History. I'm Chris Karagio, NBC News Radio, NBC News on CACAA Lomlada sponsored by Teamsters Local nineteen thirty two, protecting the Future of working Families Teamsters nineteen thirty two. Dot org. The information economy has a rid. The
world is teeming with innovation as new business models reinvent every industry industry. Inside Analysis is your source of information and insight about how to make the most of this exciting new eraic learn more at inside analysis dot com, Inside Analysis dot com. And now here's your host, Eric Kavanaugh. M all right, folks, welcome to the future. Indeed, your host here, Eric Kavanaugh, and the only Coast to coast radio show all about the information economy,
Inside Analysis, Folks. I'm very excited to have two experts on the call today for what is arguably the new center of gravity in the data world, and that's the data catalog. So what is a data catalog. For those who've been around for a few years, you may know data dictionaries. For example, Wikipedia, you go to Wikipedia. A lot of companies have wikis these days. They're kind of like catalogs. It's a portal that has definitions
about things. It has information about projects and entities within the organization. But of course the whole point is you want to use something like a data catalog to help your under help your organization understand what the data means. That may sound like it's simple, but when you start getting into the weeds a bit, you realize how complex it can be, especially for a complex business model, maybe for an insurance company or a healthcare company, financial services, but
even retail, even manufacturing. There are countless terms that are used in various ways, and if you have a data catalog, you can help define those. And what you really want is a collaborative effort. You want people from all around the organization getting involved in this, not just a handful of IT people try to chase down the business leaders who know what things mean. That's
how data catalogs have been built over the years. It's not a very effective mechanism because the IT people are constantly trying to track down the business people who maybe are not that easy to get a hold of. And with us today, we've got a couple experts. We were talking to Aaron Wilson of Athena Solutions and Kiribasu of Metaphor. Metaphor is a vendor. They have a data catalog that they sell, which is very interesting stuff. Look them up.
Metaphor data is where you can find them. They're on LinkedIn, They're on Twitter now called X I suppose. But let's kind of dive in, so Aaron, I'll bring you in first from Athena Solutions. You're in the business of helping companies understand their data. Connect the dots. That's really what data catalogs do, right They connect the definitional dots of information in your organization and
help you understand what all that information means right erin. Yeah, absolutely, I mean the new generation of tools, I mean, I can't I don't think it can be overstated. You know what an advantage these tools have over prior generations of metadata management tools bring in, you know, and it's the efforts that the companies have gone through with the more traditional methods, the data
dictionaries and various tools that have been used in the past. It's difficult to get off the ground, and it's difficult to get users really engaged and into the governance process. And I think what we're seeing is these tools, you know, they make it a lot easier to sort of democratize the process.
And you made a really good point there. And your colleague Alazaichen on a show I did with her a couple of months ago, and this really interesting comment that really stuck with me. She said, one beauty of a data
catalog is it forces the business to pay attention. Right. That's when you have engagements because they know, they understand what they do all day, will have some report that goes to senior executives maybe or to the board, or to someone else, even out to end users for example, and they understand
that those definitions are wrong, that report's going to be wrong. So when they understand what you're working on is this data catalog which is going to drive and govern how information gets shared and reported, that's that makes them pay attention. Right. Well, definitely, I think that. Yeah. I mean, nobody knows generally speaking, the data better than the people who are using it, right. But you know, I think the problem that we've had in the past, as I've said, is that you know, the tools
were it was difficult to get involved. It was difficult to jump right in to a conversation about data definitions. Just as an example, it was difficult to get an up to date data lineage when people wanted to see one. So I think that we're actually you know, the slide that I used in my presentation was was you know, data dictionary, the need was there, what the technology wasn't. But I think we're actually at a point where you know, the technology is there. Yeah, I think that's really important.
And Cirri Itasu from Metaphor will bring you in. You've been in this business for a while, in the data world, and you know there's tremendous value in data, but only if people understand it. And I'll throw another quote from Allah at you and just get your commentary on this. Said, in order for data to be an asset, it must be understood. What do you think about that? Curt Absolutely, And in fact i'd add to that, and understood by whom yea or the data team understands or business sort of
like the analysts populations understand. But from our perspective, you know, we care to make sure that everyone in that supply chain, right from the people who are producing it all the way to someone who's just looking at a report, can really take it in, really understand the things, simply be effective, be you know, sort of really add to that value if you will, for having that data, of using the data in the first place.
Yeah, And you know, I just think as I'm an old school publisher, so I've been in print production where you would print eighty five thousand magazines and then realize that there's a typo in the first paragraph of the lead article and you're just like, oh no, and that's not the world we live in anymore. But you think about data definitions in a printed document, well, guess what things change? Do terms come around, the definitions change.
So the fact that we have this digital world now where the repository is malleable, it can be changed over time, and it can be up to date such that anyone of logs in any time sees the latest information. I think that's I mean, it's a simple thing in a way. But that's one of the reasons why these catalogs are now so much more effective and so much more usable is because they are dynamic, They can change quickly and everyone can
get access to the latest version, right Kritt, Yeah, absolutely. You know one example that was just gooding you earlier is one of the many things that we do within our tool is allow you to basically persist institutional knowledge, meaning conversations that are happening on Slack or teams about like the evolution of data, evolution of metrics, et cetera, and literally within one click sort of persisted within the catalog and the moment it is like literally within the next few
milliseconds, few seconds, depending, it will show up within the catalog. Right, So the next time someone is asking for that term, even if they're completely separate, you know, completely different silo, they immediately know what's the most recent version of whatever it is that people were talking about or were
looking for. So yeah, right, and that's you know, that gets to collaboration and the importance of collaboration that you know, it's really kind of intense the different world we live in today compared to even five years ago, I mean for some companies even last week, right, because as you know, there's a very long tail of software and processes in businesses, and we're all inside this industry, so we're up to date with the latest developments,
but a lot of companies are not. And so the stark disparity between an old world system where there's like a printed document that one person prints and if he's on vacation, then you know, you can't even get access to it to today, where you have this dynamic, sort of living, breathing catalog of definitions that anyone can take part in. That's a huge disparity, and I almost think that the mindset change is one of the hardest for the business
to wrap their heads around. You know, we don't have to do it the old way, guys, you can now do it this new way, and look how nice it is. What do you think about that, Curt? Yeah, you know one quote that comes to mind a customer told us a wild back. He basically said, the best catalog is one when there's no catalog at all. And what he's really meaning is that, you know,
exactly like you mentioned. You know, the the IT team, for example, might have their own point of view on how things are going, but the reality of what users are doing on a day to day basis where they're doing it, no one really knows unless it's a very very well managed sort of organization. For most average organizations, different sort of centers of knowledge just keep emerging and going, you know, coming and going, so to speak. And that's one of the things that we really try to tap into.
Right you know, how could we get someone who has zero experience with the catalog. Heck, they don't even they really should not even need to know what the heck a catalog is. They get value out of the catalog. But on the other side, from the producer side, you have now
great visibility into what people are asking. It's like a direct you know, being a product manager for example, it's like having a very very direct window into exactly what a customer is doing and they're asking for pretty much in real time if you think about it. Yeah, and that's so important because you know, let's just kind of quickly get into organizational dynamics and how we've traditionally done things, and how you do things is you have meetings, right like
if the marketing team comes in, the sales team, the management team, all these spokes to come in and you go through the meeting one at a time. That is a very old school way of doing things. You can have much fewer meetings now if you embrace this kind of approach because guess what, it's just like it's adding almost a meta layer on top of existing communication
channels like slack you mentioned, or email. If you can capture email inside of an intranet, or a portal for example, you can capture with your technology if it's connected properly, obviously, you can capture all these conversations about the business. And then by using these large language models, by using the sort of gen AI components, very quickly spin up answers in natural language for what's happening in the company, So you don't have to call someone, you
don't even have to email someone. You're just in the system. You ask it a question, you'll see who's been doing one and where that's like the ultimate collaboration. What do you think you're in? Yeah? Absolutely, you know. The one quote that comes to mind or a meme I guess was
this could have been this meeting, could have been an email health. That's exactly what's happening is so many companies are obviously, you know, whether it's in slack or teams, they're having discussions about data, really evolving the data as they go along in a data day basis, and the ability to be able to take that and you know, basically persist that, like you mentioned, with large language models, but with one important caveat right, like this
is not a very generic Hey, here's your chat chat bought or you know whatever it is, a lot of these companies do like here, go talk
to chat GPT and figure it out. Well, it's not really that we of course, we use these large language models, but more importantly, we've tapped into all the other content that there is, right, all the business glossteries, all the technical metadata that we have, and so the answers coming out of the system are highly, highly contextualized and relevant to what you're doing, and not just chat GPT, you know, hallucinating, which is a
problem. I mean, and that's the challenge of just using a generic tool, as powerful as it may be, something like chat GPT. It's incredibly powerful, it's great for text generation, but there are people who misuse the tool all the time. I even saw a guy, a very single level person I think from Meta, who is ragging on chat GPT, and he said, somebody came up with the crap ratio, which is a clever idea.
I get at the crap ratio of like how good is the material you get back is good or as a crap But he gave it a very complex sort of mathematical question, and I'm thinking, dude, that's a misuse of the tool. It's not what it's designed to do. Now, I'm guessing that there are going to be better and better reasoning engines baked into these over time. That's probably going to happen, But right now it's just for text generation. So you always have to be careful about how you use the tool
and understand the purpose of the tool. You're not going to be using a data catalog to create graphics for your magazine, for example. It's just not the right use, so it is important to understand the use. But to your point here, it'll throw it back to you just for comment here in context, that kind of collaborative information is extremely valuable and it allows you to
save time. It allows for that meeting to just be an email, right, Yeah, absolutely, And you know, to your point, I think this is one of the reasons why it's really important for companies to know what they're getting into with regards to sort of adopting chat GPT, because you know, the EXAC sort of mandate will come down, Hey we should do chat GPT to get better, but it's going to be some weird chat bought experience which really isn't work, And so that's why we spend a huge amount of
time really trying to make sure the marrying of the metadata that currently exists, making really activating that through a language model is the is the way to go right, So using the right tools is absolutely critical in this equation. Yeah, and I'll throw it over to Aaron to kind of comment on this. Aaron, specificity matters. Context really matters with these kinds of technologies to these
kinds of use cases. And to hear its point, if you are capturing the metadata and managing that intentionally maybe in a RAG model for example, as long as you're leveraging that and the fact set from your information and you're just using the AI engine to spin up text, that's when you're going to get pretty good results. You're not going to get too many hallucinations when you're within
context doing what the business does. What do you think erin Well, I certainly think that, you know, based on the demos that I've seen, you know, I've seen a little bit more of Metaphor than than than he showed here today, although I thought it was a really good introductory demo and the way that Metaphor does it is really impressive. But essentially they're using the communicative ability of generative AI, but they are enforcing some context. So in
other words, you're you're asking the data catalog. You can either act, you can also add, if you want confirmation from an answer that you think might be a little bit wonky, you can jump right in and ask a subject matter expert, you know. And again being able to do it without actually using you know, an actual just using for example, Slack or whatever is it's a pretty compelling advantage. Yeah, because it sits on top. That's what gets me so excited. And you know, maybe I'll throw this
one over to you again. Aaron I was talking with the gentleman, very interesting guy, Neil Hunch. He runs the company called Silicon Foundry out of Silic out of San Francisco, and we did a show talking about a bunch of different aspects of it. Was actually around supply chain, but we were talking about information sharing and Jenai and some of these new engines. And you know, the summarization capabilities of Jenai are really impressive. If you haven't used
them, folks, you have got to use this. Take a big long document forty pages for example, loaded into chat GPT and ask it to summarize, and ask it to summarize around different threads and give me a voice for the board, give me a summary for the IT team. It's really good at doing that stuff. And what got me excited is he said that organizations are using it to leverage that eighty percent of data in the company that is unstructured. Well, let me tell you SharePoint was you know, kind of
going to do that. And there are some other ways elastic search, there are other ways that we've used technologies to kind of get at that data. But there's nothing like I've ever seen with these technologies. And that means that strange things are happening here in the world. But a real quick Aaron comment
on that, we've got a minute left for the break. Yeah, I mean that illustrates one of the one point that I'm actually curious to know a little bit more from Carrott, which is that you know, there's a back end to the governance process where you know, everybody wants to see governance teams produce some sort of report, you know, and or you know, a summary something that they can tangibly say, here's here's our product, here's you know, how well we're governing our data. And it sounds like with the
tools generate AI. You can generate those kinds of products, those kinds of reports, you know, so that the governance team now can actually present you something tangible. Yeah, go ahead, real quick, thirty seconds. I was just going to say, yeah, we are absolutely doing things like that, and in fact, we're trying to push it further where you don't even have to ask the questions, you know, Can we just tell you what you need to know. That's a little road map. Yea, we get
to that soon. Yeah. But I mean, seriously, folks, what we're talking about is the ability to ingest, synthesize, and then articulate key points about your business dynamically all day long, all day every day. I mean, that is like having a virtual assistant who knows everything that you need to know, just available at your fingertips to tell you what's going on.
Well, folks, don't touch up now, we'll be right back. You're listening to the only coast to coast radio show all about the information economy. It's called Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh, and take this to show. All right, folks, you can see why I love that song for our show. Take us to the future. Maybe that's Black Bananas as the band, if you want to look
them up. And I used my quote earlier today, one of my favorite quotes that we use for our TV show Future Proof by William Gibson, who once said that the future is here already, it's just not evenly distributed. I love that show. I love that concept. It's just brilliant and it's true. So we're diving in. We're talking to Aaron Wilson of Atena Solutions and Kiri Basu from Metaphor today about data catalogs. And as I've said, data catalogs, in my opinion, are the new center of gravity, and
in many ways they are like a metaphor for business. So if you think about what a metaphor is supposed to be used for, it's to help you understand complex concepts. We come up with metaphors to describe things so that you can figure out what someone really means by something, and that's what metaphors do. So in many ways, the data catalog is like a metaphor for business, and key it. I'll throw this one over to you and I understand your solution and how it works. So I've got a pretty good grasp on
that. But I'm just thinking here, the more time and effort people put into it, the more access your engine has two slack messages, to emails, even video calls that can be automatically transcribed. These days, you've got technologies like I think Gong is one of them, and there's Otter, and there's a bunch of these others that automatically transcribe your meetings and give you summaries afterwards. It's like whiz bang, wow, hello, where have you been
on my life? When you can capture all that, you are incrementally building institutional knowledge day after day right here. Writ absolutely. You know. The thought that comes to mind is so many people have said this, where AI and LM's are really at that, they're at the Sega console moment right now. It's so much more that happening. You know. Multimodal models are already very very impressive, but the directions that they're going are going to be absolutely
amazing. And so yeah, we're constantly keeping an eye out on what's the latest, greatest and being obviously very cautious about how do we bring it in. Just like I mentioned earlier, we want to make sure it's not just hey, here's a portal to an LM, Like no, no, here's a portal to an LM through the context of your data. Yeah, and maybe Aeron will throw out over to you. You think about some sort of
an layered architecture and up here's all the information that's being shared. And in between, you've got this data catalog, and then you've got databases and things like this underneath, and that data catalog is it's like a lens through which you can view the world, like a colleidoscope or something, but it's a lens that allows you to understand the definitions. And ideally you want it to be connected to your reporting and your analytics, your business intelligence, your whatever
you're using to analyze your data. Because if a definition changes of let's say customer, so we've figured out some new way to define customer and we're no longer including some category that we did include missing. Little changes like that can have a great impact on reporting and can give you bad numbers like duplicates. For example, if you've figured out, oh no, these are dupes. Now you have to get all these duplicates out, your total number of customers
go from one thousand to eight hundred and fifty. That's going to change the numbers of your revenue per customer, of your categorization of customers, all these kinds of things. That's why it's important to have this collaborative effort around the data catalog. What do you think erin, Well, yeah, absolutely, I mean you've brought up a couple of different issues there that the point to
how the new technology is so useful. I mean, one of which we're talking about here is lineage, right, and the idea of being able to correct errors quickly. I myself have been through in many of the people on the call, I'm sure I have been through, you know, the difficulty of trying to trace back a data error and you know, find out then you know, how many reports were impacted. There's always the big question it was just this one reporter. We've we do have a you know, a
whole nightmare of clients that were impacted. But the lineage tools, to just name one, are extremely you know, to be able to get your hands on lineage that that's accurate, you know, instead of looking at charts that you don't even know how old they are. I mean, it's it's it's it's extremely valuable. Yeah, Kira it, I'll throw that over to you, because there are some of these issues that we have been contending with for
years, one of which is out data reports for example. Now we have all sorts of really cool technologies around observability that show us when data feeds are not working properly, and they give alerts to end users, people who are delivering reports, for example, how there's a problem here, let me go check and fix it before the reporters do or whatever. They're cool things that
you could do. But the lineage and the timelineans if things are very important because something changes at a certain point, do you have the capacity to roll back to other versions and see things or how does that work? Yeah? So, well, a couple of things. We don't actually touch your data, so we're only looking at meddata, so where we're sort of incurring things
out of metadata and presenting to you. Yeah. Absolutely, We do show version histories of things that you could certainly go back and see when changes have occurred, so we can help fix all of those kind of things or certainly get you much much better insights into how to fix it by pinpointing the right places where these kind of things happen. Yeah, and that helps because, as Aaron suggested, some changes made, maybe it was made incorrectly. All
of a sudden, the reports start adding up. And this is why business analysts ask questions, right, this is why they're looking at some way there's something wrong with this looking into In the old days, it was like, good luck figuring out what went wrong if you don't have the documentation. But now you really can figure out what went wrong. And that's all very positive for being able to sort of true the wheels of business, if you will,
and ensure the accuracy and the relevance of the data. What do you think you're right? Yeah? Absolutely, So you know, the way we think about it is ours is sort of a merging of three different graphs, if you will, right, Like, there's the technological graphs, so connections between systems. There's the business graph, which is you know, glossary terms,
how they relate to objects within tables, et cetera. And then, as we call it, the social graph, right like, how are people using, what are they talking about, how are they you know, interacting with the data, et cetera. So we bring all of these things together, and so to your point, when someone needs to go back and say,
Okay, what happened at this point in time. It's not just here are the technical changes that have happened, but oh, by the way, here are the conversations that were happening about this data which led to this result. And here you go. Right, So it's a much much more fuller picture of what happened and when. Yeah, that's amazing because you can learn and I'm guessing sing and curate kurtb Fro, I'm wrong, I'm guessing that. Especially over time, you can also get a very good feel for who
knows which subject matter better than others. You could probably ask it who really understands this skew in our manufacturing environment, or who understands this region. Those kinds of questions can probably be answered very accurately. Now, if someone's using this kind of technology, right, absolutely, I would say this. You know, certainly a lot of governments people would want to specifically go out and
mark an individual or two as being the expert. But then the natural expertise that has just arisen because there are people talking about it, etc. You could easily pinpoint these kind of clusters if you will, of knowledge. Yeah, Aaron, I'm going to throw that over to you. That's a really big deal. Like think from the perspective of a senior executive who is in
here to try to understand what's going on in the organization. Maybe there's a recent merger or acquisition or something, and they're trying to wrap their head around who knows this or who knows that? To be able to ask a question of a data catalog like that, who really understands our business in the Southeast region or who really understands this product line and get some dynamically generated answer based upon conversations happening in the company. Is it me or is that just a
massively cool deal? Yeah, I think it's potentially a huge advantage. It's going to be a shift, though, and I think that's one of the things I'd like to ask Curate about, is that now you sort of democratize data and then it's no longer this top down as Eric you pointed out, you know, maybe a senior manager who goes to his manager, who goes to you know, goes to his next report down the line, this is
where is this data come from? Now you have it's been democratized, but it's it's a real it's I would imagine that companies undergo a real, uh sort of cognitive shift in terms of the way they operate. And I'm curious to actually know what he has seen, you know, when they introduce these
types of tools. Yeah. Absolutely. You know, if you think about every governance role that's out there, you look at their job descriptions, it's about like chasing down governance problems and making sure tagging is right, et cetera.
Pretty much everyone hates doing the manual labor involved in doing that stuff, but with technologies like ours, it allows them to actually be about governance right, Like they can step back all the way and say, Okay, big picture, how am I solving the problems that I'm trying to that my company is having. That the real role of governance sort of really shines through with the power of these technologies to take out the mundane that everyone has to do
otherwise and make it that much more effective. Yeah, and I think that that gets one of my favorite soapbox topics, which is morale. And I throw this speck over to you, career. Once you understand what the changes that this kind of technology brings, which are significant, but once you wrap
your head around that and understand it. I think it's great for morale because the things that kill morale are spinning your wheels, not being able to get answers to things, not being able to change what needs to be changed, not knowing who is responsible, not knowing who I can reach out to to get answers. All these things are solved at least to some degree with this
approach. So I have to think that improves morale because by and large, if people feel like they're getting somewhere, they're getting something done, they're making progress, that's excellent for morale. It's the opposite that gets them depressed. What do you think her absolutely? I mean, I think I mentioned this
earlier. The code that we hear from most of our customers, especially who spend a lot of time on legacy catalogs, is you know, catalogs are when data goes to die, and that is fundamentally one of the issues where people don't care about working on these, you know, especially antiquated systems. The UIs are terrible, all those kind of things. And now this is a whole new varadigm where heck, you don't even need to be trained on
the catalog to get value from it. So, yeah, absolutely, morale makes a huge difference in that well and in training too, So you think about upskilling training people. A catalog again, properly used, properly installed, et cetera, is a great training vehicle because it is an access point to information about business processes, about terminology. I mean you talked curate about understanding
the relationships between systems, and that's a very important thing as well. And again this generative AI concept, these technologies can do tremendous things in explaining things, explaining how these two systems interconnect, right, go ahead, cirrit. Yeah, so you know, I think this is where from a from a product perspective, the point of view that we take is JENNAI is one of the tools in our tool get right. Like the other really really critical one
is in fact, the user experience and the user interface in general. My philosophy really is like the moment you have to make a context switch and go and look up a document or documentation, etc. You mix the point completely.
So to have an experience, whether it's magical through AI or it's a very procedural thing, but having it be so intuitive, so simple to use is one of the baselines for us as we design any kind of capabilities, because we want to make sure every user gets value out of the catalog. Yeah, and you know, that's an excellent point. And this kind of gets back to another theme that I have about the UI and the point of
interaction with the business. And what I think is very compelling about what you folks had metaphor has done is that you are leveraging the places where they're already going. You're leveraging Slack because you're indexing what goes across Slack. You're leveraging email. If they get access to the email, whatever channel of communication people are already using, you're taking advantage of that, so you're not forcing people
to go into some separate environment to ask questions. To your point, I think most people know that when you're bouncing around from this app to that app to some other apps, it's disruptive. It is it's a discontinuing sort of event, and it is disruptive to the brain. I mean not that you can't do it. You can't, but it's just it's like a hurdle you have to jump across, and you've kind of knocked all those hurdles down.
Isn't that about right here? Yeah, absolutely, I mean, you know, I challenge people to find or if anyone has less than I don't know, twenty tabs open in there, for example, I'd like to meet them. But yeah, I mean, why introduce fifty other you know, browser Windows, you have to go searching through stuff, switching context, you know, hard switching context. Whereas the actual problem you were trying to solve, what's completely different. It has to do with business, but now you have
to go through all the details. So that's exactly one of the reasons why you know, slacked. We want to make sure that we can give the answers you want right there with all the context and all the stuff behind it, right. Yeah, Well, it's a big time saver, first of all, and it maintains that continuity. Know, Aeron, I'll throw this over to you. We've got about a couple of minutes before this segment is up. Analysis is a thought process and it really should be fluid in order
to be effective. And what I mean by that is if you're bouncing around from one app to another, that's not very fluid. Right. Those are sort of truncated moments or disruptive moments when you're bouncing around, and you want to have that true conversation with your data. We've talked about that for thirty years in this industry, but now you really can have the conversation with your
data. If it's connected to the information systems, if it has a data catalog layer which is making sense of things, and if you're able to use this GENAI stuff real quick, you could have a conversation with your business data. What do you think, erin, I completely agree. I mean, I think you can't. You can't overstate the value of kind of eliminating that layer right of having to go to another tool separately. You know, I've I personally, I've done it, and that's the way most you know,
of the previous generation of these tools have worked. But to be able to just you know, interact directly on teams, on slack, that type of thing, and then if you do need to provide you know, sample data and talk about the problem or jump on a call or something like that, you can also do those things too, but without that extra layer of going to your you know, your governance tool as it were. That that's exactly
right, folks. People want to work in one environment. They want to be on the phone or in a zoom call or whatever doing their thing. They want to have to jump all around, but don't touch that doll. Folks. We're going to be right back. You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh Show Fact. Here on Inside Analysis, we're talking to Aaron Wilson of Athena Solutions and Curates
of Metaphor. They are a data catalog company, Metaphor Data, and Aaron, you had a question for key Rits to go ahead. Yeah, what I was curious about from Curate's perspective is, you know, we have data
governments, governance teams. You know, most companies to deal with data have a data governance team, and this type of functionality, you know, with a product like Metaphor, really I would think completely would reshape, you know, kind of their daily existence because I'm looking at it from the perspective of so much time spent on information gathering right and curating, and you know, with with a tool, with a modern data catalog tool, this probably shifts
them more into making. It probably allows them to come up with policy quicker and a number of other things now that they've kind of got a lot of that mundane work out of a way, But I'm curious to know what perspectives you've gotten from clients. Yeah, so I think you know, it's across the spectrum. Obviously, I'll give you the high and the low side.
So on the on the low side or the very simplistic sort of side, we certainly have customers where yes, governance teams are there and they have, you know, sort of a vision of what they do, but the reality is that the company on the whole might not be fully aligned with that vision. Right, Like the governance person wants to get something done, but the
rest of the company is still trying to get there, et cetera. So we certainly have like it's helpful in that sort of realm because it, like you said, allows them to step out of the Monday and say, okay, big picture, here are the types of things I can actually get done now that I don't have to do the manual work that I would otherwise have to, you know, do it on a day to day basis. So
that's one side. The other side, absolutely we have customers where they came in effectively saying, oh, yeah, I need to be a governance data governance person and you know, I through all the documentation on the internet and it says, these are the twenty things I have to do, and oh, by the way, now I have to do like half a thing, right, or I can think much more big picture, and so a lot of times we find organizations who are much much more further advanced and mature,
so they're already on their data meash or data product, one of those sort of journeys. They've already built out some of those kind of things. They can take a much bigger view and they're effectively redefining governance. They are talking much higher picture. They're talking about like here's the data product and here's I'm going to evolve It. Nothing to do with the like the menial labor that you have to do. You're focusing on the data. You're focusing on the
actual problem and the outcomes that the company cares about. That's awesome. I mean, really you think about Okay, ours right, maybe I'll throw it over to you, Aaron. I remember when this whole concept of objectives and key results came out as opposed to just numbers and things. Because business is very unwieldy when you can get right down to it, and you want to be striving towards things, but not just basic metrics, right because if you're
just going for metrics, what happens they turn into vanity metrics. So the objectives and key results I think is pretty important stuff. And a data catalog is a great conduit to help you get there, to help you define reasonable okay ours and know if you've gotten there right erin well, it definitely would be I think what it would do. Would it would open the door to
making you know nothing against KPIs. You know, I'm sure organizations will you know, always have a use for those, but you could probably make them a heck of a lot better by being able to get your hands on maybe additional data. I mean, dashboards will be easy. It'll be easier to come up with a new data point and understand it and incorporate it into a dashboard and make it that much more useful because you can get your hands on
the data and understand it better. And you might find that users from all over the organization can participate in that process, whereas it may it may not have been the case earlier mm hmm. And that's the key point again to get back to collaboration, krat I'll throw it back over to you. Anyone can log into this, anyone. I mean, obviously they have to have
permission to do so. But having teams from different parts of the organization be able to make their comments about things, especially if folks in the field who would say, hey, guys, I noticed something and that's not correct, and you fix this all of a sudden, it's like you're fusing what we're to spirit channels. They think support, for example, versus email versus slack.
Right, the guys in the support and the girls in the support area, they're the the coal face, as they say, dealing with customers and they're going to see stuff, and historically it's been very difficult for that feedback to make it all the way up the IT ladder to the person who has the authority to change something. And that's just out the window now right.
Absolutely. I'll give you a great example of this. We have several customers who've been through this recently where you know, their old school sort of pointer of view and doing government was like, let's get coverage, so let's make sure that I don't know, one hundred percent of our data is documented, and let's be honest, most documentation in that case was like one sentence, Oh, this table does blah, and that's the end of that. So
we've sort of changed that around. What we've basically said was like, hey, look through all the comments that you're having, you know, questions coming in through Slack or support channels, et cetera, and then basically answer your top twenty five questions, meaning you literally write it out verbatim. See if the system has an answer. If you don't create, here are buttons and guess what. Here's help with AI which will help you generate that right answer
as well. And basically within a couple of hour window, you could answer the top twenty five questions and every single variation, infinite variations, because that's where the LN really shines. You know, I asked for how do you define revenue? Your version of that statement, how is revenue defined? Doesn't matter, We'll get you the answer. We know how to get to that
point. So that has been like a huge change and how like the old way of doing things have has completely been supplanted by a very outcome oriented way to get to the answers that you need. Yeah, and real quick, and we can maybe get into this in the podcast bonus segment a bit deeper, and we've got a couple of minutes left here, but I'm going to throw us back to this concept of an abstraction layer that can absorb from all
these existing channels. Right. One of the challenges of deploying a new technology is that it's a new app, it's a separate app, it's a separate log and all this stuff you have to go and do and then you know,
historically set up pipelines and things. But the fact that you're pulling from all these existing channels and allowing for all that to be baked into this data catalog that I think is one of the real key differentiators because now you're not forcing people out of slack, you're not forcing people out of email, You're allowing them to work where they work and live where they live. You're just adding this layer of value to it, which is a reconciling layer, a
definitional layer. What do you think, he rit, Yeah, absolutely, I mean it is. Yeah. The way I think about it is, you know, the stuff that we do in addition with the AI sort of capabilities, it becomes kind of like the great equalizer of SUTs. So it's no longer that you have just the data people who are the experts. And I mean, of course people are experts, we're not denying that, but the availability of that expertise is instantaneous to someone who's you know, not in
that role, and so absolutely it's it's abstraction layer. But it's more than that, it's an equalizer for for people. Yeah, that's I'll ask you to comment on that, Aaron. I think, I mean, really it's kind of a slam duck, right, if you can afford to do this and you can get it done in your organization, it's kind of a slam duck to do something like this. What do you think erin for sure?
And one of the points that you kind of touched on there is the fact that the ability for a product, you know, for a modern day catalog of product and not just work with the data assets, but also through the distribution channels. I find that particularly, I mean, I just think that's really cool, the fact that you can monitor and track how often is somebody talking about this particular data series, right, who's talking about it? What
reports is it appearing in? I mean, I guess that's the you know, it's part of this concept that people are calling active metadata, this idea that you know you have a feedback loop. Now you know what your relevant data points are, much more than you're used to under the old environment. Yeah, and just then use ability side of the equation. Who's using this? What are they talking about? You think about bubble what do they called
word bubbles? Where you see which terms are most popular. These are all useful constructs to help guide the attention of the executive or help guide the attention of the frontline worker. I mean that's the other thing is customer service. If someone's on the call, if you can, you can patch this into a customer service center. Think about it. In the call. That person needs to have whatever information is available and relevant right now. You want it
very quickly. You can't be waiting, especially if the customer is angry calling up all upset. They came be like, oh my systems are running slow today, Sorry sir. You know nobody likes to hear that. But these kinds of information flows can be extremely valuable for anyone in the organization, whether you're on the front lines, whether you're middle management, whether you're an executive. If you give partners access for example. The point is it's it's a
knowledge repository. And I'll go back to that comment I made earlier about the gentleman from Silicon Foundry that the aha moment in my head was knowledge management. Remember that guy is like twenty five years ago, before even business intelligence became a thing, it was knowledge management and it kind of went nowhere because there just wasn't the compute power. There weren't the technologies available to really make sense
of all that stuff. And now that is all here. That whole paradigm has shifted to where you can do actual, real tangible knowledge management with you in your organization. Folks. That is not a small thing. We're finally going to be able to truly leverage this eighty percent of corporate data which is unstructured. But podcast bone A segment is coming up next, folks. You
are listening to Inside Analysis. Okay, folks, time for the podcast boning A segment here on Inside Analysis talking to Aaron Wilson of Athena Solutions and Kirt Basu of Metaphor. Aaron is a consultant. If you need some consulting help, give a call curate. He's the vendor if you need a new tool, and I think everyone does. You got to get a data catalog, folks. This is good stuff called ty writ and his team parent. I'll
throw it over to you. Metadata management. You know, I remember, gosh, twenty years ago talking to a client about his metadata repository, and I was such a babe in the woods. It was like twenty four years ago in fact, I said, I'm like, you know, this is I keep talking to these vendors and they all have their own metadata catalogs.
But wouldn't it be good if there were like one metadata catalog that we all kind of adhere to, and you know, and that's like an ontology, right, I'm like a little babe in the woods out here, and he like kind of shakes his day's like, well, yeah, that would be good, I guess. But it's just not really the way things work out there in the real world. But we're kind of getting close to that. But maybe just talk real quick, Eric about metadata management, why it's so
important, and how things are changing now about how you do that. Sure, I mean, I think it's no accident that you know, some of the bigger companies that are really data intensive, and I mean, you know, if you look at metaphors origins. You know, coming out of the data team at LinkedIn, they were under enormous pressure, probably more pressure than most right to make, to get valuable insights out of an enormous quantity of
data. And you can't really do it effectively if you don't have any concept of metadata management. You have to have commonly agreed upon data definitions, you have to be able to get your hands on lineage quickly. So I think the old way of doing things. You know, you need to bring clarity to these things, and you don't have a whole lot of time to do it. You don't really have a lot of time for a governance team to assemble, for example, you know, a collection of terms or a data
dictionary. It's it's tremendously valuable to be able to kind of get to the point when it comes to metadata management. Yeah, good point. I'll throw it over to you. Here it and maybe talk about how metadata management has changed, because I mean it used to be a fairly uh what manual process and only a couple people touched it. They had to go, like I said earlier, to go talk to the business try to figure stuff out.
Now it could be much more dynamic, and it's very useful in terms of understanding who's doing what, which metadata is being accessed, what's the important metadata we got to watch out for. All those things are now much more clear because we can see what's happening right here. It absolutely so you know, I'll give you a simple example. When you're searching for, like, what's the definition of revenue. Let's say you plug that into our system. The
answer you're going to get is not just the actual definition of revenue. Of course, there's that you know, it might be specified in a glossary somewhere, or could be in a dictionary, depending on you know, what that metric is. But we will also give you other context We'll give you all the others. Well, yeah, here's the definition, but by the way, here are the you know, stated owners of these, or here are the conversations happening about. So what you get is a much much more richer
contextual answer about what it is whatever it is you're asking for. And so yeah, I mean, it's still a human doing the interpretation, but now you've made that human into a basically an analyst. Even if they were not, for example, right like they are able to synthesize much much more interesting data to get to a conclusion that they need to. Yeah, that's that's
a really good way to put it. And you know, it's funny you would say that because our show is called Inside Analysis and we our tagline is take the inside Track to Insight. But our whole mission was to enable the
audience to be an analyst. Like that's our whole job in the world is to share information about tools, technologies, processes, people, to help people understand how to ask the right question, put it in context, and they make better decisions about the number one the tech that they use, for sure, but the business and how they run the business. So I think that's really beautiful that you just said that. You spoke to our whole Reison Detras
as the French would say, so good good, good work. Final comment, Aaron, final piece of advice about how people should get started or do something with this, Well, yeah, I mean I would say that, you know, with companies that are struggling with these types of issues, you know, this is an important decision to make. This is you know what do I need? And this is the title of one of our prior presentations.
You know what data catalog do I need a lot of considerations. One of them, though, I would say this, this is that if a company has a reasonably good handle on, you know, their data challenges. And what's interesting is, you know, I would say ten fifteen years ago, most companies were consider themselves, by their own admission, very much behind the curve. But now that we've gotten you know, companies are further ahead
in terms of data warehousing and integration. And then you still have you often left with the metadata problem. You know, it's kind of what's left. It's kind of like, well, we have good you know, we we have good time to market, we have our data is better organized, but yet we don't have general agreement. We have problems around definition and lineage and so forth. These these are the kind of tools that companies may very well
be ready for if they find that those are their pain points. And that's part of what we do with Athena is to come in and do an assessment and to say like, hey, what are your pain points, and then we can hopefully line you up in terms of vendors with the vendors that go to those strengths. Good stuff for folks. Th Thank you so much for your time today for listening in thanks to Kirrit Basu of Metaphor and Aaron Wilson of Athena Solutions. We do all these webinars for the interviewing. Share it
with your colleagues. One nice thing about Zoom is the URL for the live show is the same for the archive. Relliant. Someone was thinking, that's an architectural decision. Someone made it zoom a long time ago and it was fantastic because WebEx was not like that, it was a different URL. Someone says, you know what, let's make it the same URL for usability, for consumption, all this great stuff. So check out Metaphor Online, Metaphor
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