can you talk a little bit about that mindset shift from tables to graphs?
Ashleigh:
So a good way of thinking about it is start to turn your tables into graphs, even if you’re using Excel. So the way to do that is you start to look at how things are related. So let’s say books, right, or assets of some sort that are published. They have authors. Well, how is that book and author related to each other? Well, we all know it’s because an author writes or creates a title, right? A machine doesn’t know that. You probably have titles of articles and authors in your table somewhere.
Ashleigh:
So what is the relation between those two things? And when you start to tease that out, you start to see how… And this is my big piece of advice if you want to start to get into graph is, find your MVP query that you do today in your regular table data and try to update just that one query with graph. And it doesn’t even have to be a true graph database in like the big overhaul. Don’t worry about that so much. You can use a lot of open source tools. Grapho is a good one that you can use. Another is WebProtege, not regular Protege. Regular Protege is really hard to learn for most people. WebProtege has some good type-ahead stuff that’ll help you.
Ashleigh:
So start to look at how graph would translate the tabular data that you have today into this graph-like framework for that one query. And I guarantee it’s going to be faster for you if it follows the main rule for graph data. And that is, it has relations. If you have to do more than two to three joins for a typical query, it is likely going to be helpful if you look at graph to see how fast they are in comparison.
Larry:
Right, because, and I think isn’t virtually all modern current publishing technologies built on relational databases? And so-
Ashleigh:
Exactly.
Larry:
… you need a layer someplace. That’s what you’re talking about with the data mesh or the data fabric or knowledge graph or something that does that easier connection because I’ve written a lot of SQL queries and my skills would fizzle out. I wouldn’t even try to do what you just described.
Ashleigh:
Well, the other thing is, if you look even at the trends, the traditional relational databases, every single one of them is adding graph componentry onto them. And it’s because they realize that not every solution is going to be graphs. That’s very true. But a lot of things in publishing do end up being helpful to have graph. Again, it’s not essential in all cases, but I always encourage people to look at it from an efficiency perspective and understanding things that they normally would not be able to see. So a good example of this is, I was working with a very big aerospace company that had lots and lots of assets. They were trying to find new markets for some of the R&D projects that were coming down the pike. In fact, they were trying to look for specific startups to buy to help them with that endeavor.
Ashleigh:
And so we started to look at, again, a very small data set of the content that they said was MVP. And what we started to find was all of that content was about aerospace materials. Okay. And so I started to then look at what additional data sources even out on the web. So this would be linked open data, the stuff on the web, was talking about materials. So I brought some of that in and we started to look across the full corpus of information they had and what we discovered was aerospace materials… This is something they could not see otherwise because there was too many hops between the data that we were looking at. Hops are like joins for instance. And what we found was a lot of aerospace materials were actually used in medical devices, something that this big aerospace company would never have looked at before.
Ashleigh:
And so overnight they found an entirely new market for the content in the R&D that they were producing by just connecting a few dots. That’s basically what a knowledge graph does. And I know that it increased their revenue just on their very small publishing of standards and regulations and stuff that they were contributing to, about $500,000 a year in additional revenue that they didn’t even know they could get at.
Larry:
Wow. And you hear that all the time as like a generic benefit of graph technology is discovering things you would never have known otherwise. And I’m thinking about like, again, back to that freeing up people to do the more creative work, can you think of publishing or content use cases where that has helped? Instead of research drudgery, you’re doing some exciting new…
Ashleigh:
Yeah. Well, the way that I think about this is, In publishing we usually have a lot of really amazing metadata folks. They are subject matter experts in something. So imagine you go into a kitchen supply store where they help you redesign the kitchen of your dreams. That salesperson, they’ve got experience, they understand their customer. And so if they see, let’s say a couple walk in and they see the way they’re dressed and the way they interact, maybe they say, “Well, I know other customers just like this and they really love that farmhouse-look kitchen.”
Ashleigh:
That information right there, that’s something that you can actually use in your content, right, in your knowledge graph where think about all of this expertise that your staff has from all of their experience, all the degrees, all of the things that they have ever worked on and having that special sauce that they all understand and giving them an outlet to make that a reality so that they don’t have to now sit down and read through all of the 500 documents that they retrieved or piece of information to actually create something or to derive meaning from something.
Ashleigh:
Instead, we’re giving them a tool called a knowledge graph that will tie those things together. It maps into the things that they understand in their brain because they have helped the machine understand that for them so it can help them, right. Machines again, are not that smart. It’s all about the people behind the scenes. Frame them up to do more of this, it’s going to make it even better for them and for whatever you’re trying to derive from your content itself.
Larry:
Yep. And that use case, you just described a couple walking into the store and knowing what to give them, that you just described personalization, which is a huge concern in the digital content world now. Do you know of examples of like… I know I’ve seen a few things here and there, but are knowledge graphs helping with content personalization that you have?
Ashleigh:
Yeah. Oh yeah. Yeah. So, I mean, obviously there’re recommendations and that sort of thing that has a personalization element to it, but a new thing that’s coming down that, mark my words, it’s going to be the next best thing in search and in content. And a lot of people aren’t really talking about it in this way quite yet. And that is a personal knowledge graph. Now, if you go and Google it, you will find a few different versions of what this means. The way I’m talking about it is you have, let’s say a knowledge graph of interests that your content supports. Let’s say it is theater productions and what they’re all about or movies and films. So that kind of media entertainment.
Ashleigh:
If you have something that’s already connecting that information together, that’s just a normal knowledge graph. But now what if you allowed your end user to understand how you are connecting their data into this larger understanding, and not only are you going to do that, but you’re going to be transparent and you’re going to say, “We know you own your data. We know that you as a consumer, you know more about data than you have ever known in your entire life, that this day, you know more than any other time in your life, and it’s going to continue to be that way. We’re going to give you that transparency and you’re going to have the ability to tweak the way that we are doing our algorithm or our recommendations, because we want you to have a good experience as well.”
Ashleigh:
This has been shown already to improve brand loyalty, because now they trust you and the brand is saying, “Hey, did you know, here’s this data and this is helping with that brand loyalty.” But also that trust factor that is so incredibly important nowadays is what are you doing with my data? What is it that you’re collecting about me and making sure that the end user understands that they have a say in this is incredibly important and you’ll notice Google snuck something in on all of us.
Ashleigh:
So if you go into Google today and you do a search, there’s little three dots next to some of your results. If you look at that, it actually starts to now being more transparent and tell you why those search results are coming up for you. This is the predecessor of true personal knowledge graph. And if you’re looking at this from a personalization perspective, it helps you make better search engines for a specific person. It helps you give them that inquisitiveness like, “Oh, that’s kind of cool. Why do you think I like that? Oh, I like that. That’s kind of cool. Let me play around with that.” It actually helps people stay on your product pages more often because they are exploring things that they’re like, “Wait, why are you doing that?” They’re trying to figure it out the whole time. And so it’s actually kind of fun and helping people get familiar with their data at the same time. So that’s what is really exciting that I think is coming up and sooner rather than later, I think people are going to really start to pick up on this.
Larry:
No, that makes perfect sense. I’ve explored personal knowledge graphs from a number like all the personal note taking people. There’s all that. There’s a whole time. . . different. Yeah, yeah. All that kind of stuff. But you just probably described a way more adoptable and immediate use case for the notion of a personal knowledge graph.
Ashleigh:
Exactly, exactly. I have a video on my channel that talks about, oh, there’s three like kinds of personal knowledge graph context. And I think this one that I just described is, to your point, it’s so much more relatable. People understand it better.
Larry:
And it sounds like you can gamify knowledge stuff with it, which seems like a good idea in the 21st century.
Ashleigh:
Well, and we’re always talking about intent. What is the intent behind the query? What is the intent of the customer? Why are we all guessing? Why don’t we just ask them? Right.
Larry:
I love that. It’s like, yeah. Did you think about asking? I love that, right. That’s brilliant. Yeah.
Ashleigh:
Yeah. I mean, no. You’re not always going to get a data specific person that’s on the other side that’s going to understand how their data connects together. But again, I think we’re doing a lot of disservice to a lot of folks out there in the market. They really understand their data a lot better and giving them maybe not giving them the full view, right. You got to keep some things as your secret sauce behind the scenes as to why they’re getting what they’re getting. But why not say, “Hey, did we get this right? No. Okay. How can we do it better?” People value that too. They want a good experience. And if they know how you’re using their data, they’re more willing to give it to you.
Larry:
Yep. No, I love the transparency and the utility. I mean, the main thing is what are we doing all this for? Oh, so we can serve you better so you can get the right pair of shoes or the right new car or the right information that you need. Yeah. Hey, Ashleigh, I can’t believe it. We’re already coming up on time. These things always go way too fast, but I want to give you a chance. Is there anything last, anything that’s come up that you want to elaborate on or just, you want to make sure we get to before we wrap up?
Ashleigh:
Yeah. I’ll say the one thing that I am always floored at in the content space that it just seems like there’s some people that just totally get it and others that are missing the mark, and that is, at the end of the day, we are creating content for people. Don’t ever lose sight of your end customer. And I know that sounds like so cliche, but especially when I talk to a lot of the folks in the taxonomy and metadata and enrichment space and the machine learning space, it’s, “Oh, this is the accurate thing to tag this content with,” or, “Well, there’s this great algorithm that can do this. And we’re getting this F score, which is the confidence of the machine learning model.”
Ashleigh:
And I’m like, “Cool. But how does your user use that? Do they agree with you?” And if you don’t know the answer to that, you should definitely try to find the answer to that because at the end of the day, we are here trying to make things better for our end user. And just because you think it’s cool or accurate doesn’t mean it’s going to help them. So always make sure you are testing your logic against your end user.
Larry:
Yeah. Now, I want to do a whole other conversation just about the human in the loop and human centered design. You just opened a whole other thing. Well, thanks so much. Oh, and one final thing, Ashleigh, what’s the best way for folks… We’ve got to share your YouTube channel and however else you’d like to be connected with folks online.
Ashleigh:
Yeah. I mean, I’m very active on LinkedIn. I post all my videos there too if you don’t want to subscribe on YouTube. And if you do want to find me on YouTube, it’s just my name, Ashleigh Faith. I think there’s two of us on there. So just make sure you look for my mug and that’s the correct one and I would love to talk to you all and hear about your pain points in this space. Because usually it’s just a big therapy session for people when I talk to them.
Larry:
Okay. I got to say one last thing. I love that you brought that up because it’s often a joke among content therapists, the content strategists that we’re really therapists that that’s-
Ashleigh:
Oh, we are. Yeah.
Larry:
Yeah. And same thing with knowledge technology. Yep.
Ashleigh:
Exactly. I am a huge advocate of data therapy. That is how we help our stakeholders understand what we do and understand what’s in their head so we can actually make it actionable.
Larry:
Nice. Well, thanks so much, Ashleigh. I really had fun.
Ashleigh:
Well, thank you. This was fun. Yeah. Thanks for having me.
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