Stronger Data Integrity for Enterprise Systems with Verl Allen from Claravine

DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!

Verl Allen 0:00
With your solution, we took that from a three month window of waiting to make decisions, literally hours. And so it’s that sort of because again, a lot of the automation and a lot of the manual work involved in getting to the point to actually do the analysis is eliminated.

Alexander Ferguson 0:21
Welcome to UpTech Report. This is our applied tech series UpTech Report is sponsored by TeraLeap. Learn how to leverage the power of video at teraleap.io. Today, I’m joined by my guest Verl Allen, who’s based in Utah. He’s the CEO at Claravine. Welcome, Verl. Good have been great to be here. Now, Claravine is a platform for data standardization and integrity platform all around content, really. You guys are focused on on on CMOS helping CMOS of enterprise enterprise specifically it? Did I have that correct? That’s correct. Yeah.

Verl Allen 0:53
And it’s primarily around the the customer experience. So that, you know, that includes content includes campaigns and marketing and advertising. It also includes products. So it’s everything kind of around that

Alexander Ferguson 1:05
customer experience. All the pieces come together now before the existence of SaaS and software in today’s context of why you guys exist. People are using what before you spreadsheets?

Verl Allen 1:16
Yeah, there’s there’s the what? Yeah, like a lot of companies out there today that exists on the south side are selling to large enterprise, it really was kind of started this problem existed. And it was being solved largely with trying to be solving the enterprise with with spreadsheets. And I think what you’ve seen happen in last 10 years is the number of SaaS solutions and the amount of data being generated has exploded, you reach a point where that solution just doesn’t work anymore.

Alexander Ferguson 1:43
columns and rows and other tabs in this spreadsheet for that I feel like you were talking about the spreadsheet almost becomes an application itself.

Verl Allen 1:53
Yeah, in a sense, it’s really saying, you know, what we had an application we’re using to create manages data. But ultimately, Excel is not a data management solution. And it’s really not an enterprise solution. Collaboration is a big part of this. And so it’s a lot about not what we’re solving is not just a technology and a data problem, but it’s also around, we think about standards, it’s around collaboration, and it’s around people. And it’s really trying to kind of manage that problem from a technology people and a process perspective across the enterprise.

Alexander Ferguson 2:28
The company started in 2013, you joined in in 2018. And some of the one of the customers, I think, you said mentioned is Under Armour. And they it’s like across the board, as far as like the content team marketing team agencies, how many people are from are they than you using your platform?

Verl Allen 2:48
Yeah, within their organization, there’s more than 100 people using our application. And it’s, and it’s not just not just employees, but it’s also like, like, you mentioned, people on the agent, you know, they have people on their agency side, as well as contractors and other people, anybody, it’s kind of connected, that customer experience is really involved in using our application, because, again, really, what’s about is driving kind of enterprise level data standards, which have, there’s a lot of far reaching impacts around that, and the implications of that importance of that what’s

Alexander Ferguson 3:16
what’s the biggest pain that you’ve seen, you know, CMOS enterprise, when it comes to this content, just data that’s around the biggest pain that they have, and they are focused on solving.

Verl Allen 3:28
Yeah, so So if you think about what’s happened, you know, over the last, you know, like, last 10 years is that everybody’s focused on automation, personalization. And, and doing it at scale. And the challenge you have is that as you start handing off those disparate decisioning to machines, that the data is not clean, and the data is not consistent, and it’s not standardized. As you’re getting data and and you’re and you’re getting data back from all sorts of different applications, the average marketing organization, the enterprise has over 80 point SAS solutions. those points, SAS solutions all have kind of their own proprietary unique data models, not because they’re they’re just architect that they’re all architected and kind of developed independent each other. And there’s not a way to kind of there’s not a unification layer around the data for that. So you’ve got in situations where you’re delivering experience in the enterprise, there’s multiple applications involved in either the creation, the delivery, the measurement of that. And really what we do is help solve a kind of data standardization and create a layer that of data in some ways think about as metadata that is able to be to enrich and to enhance the data that you’re getting from all these different applications and provide a bridge across them from a just really a data standards perspective.

Alexander Ferguson 4:47
Now, how is this is this flow happening, but effectively, you’re connecting all the different other than like average of 80, other SaaS solutions and enterprise has, it’s coming in through first clarifying and Then B people blog into clarify just to see that enrich data. Is that what it is?

Verl Allen 5:05
Yeah, really, the way to think about it is, it’s a little bit I think there’s a company called BASF. Back in the 80s, or 90s. It’s kind of the the products we make help other products work better. And the way I think about our business is we the data that customers are creating, managing and our, our application in a lot of cases, they’re, we’re ingesting data from other applications, and they’re standardizing it, the data that we create helps other applications, other analytics solutions, other processes work better, whether it’s machine learning, whether it’s bi was analytics, whether it’s optimization,

Alexander Ferguson 5:39
let’s let’s actually like just dig in first, like we keep talking about data, let Give me an example. Give me an analogy, how we understand it in play. When we talk about data, and someone’s using it. What does that look like?

Verl Allen 5:48
Yeah, so when you think about it is if, as you’re collecting, say, for example, the way that systems name data, so they may you may call on one system, you know, if your simple example, if you’re if you’re publishing an ad on Facebook, the ad server may refer to Facebook as FB, you may have another system, your your, your creative system may refer to ad placement as Facebook. I mean, it’s the simplest stuff like that the thing, that’s a simple example. But even as you pass creative IDs from application to application, those, those applications will actually create their own have their own way of creating a ID for that creative. So yeah, your your digital asset management solution, or may have a certain way that ideas using for a piece of creative once it goes to your ad server once because other applications, they create their own IDs. So how do you string across those applications that, yeah, this experience involved this piece of creative, because because the actual delivery and the actual measurement that are happening in other systems, and the and even the IDS get changed. So we start to provide a map, if you want to call it that, of that data, and then standards for that data. So as data goes down into analytics, and these other systems that their machines are processing the data, it’s it’s cleaner, it’s more clearly defined, and the data is, is consistent across all those different all those different applications that the machines can actually determine what that is,

Alexander Ferguson 7:22
for having a standard, like give me an analogy or a use case where you actually saw the problem that these enterprises are facing.

Verl Allen 7:30
Yeah, it was interesting. I was on a call with a large, it’s a global technology company to everyone. If I said a name everyone recognize it, I was on a call with them. And one of the one of the largest global advertising firms and companies in the in the in the world. And we were on the call talking with the client about the agency adopting our solution. And it turned into a situation where they, the agency pulls up a spreadsheet, it’s a massive spreadsheet. And they start talking through and they’re like, okay, we’re on, we’re using version 121. And the client pulls up a spreadsheet, like, wait, what are you talking about, we’re not on that version, we’re on a totally different version. And we don’t even have, they start going through, like, there’s budget gaps of what we have and what you guys have. So like, hold on, hold on. So they they get that figured out, we’re kind of just see you’re watching this happen. And then they they finally kinda get on the same page. And the agency starts talking about a particular field of data and said, Hey, this field, they are described what that field is, and then the clients like, Wait a second, we’re using that field for these purposes downstream in, in some of the work we’re doing on the BI side, and some of the some of the analytics work we’re doing. We actually have a bunch of different like, that’s not what that field and what you’re describing wheats, we assume that was this. So there was a complete disconnect as to as we just sit there and kind of watch this between the two of them. And we have this conversation all the time, it was pretty clear to me that this Yeah, we are sitting in these awkward conversations between the brand and the agency, and

Alexander Ferguson 9:09
it seemed all over them. Hmm. It does this sound does this seem normal to them that like, Oh, yeah, they’re they’re kind of almost used to this issue.

Verl Allen 9:17
I think what happens and we see this a lot is there’s this, how a little bit of entropy what enters which is like complacency of maybe when we’re safe, where they assume things are the way they are, there’s assumptions that, yeah, what you send us and what we’re using are, are good. Then they start to realize, well, a there’s all sorts of errors in this thing. b we interpreted what you guys sent us a couple years ago, and the person that got it is gone. We don’t know where they’re at, and they left and we’re not even we’re not even clear on what the meanings between these two are. And we’re not even on the same version of the spreadsheet. And and so but but again, these are situations where they’re using this data and inputting this data to make really critical business decisions. And there’s not alignment. And there’s not consistency or the standards we talked about. And there’s not one place in which is managed. And so if you see how it’s very easily easy for an organization that has multiple geographies, multiple teams in different channels, multiple agencies they’re using, and you’re talking hundreds of people trying to manage a business, really doing data management in a spreadsheet, it becomes utter chaos. And that’s really what we’re eliminating. And that’s what we’re, we’re alleviating. And it’s all the sorts of things that you think about that are missing from that raw data management perspective in the sense of understanding when data gets changed, who changed it, why they changed it, and all that all that all that all those standard features that people expect in a data management solution, that are just missing in a spreadsheet. And that’s, that’s that’s kind of where it starts. And then it’s and then the thing, you know, well, that spreadsheet does not connect into all these systems. And that’s the kind of you suppose the problem branches out from there, as you think about it.

Alexander Ferguson 11:02
If an enterprise cmo doesn’t have a solution like yours, how does that play out? How does that felt then?

Verl Allen 11:13
Yeah, so as you’re thinking about cross channel, optimization, a cross channel measurement of performance of you know, so let’s say you’re, you’re you’re advertising across different walled gardens, how do you how do you then measure performance across different channels if you don’t have standards in place? Or how do you measure not just advertising, but you know, email and other other channels. And really what it is, it’s, to a certain degree, where we kind of was all started was really about enabling that cross channel measurement. We don’t actually, again, we’re not we’re not an analytics solution, we really are a data standardization solution. So our data feeds into the analytics and enables those sorts of analysis to take place that right now, a lot of companies are struggling. In a lot of cases, there’s a lot of data cleanup, and a lot of manual work involved. And getting those those results. And it’s interesting. There’s one of the big pharma companies, I had a conversation with him one time and they said, Listen, before we started using clarify, it took us three months, three months to do analysis of campaigns and performance. And so we’re coming back three months later, and making decisions about what happened three months ago, the environments completely change. With your solution, we took that from a three month window of waiting to make decisions, to literally hours. And so it’s that sort of because again, a lot of the automation and a lot of the manual work involved in getting to the point to actually do the analysis is eliminated. And we see the same thing with with other folks working with there’s a big gap between I’ll call it the operations, marketing, ad Ops, and all that stuff. And the analytics, and it’s that gap between the two where there’s a lot of manual work and ETL, and data cleanup that has to happen that we eliminate a lot of that.

Alexander Ferguson 12:59
So the big result as a success is the decreased time of getting an answer to Hey, did this campaign work, all the places that we use this content that it was showed up here, and we used it in this ad is displayed over here, you’re able to get the results across the board of all your different applications all speaking together, you see the results of all of it together? Mostly instantaneously.

Verl Allen 13:21
Yeah. And that’s and that’s, that’s kind of one application is around campaigns, right? You understand that cross channel, but there’s but but you think about that data that they’re getting from experiences, they’re using it for all sorts of other decision making an organization we have, we have companies that are also looking at it saying, Hey, we’re struggling on our finance organizations to understand performance within different business units. Because the data that we’re getting back is, again, it’s coming from multiple applications. How do we how do we create a standard across so it’s much easier quicker to actually do the analysis. So So what you’re talking about with campaigns and cross network campaigns, is kind of one use case you want to call it that of how our our data is used. But But ultimately, what we’re solving is, is really kind of this, this idea of like, ultimately, what what companies are trying to get at is better quality data, results in better decisions, and better, you know, better results. And that I think most organizations, we talked them, we talked about data quality as data integrity, most most organizations are struggling to actually get the definition or that the quality of data they need to make real real good decisions. And again, a lot of decision making is moving to machines. And so if that’s not in place, either the set of data you’re using to decision often on the machine learning side is much smaller. Because you got your data, you have to get to a certain level of fidelity in that data and quality of data. Or there’s a lot of time spent cleaning that data. You know, people talk about data scientists at least 80% You know, a lot of people talk about this 80% of data science. is spent cleaning data and it’s that small leftover sliver of 20% is left to do analysis, which is what you really hire them to do. And so we’re trying to eliminate a lot of that kind of data cleanup.

Alexander Ferguson 15:11
So you’re the majority of solutions and cut out there is taking it after the fact, yes, let’s make the data, let’s clean it up, what you’re trying to do is get in front of it, where those who are creating it or in the content, you’re creating standardization.

Verl Allen 15:27
Yeah, so that’s a great way to think about it. So historically, the way we solve kind of data quality is it’s it’s been a reactive approach to creating data quality and data, right, we take a very different view that and we look at us, it’s much more about a proactive way, at the at the thing about I think about the automakers in the 70s. Right, as cars rolled off the line, until they really built Total Quality into that process. They were fixing doors falling off, and you know, things not working out in a parking lot at the end of the supply line, excuse me, the manufacturing line. When they put quality into the manufacturing process, and us putting data standards on the front end of this process, with the business teams, it ends up driving quality throughout the process. So by timing, the data ends up in the in the you know, in the analytics solutions, or in the data, you know, the cloud based data lake for EMI ml and AI quality is that the quality is kind of built into the data and that that set of data we send along with the behavioral data is an added set of data or metadata, if you recall that that they can decision off of and it really waterways a bridge between the quants side of the house and the creative side of the house.

Alexander Ferguson 16:44
Is that something that you’re setting up manually for enterprises, those connections and creating that standardization for them? Like, let’s let’s come with our common knowledge here, this program here is going to bring in this data is that something that you just say, here, were all your connections? And it does it automatically? What’s that process?

Verl Allen 17:00
Yeah, so a lot of it is we have we have situations where customers are, again, you think back historically, people were managing a bunch of data, operational data, strategy, strategic data, information about what experiences were meant to be, what was happening, the organization, they’re managing a bunch of this data in spreadsheets, we’ve pulled that into an application. And there’s a bunch of governance, if you want to call it around that. So as you’re creating data in our application, or you’re ingesting data into our application, what we what we have is we have a standard that which that data way that data should look, the data is coming in, and it’s not correct. We identify where there are problems and we either fix it in an automated way, or we surface it to allow the end users to correct that information. And so that’s that’s really, to a large degree, what we’re doing is helping to ensure that the data that we’re creating the data is being collected and created our application conforms to a standard.

Alexander Ferguson 17:58
For you personally, like your your background came from Adobe. Right? So you’ve, you’ve been in the enterprise space and understanding the maybe some of the challenges. Why are you passionate about this?

Verl Allen 18:15
Because you know, as I as I was at Adobe, I spent about 12 years at Adobe and I came to Adobe via the acquisition of a company called omniture. And omniture was a large kind of digital enterprise analytics application. As I came over there, what we ended up doing over about a 12 year period was quite a series of companies and built out what what is now the Adobe Experience cloud. What I saw happening as we did that is, I saw this over the years, this proliferation of applications, the organization, it seemed like we at Adobe, even across our application set was getting to experience cloud had trouble actually being able to provide clear answers when the decision involved data from this application. And this application, which we own both of them, they both came through acquisitions. But even there was not even standards between those two applications. So a lot of cases I could get an answer here, which would be x ID an answer here that we why if I pulled the data together and cleaned it up, I get a totally different answer. And so that’s where it started becoming a problem. Because I’ve we’ve all if you’ve been in a large company in a marketing meeting, everybody brings data to the table. And you know, you go around the room, yes, this person what How’d your team performance, how many acquisitions, customers acquire this team of this team? And everybody has their own answers. And then as you start summarizing that data and aggregate that data, there’s a totally different set of answers. You know, this the end if you take the sum of them equals to x which you actually get a new customers. And so that’s that’s part of what we’re helping them to realize is that it all starts with standards. If you do not have standards, understand how the data fits together. It’s really good. Difficult to actually come to an answer. And I’ve seen that throughout my career time and time again. And as this industry has exploded, and then again, that this fragmentation at the application layer, everybody, everybody, the last 10 years was focused on automating, and being able to market and acquire customers from all these different channels that were emerging and popping up across the ecosystem. You know, 10 years ago, we didn’t have, you know, social really wasn’t a channel for marketing. And now there’s a proliferation of different social options out there that people are acquiring customers from, but it was application after application that was put in place to manage these, there wasn’t a lot of thought about, hey, how do we deal with the data coming from all these different applications that have different data models? In the last few years, what’s happened is you see with snowflake, and AWS and others, as these large as all this data infrastructure has moved to the cloud, it’s now enabled them to pull all this data in and the question is, now, what do you do with it? And again, the way we’ve operated the past is it’s the data problem is the data teams problem. that’s their problem. But what we’ve kind of realized like that, the problem you end up with there, the reality of that is, you end up in a situation where data quality is sort of an afterthought. And and that’s, that’s absolutely kind of what we think. And there’s all these tools have been that are that are being funded right now, all these companies around, you know, kind of data pipeline, cleanup of data. And we’ve taken a different perspective saying, Listen, actually, if you push the problem back in the front end, you can actually help the business team itself, in the end in the way they operate, the way they work can help solve some of this at the very front end without even involving the data side.

What’s the biggest pushback that you’ve seen on the technology from others? How are you addressing it? Because obviously, making this adoption doing a more proactive approach versus a reactive in some ways? what some of those objections you’re getting? Yeah, and I think this is, it’s at the core, sometimes, some of the challenges we see with adoption at certain companies is, if the company doesn’t have kind of an enterprise data strategy, and in a lot of cases, there’s lots of companies out there, I think the numbers are like 65, or more percent of companies out there do not have a enterprise wide data strategy. A lot of what has to happen we come in is people get I call it keep moving cheese, people do not like their processes change. And it doesn’t matter if the process is efficient. And it really is not a great process. It’s a process they’re comfortable with and they’re they’re okay with the fact that, hey, it may not result in a great outcome, but it’s a good enough outcome. So when we come in sometimes what we what we find is that people do not want to have the way they are doing things changed. And so the biggest challenge sometimes is, is actually helping them understand like, Listen, actually, the way that you’re doing things is really not great. And here’s a here’s if you insert our this solution, in the middle of this, there’s going to change take some some process change, the outcome is going to be way, you know, much, much greater. And in a lot of cases, you know, the people you have an agency or something, they may not directly benefit from the process change. It’s the enterprise that doesn’t. So invariably, there’s that conflict of, you know, the brand, really saying, Listen, we are going to enforce a process change, not only internally, but with our partners and our agencies. And you kind of have to buy into this. And I think what we see is that when the brands are really serious about solving this problem, they’re able to help their agencies understand the benefit to them, and it makes that relationship beyond their relationship gets better, because there’s not the friction that continues has in the past kind of continuously existed between the two of these witches. Everybody keeps throwing the problems downstream. And it’s somebody else’s problem to deal with downstream but ultimately kind of ends up like we say the data problems in the data science and data engineering teams. But what what I see is that process change is a big big challenge. But most companies are bracing because not in there’s a lot of automation, the way we’re our solutions are actually speeds up process and time and it speeds up work time for people because, you know, we see on the front end people like well, it’s gonna slow down my process. And it’s like, well, actually, what took you seven days to launch it, you know, launch a campaign, for example, we could share that now. Two, three, and here’s and there’s a better outcome on the other side on the data side. And so some of that is just sitting down and having that conversation with people. But it really takes a commitment at the enterprise level to say listen, we’re we we believe that better quality data results in better outcomes. The history of have clarified in starting 2013 How did it How did it begin like it was already enterprise at the very beginning. And how did that evolve?

Yeah, so it’s interesting. At the very beginning, the company was really focused on solving, I would say, a problem for a certain user. And, and but it was all it, but in a sense that they weren’t thinking about data quality they’re really thinking about, we’ve got a tracking code problem for the marketer. And right now they’re using a spreadsheet to do it. And let’s just, let’s just allow them streamline that process. That’s kind of where the company started. And it was typically a problem that you saw in large, large companies. So when I joined in 2018, there’s I think, was about 25 customers here, at, you know, with clarifying, and we quickly kind of realized, listen, that point problem for that individual is one thing. But there’s a bigger problem here that if you think about it, it’s not just on the marketing side, it’s a much bigger problem in the organization. And there’s, there’s a bunch of constituents, if we can, if we can take our application and connected it with the upstream systems, right, you know, the add the applications, add servers and Associates, and the other marketing applications and on the downstream connected to the data, the data applications, the data infrastructure, then there’s, there’s a huge benefit that can be unlocked for the for the enterprise. And so that was largely where we kind of start making the shift and was really kind of a GUI from a holiday submission tool for data to much more of a management a platform for managing data. And, and the implications are pretty significant. And how, what what you actually have to change from an application perspective, so we had to completely rewrite the application. It was a wholesale, wholesale shift on the product side, but also in the organization. And I think that that was probably the biggest change from our perspective as really kind of focusing on the on a much larger enterprise problem, and validating that and then making the shift as a company to to go there. And I think that’s where we’ve kind of really helped our customers unlock a lot more value.

Alexander Ferguson 26:59
What do you see as kind of the next big challenge for you guys going going forward, then, and the next obstacle that you’re going to be tackling?

Verl Allen 27:10
Yeah, I think for us, you know, we’ve gone through this process in the last quarter and half of really realizing, listen, we are kind of changing the way this works, this whole idea of like, data quality was a much more reactive approach, you know, the reactive approach, the enterprise took a proactive approach. And so we really kind of step back. And listen, we don’t really fit into an existing category, we are kind of creating our own category here. So we’ve we’ve made a definitive decision to really kind of go down a path of having to trailblaze a unique and new category, which has its own set of challenges and opportunities. And we view ourselves as a leader in that. And so a lot of this is right now, I think the biggest challenge we have sometimes is, is really being able to get the right people in the room on on the customer side, which I mean, what I mean by that is there’s some customers that are much more advanced and much more mature, they’re kind of evolution as a business around data and how they manage data, how they use data. And it’s getting those folks in the room to really understand what the next set of problems are. So what we’re seeing is the number of integrations, number of points they want to integrate us in the organization is increasing. So it’s making sure that we have the ability to scale that. And what we find is that it’s it’s a little bit self serving, because what we find is that as the number of points of integration go up, the stickiness of what we’re doing and the value of what we’re doing increases. And so it’s symbiotic. Because the organ, the enterprise is seeing it as solving additional problems and solving additional it providing more value, it comes back the same way to us. But it’s again, against making sure that we’re building the houses prioritizing the right integrations and prioritizing the right. You know, next big thing that we’re solving for them, so that we’re not solving it for the squeakiest wheel, but we’re solving it for a much broader set of our customers and being consistent with that, and it aligns with really much with with our customer set, not a customer. And we’re seeing we’re seeing this right now, we recently launched a partnership with a mobile gaming called branch i O. And, you know, the interesting thing was, we’ve got really very, very significant overlap between customers. And so be able to come together and deliver a solution to those customers that a creates a lot of value for the customer in their relationship with branch. But B it creates a lot of value for their relationship with us. And so those are the types of partnerships we’re trying to integrations we’re really trying to push on. Because it’s it’s it feels about win wins. This is really a win win win because the partner benefits we benefits but ultimately the customer is the one that wins.

Alexander Ferguson 30:00
You’re touching on a point of the concept of integrations in this platform of SAS connected to other SAS and built upon other SAS? What? What do you see the future is going to look like when it comes to software and SAS and the, in the in the b2b world, is it just going to become more complex? And and just layered and layered and layered?

Verl Allen 30:26
You know, I don’t I don’t think that’s I think what you’re saying in some ways is when we see this trend or last few years, there’s, there’s much more I think simplification as much says that there’s simplifications becoming more I think of trend and consumerization moves in one way. So there’s, there’s consumerization within the applications themselves. But I think you’re also seeing more standardization, I think enterprises are pushing for this, I think the market is pushing for this more standardization across channels. So that again, nobody wants to add another, you know, take from a SaaS point solutions to 180. But But I think the challenge you have there is one is going to continue to exist is one of the points of consolidation in which you can you can you can aggregate those, the work or the channels, if you want to call it that. And unfortunately, as much as the enterprise wants to simplify, on the other side, you have, I think even more fragmentation happening. There are some there are there’s some consolidation in the sense that you have big walled gardens and the fact you have, and they’re becoming much, much more distinct. You’ve got Facebook, you’ve got Apple, you’ve got Google, and a couple others. But it still creates challenges from an organizational perspective, because at the same time, you’re seeing that clearer, much clearer distinction. They’re also making big changes on the in that ad ecosystem, where IDs and cookies and log files are going away, which is why that industry was architect. So there’s a bunch of change happening. But I do think that even that change that’s happening is going to standardize this even more, because that the the level at which we’re analyzing data, now, aggregate level data, makes it such that you don’t need an app specific solution for every single, every single one of those kind of channels. And we call that but but again, it we’re going to continue to see some fragmentation, but I think you’re seeing also the vendors themselves, continue to expand their solutions. And, and provide more value and more coverage. For the applications. you’re you’re you’re using because, again, there’s so many point solutions. And again, there’s some of that expansion is happening on the on the application side, I’ve been at the vendor level,

Alexander Ferguson 32:49
what would you say is just kind of a key takeaway for the CMO of an enterprise when it comes to data, and and ensuring you’re actually able to get use out of all the data that’s got this flying around? What What would you say is the one key takeaway for someone?

Verl Allen 33:09
Yeah, it’s interesting, because I think over the last number of years, there’s been a lot of talk about it, especially in the enterprise. And in the data space around first, second, third party data, we’re seeing this huge shift that is being brought on by the industry itself, with privacy, push towards first party data,

Alexander Ferguson 33:28
what do you mean by party data, it just

Verl Allen 33:31
first party data is is data that I originates from me as the enterprise around and about my customers, through their interactions with me. third party data think about it is I gain insights about my users or my customers through information that is collected by third parties that they share with me. Gotcha. And so the challenges right now with privacy is a lot of that third party data, and the ability to connect that third party data back to your to your user ID, it’s not data you’ve collected, is being challenged. And that’s what’s happening when I was talking earlier with Google and Apple IDs. And I think it’s I think it’s a real fundamental shift. But what I would say is, what it’s doing is it’s really increasing the value. If I’m a CMO, it’s really create increasing the importance of me collecting and managing that first party data asset in a way that it is the most valuable thing that I have at my you know, that I have as a tool and as a, as an asset, as an organization

Alexander Ferguson 34:38
can’t rely on third party data or soon won’t be able to say,

Verl Allen 34:42
yeah, it’s becoming more challenging to do that. So that that and so what I think you’re gonna see is I actually fundamentally think we’re here to see as those organizations that are data rich, that are that have massive scale, in some way with that third, as much as I think some of that we’re still fighting this a little bit because it’s it’s changed everybody’s lives change. You’re gonna see that that data itself in some ways creates really interesting competitive moats for them that there were there before. But I think even become more distinct and more valuable going forward. Because you are, you have you are one of those, you’re one of those lucky ones that has scale. And you know, it’s not just scale in the sense of you got large, large set of customers and, and that way, data also have depth of data on those users. So I think that becomes a really interesting competitive mode, as the third party becomes less readily available. in the marketplace,

Alexander Ferguson 35:38
it’s a fascinating perspective on the trend of third party data goes away, or will decrease. So it’s gonna be it’s gonna Danish, and, and so it’s a real asset will be each customer, each company, organization has their own data of their customers, and now they just need to focus on deepening that they’re not going to just plug in and say, Oh, just give me a third party data. So I can then get all the other content from other Yeah, yeah. And I think more inward focused,

Verl Allen 36:07
yeah, and those and those, those companies, and I think people have recognized this, but I think this is even kind of forcing more clarity and more focus around that, and more conviction around it. And so it’s gonna be really interesting over the next few years as this as this transition happens. And I think it’s going to create, in some ways, some real winners and losers. And I think there’s, you know, a lot of it comes down to how well you manage that asset is is such a valuable, it’s such a valuable component of what really is going to be enabled an organization going forward to really create meaningful, unique and personalized experiences with their customers. And that’s, and that’s really the bet we we have made as a company, we are largely architected around that world that I’m talking about, which is all around first party data. And it’s it’s less this world that we lived in, where it was all kind of ID based and everything, it’s about this, it’s about this first party data and the value of that data.

Alexander Ferguson 37:08
Well, thank you so much for helping us understand both where things are headed in many ways and and the need for this data integrity of the value of first party data, and then a good platform for those CEOs of enterprises to be able to make that happen. For those that do want to learn more, again, the site is Claravine.com that CLARAVINE, like the old software that no longer exists. Fine, Claravine.com, and looks like you’ll be able to get learn more, probably get a demo of the platform there.

Verl Allen 37:45
We’d love to do demos. I would love to like, give us an I just I just think what we’re doing is the world is shifting in a way that data quality and data integrity is just at the core of kind of everything is going to happen going forward with first party.

Alexander Ferguson 38:00
Thank you so much again, and we’ll see you all in the next episode of UpTech Report. Have you seen a company using AI machine learning or other technology to transform the way we live, work and do business? Go to UpTech report.com and let us know