SPEAKERS

Christian Klepp, Verl Allen

Christian Klepp  00:00

Welcome to B2B Marketers on a Mission, a podcast for B2B marketers that helps you to question the conventional, think differently, disrupt your industry, and take your marketing to new heights. Each week, we talk to B2B marketing experts who share inspirational stories, discuss their thoughts and trending topics, and provide useful marketing tips and recommendations. And now, here’s your host and co-founder of EINBLICK Consulting, Christian Klepp. Welcome everyone to the B2B Marketers on a Mission podcast where you get your weekly dose of B2B marketing insights. So this is your host, Christian Klepp. And today, I’d like to welcome my guests into the show who is on an important mission. And that mission is to create stronger data integrity for enterprise systems as he likes to put it. So coming to us live from Provo, Utah, I believe it is, Mr. Verl Allen, welcome to the show, sir.

Verl Allen  00:49

Great to be here. Christian. Thanks for having me on.

Christian Klepp  00:52

Fantastic. It was really great to connect with you Verl. I’m really looking forward to this conversation. So yeah, without further ado, let’s dive in.

Verl Allen  01:00

Great!

Christian Klepp  01:01

Okay, so Verl, you’re the CEO of a company called Claravine. So that’s a company that helps leading brands to take ownership and control of their data from the start. So for today’s conversation, let’s zero in on the specific topic of data integrity, for better B2B marketing. So in your experience, what do you think causes databases and data sets to become too fragmented for marketers to deliver results?

Verl Allen  01:29

I mean, some of this, I think, is inherent in the structure of the way marketing organizations are actually structured themselves in the sense that we see in the enterprise, the average marketing organization has 50 plus point solutions. Each one of these point solutions has their own kind of data, you want to call it data model. And the way they named data is different between applications. It’s sometimes it’s simple things, the way that they are measuring performance and what they’re measuring. And other times, it’s the way that they’re writing and describing words. And in other cases, it’s simply that there’s a lot of missing elements in those applications that are specific to the business itself. They don’t, they are, they’re in sense abstracted, because we’ve come to a world where, you know, everything is multi tenant, everything SaaS and ultimately, these companies are trying to… the software companies and vendors are trying to delineate everything down to the lowest common denominator and what gets lost in there is a lot of the necessary elements that are very specific to the business that don’t get captured in that data model. And there’s also some big, fundamental kind of shifts in the industry right now. We’re on the B2B side, you know, the world for a long time on B2B was open web and IP address, B2C has dealt for a long time with kind of the walled gardens and dealt with this whole world that we’re seeing now evolve around obfuscation and kind of the goat the you know, IDs and cookie for third party cookies going away. B2B is going to have to face the same issue as you think about IP address and other elements that they’ve been using to, to target going away and continue to go away over the next 18 to 24 months. And it’s going to be a different world. So it’s a bunch of things that’s inherently in the stack. It’s also changes in the industry. And also the teams like you’ve got organizations that have global teams that are using different taxonomies or data models. And so there’s some of this is around creating a common language across the business and across the kind of technology stack, and then also across what’s going to become more and more prevalent on B2B side, really, more walled gardens.

Christian Klepp  03:36

You brought up some really interesting points in the past couple of minutes. And if I understood you correctly, a lot of this also has to do I mean, technology aside, also would be the systems and processes that are either in place or are yet to be implemented, is that right?

Verl Allen  03:52

That is correct, I think, yeah, a lot of that, you know, we think of the world a little bit differently from data integrity. I think traditionally, enterprises have looked at data integrity as a problem that the data teams solve, like, they kind of… the business side kind of pushes the data problems down into the pipeline, and down into the lake. And the view is that the data scientists, the data engineering teams, that data teams are going to… and data analysts are going to deal with these problems. The challenges is that, and this is where we come in, we have a very different view of the world, which is, if you don’t have a lot of that context, and you don’t have a lot of the business specific data that may not be captured in in the application themselves. It’s very difficult to add that after the fact because there’s a disconnect between those that are executing, you know, campaigns or executing creative and other marketing initiatives with those that are kind of tasked with data cleanup and data quality. And so we view that as kind of inherently just structurally part of the problem. And we don’t view data integrity and data quality as a reactive issue in the sense of that’s the data team’s responsibility, we kind of flipped that around, say, hey, if we can, if we can enable the business side of the equation to be more proactive and have the tools necessary to add a bunch of that context, then a lot of the data quality, data integrity issues are kind of resolved at the front end of the problem here.

Christian Klepp  05:16

Yeah, for sure. And it then becomes a collective responsibility if I understood you correctly, right. So it’s not just the one specific business function, one specific department that’s responsible for the data.

Verl Allen  05:28

That is correct. And if you look at the data out there, you know, Gartner and others have a lot of data on this, which is, many, most, the majority of organizations, when they report out data strategy, they are lacking an enterprise wide data strategy, you’ve got strategies, but what we believe is is really ultimately going to be a solve here is you have to create business wide data strategies, if that’s not in place, and that means not it’s not the data teams responsibility, or it’s not the business teams, but there’s got to be alignment between the two. And there have to be the tools in place to help enforce and create some of these standards.

Christian Klepp  06:04

Yeah, 100%, 100%. And I’m glad you brought up that point, because it’s a beautiful segue into the next question, which is around the common mistakes and misconceptions that organizations have, when it comes to data integrity, and you alluded to it as well in the past couple of minutes. And no, please share your thoughts with us and tell us what you think organizations need to do to address what is clearly a very serious pain point.

Verl Allen  06:28

Yeah, I mean, again, historically, we solve a lot of data problems and a lot of data quality issues with ETL. And really kind of it’s viewed as data cleanup, or cleaning up data. And, you know, one of the most finite assets and kind of areas in the business is around on that, when you think about data analysts, data engineers, data scientists,  those resources are typically scarce, and to have those individuals assigned with responsibility for delivering data quality, and data integrity is inherently part of the big problem. What we see the world so differently in the sense that, you know, if you can help create a common data language, you can allow that and put in the hands of the marketing organization tools to create that, tools to establish that and enable that. And a way to get that into the hands of the data science teams, data engineering teams, that eliminates a lot of the ETL and other processes that historically have, you know, increased the time required to get to insights, increase the cost of actually achieving data integrity, and ultimately decreased, you know, with ETL, you’re left to deal with what’s available to you. And so we’re hearing time and time again, from a lot of our larger customers that have made investments in AI and machine learning is the data sets of their decision off are so small, because of the lack of data quality and kind of lack of data integrity that they’re seeing, there’s a bunch of stuff they’re having to throw out. So ultimately, you’re turning machines decision off a much smaller set of data, that gives you reliable results. But again, the variability and the and the applications for that are limited because of the size of the data sets.

Christian Klepp  08:14

So that then it begs the question, I mean, not really begs that question. But it’s paramount, of course, that you know, data hygiene, right. But it’s something that needs to practiced, that needs to be constantly enforced and monitored.

Verl Allen  08:26

Yeah. And again, but I even think data hygiene sort of implies we’re fixing it after the fact. What we believe is, if you really start with a belief that how… you know, and we see this across the world, right, whereas complexity increases, if you’re able to create standards, and create and enforce those standards and manage those standards, and their enterprise wide. The complexity of the problem, you’re able to decrease the complexity and decrease variances across, you know, in this case, data. And that’s how we kind of… that’s how we see this, this world shaping up is you have to start with the end in mind, and you have to be able to to enforce and create those standards, and then apply those standards through the process of data hygiene.

Christian Klepp  09:13

So to a very significant degree on prevention before detection. That’s the word.

Verl Allen  09:18

Exactly exactly.

Christian Klepp  09:20

Right. Okay, in your opinion, um, how can data deliver effective ROI for brands?

Verl Allen  09:26

Yeah, I think this is something that companies are struggling and dealing with. And the way I see this is I’m a big believer and a big fan that this notion that enterprises with large sets of data are sitting on what is potentially a huge moat for them from a sustainability and defensibility perspective of their businesses. The challenge with these businesses that these organizations have these large organizations is taking data and turning it into insights and turning it into value. And so we sort of operate in a world where the scale, the data has now got to be a problem and become a hindrance to unlocking that value. And that’s why I think in some cases, you’re seeing, you know, these companies not taking advantage of this. And they’re still facing disruption from smaller competitors, who may be out innovating them on product and other kind of other alternative ways to go to market and, and unique business models. But one of the things that I’m a big believer of is this notion that if you have the ability to take and leverage the skill of the assets you have on the data side, it can be… and do it in a way that you are leveraging and creating quality that data and creating insights, you have the opportunity as a large organization to create these defensible moats. And so from our perspective, it’s shortening that time to insight, it’s expanding the set of data that you’re decisioning off, so that you’re actually you know, leveraging that larger set of data. And it’s becoming very clear about enabling views of data across applications and across channels and across what traditionally have been silos. Because if you’re not able to do that, then these inherent scale advantages that I think organizations have,  large enterprises have with data are just not available. You know, they spent years putting in place applications, to automate processes, to automate and accelerate the execution of campaigns, the personalization of them. But if you don’t have the data to fuel those, it’s really, really difficult to get the ROI that you’re expecting out of those investments. And that’s where I think there’s a big opportunity for brands, particularly to larger, larger brands to really kind of take advantage of those scale opportunities. And again, but it’s gonna come down to how do you take that large, large data set and turn it into a huge benefit for the organization. And I think there’s a lot of challenges, what the companies are having, again, like you’ve talked mentioned, or like cleaning up this data, and the relationships in the data matter. And that’s really kind of where we come in. And that’s how we view the world.

Christian Klepp  12:08

So I just have to throw that question out at you. I had a gentleman on a week ago, or two weeks ago, that when we were talking about how AI can be leveraged or used for content optimization. And obviously, we talked about data as well. So I’m going to ask you the same question I asked him. So in your opinion, for what you’ve seen, do you believe that if properly like you’ve been talking about in the past couple of minutes, do you believe that data can help to de-silo organizations?

Verl Allen  12:42

I do…with… So here’s the challenge, I think that we’re saying, and this is the, we have structured our organizations to operate in channels or swim lanes. We’ve given them tools to operate within those swim lanes, if you have no way of bridging both organizationally across the people. And it’s not just people across the channels in the marketing organization, but between well I call the creatives and the marketing folks and the data folks, the quants and the creators, if you don’t have a solution, and you don’t have a strategy that bridges the two, and you don’t have a strategy that bridges across these applications or channels, it becomes very difficult in the world we live in today where the fragmentation at the application layer, the fragmentation of the people there, the fragmentation at the channel layer is so great, that their hat, you have to be able to bridge those. And that’s really kind of what the reason I, I got into doing what we’re doing. And the problem we were trying to solve was really specifically around this, this challenge I think you’re seeing. And so in order to do that, though, you have to have a way to bridge or to connect these, you want to call them data models across applications, across teams across channels, in a way that really allows better richer relationships between those. And it’s becoming more and more challenging when you think about identity going away, especially, you know, with third party cookies and a more heavy reliance on first party data, you have to be able to do richer analysis which requires you know, richer relationships in the data to to achieve that. And that’s kind of where, that’s where we kind of you talk to our customers, that’s really where we support them.

Christian Klepp  14:29

Yeah, that’s a really interesting insight. And I suppose the answer is that there is no one size fits all solution, any of that, right. Like it’s um… does it depend on the dynamics within each individual organization, probably does right

Verl Allen  14:44

It really does because, you know, we see it with our customers. They have different even if you think about the way some organizations are structured, they have different geographies that they’re dealing with, the way they their businesses kind of architected, they have different ways that they work with their agencies and data. They get back to their agencies, our solution really extends not only across applications and across teams, but also extends kind of their data model out into their agency partners, both on the, you know, like, I’ll call it the campaign side or the media side, as well as on the creative and content side.

Christian Klepp  15:16

Talk to us about a challenge that you and your team have been able to solve in the past 12 months or so, I mean, like, something that you’re comfortable sharing with the audience.

Verl Allen  15:26

Yeah, I mean, and people probably have talked about this in the past, but it’s something that we faced head on. So we saw a situation during COVID, where in the enterprise, there was kind of a stall, with budgets for you know, back in that kind of middle of near the middle of 2020, we were at a point where we were actually out in the market actively raising a round of capital. And there was a bunch of stuff happening at once, it was the way that organizations, enterprise, are buying and purchasing applications and purchasing solutions. The shift that we had with our team going remote, and then kind of having to fundraise in the middle of all that. And, you know, one of the big things that that I think for us has been a huge benefit is we were sort of architected from the beginning remote, in the sense that we got a team, even on my leadership team, I had two of the other members of our leadership team was on the East Coast, one was in Denver. And so as we quickly shifted to going remote over the last 12 months, we’ve been able to not only kind of be able to keep the team engaged, but we’ve on boarded about 50% of our current employees today during COVID. And so for us being able to onboard those individuals, create engagement, actually get them productive, quickly, actually ensure that they we retain them in this environment that we’re seeing today, whether it’s huge, huge kind of battle going on for talent, we’ve been very fortunate in our retention rates are very, very high. We’ve really not lost anybody in the last year. And we’ve been able to onboard to get people productive very quickly. But it takes an intentional approach to that because we’ve had to change the way that we think about onboarding because a lot of that was happening, especially on the engineering side, a lot of that was happening in the office. Well, we’ve now started hiring engineers remote, and we view the we start going remote, the world sort of kind of becomes your oyster. And so it’s not only challenges with them being remote, but then you start throwing in time zones. And in some cases, there’s their language challenges that you have to face, confront and deal with. And I think our team has done a really good job of, in each one of those instances, we try to look at this specific problem we’re trying to solve and address that, in conjunction with kind of the layered effect of the fact that we are remote. You know, we are still dealing with a pandemic. And so I think that, for me, is one of one of the biggest things that we’ve been able to crack over the last 12 months is how to really make a team that’s remote productive, keep them engaged, and keep them aligned. And that’s not always easy.

Christian Klepp  18:15

And that in itself, I think is like an incredible achievement. I mean, like, you know, we know that we’re fully aware of the fact that everybody’s talking about working with remote teams and the environment, environments becoming more digital and physical. But the actual implementation of all of those things that you have mentioned is definitely no walk in the park. Right.

Verl Allen  18:35

It’s not and it’s and it’s always kind of shifting, you know, we’re learning more. And a lot of it really comes down. In fact, I just had a conversation this morning, I meet with everybody that joins our team, a couple weeks in and a couple months in and really we continue even this morning, I got some some great feedback from one of the engineers that recently joined and understanding kind of where there are still some opportunities to kind of continue to tweak that. And that I think is more than anything I’ve learned over the last year is it requires a lot more listening and a lot more empathy and a lot more flexibility. You know, I think we all tend to be have our own biases. And I’m one that was really biased towards being in the office. That’s what I preferred personally. But I’ve learned that’s not the best thing for everyone. In fact, there was… the thing it was surprising there’s a couple people as we went remote, who really did not, were kind of kicking and screaming, screaming to go remote. And they’re the most now a year later the most adamant about staying remote. It’s been really interesting to see that shift. And I’ve talked to them about why and and I don’t think we things are not as obvious as I think sometimes we think they are and that’s one of the big lessons I’m learning is to be much more flexible and much more adaptable.

Christian Klepp  19:52

That’s absolutely right. And I think one of the things that I would say as well I was at least a buzzword for me in the past 12 to 15 months is unlearning things. I mean, everybody talks about like learning new skills and shifting your strategy and approach. But there’s a couple of things that were brought about as a result of a pandemic that you have to start unlearning. But one of them for me was like, okay, getting contacts, without having to go to an in person networking event. Right. So you shift, you shift that approach to LinkedIn or having conversations with, you know, people like yourself on podcasts, right?

Verl Allen  20:30

Yeah. And I think you’re right. And I think it’s, it’s interesting, I think, as a broadly, I think people are learning this because it’s not just about us realizing that we have, you know, we can’t we’re not doing physical events that we have to, you know, we have to find other ways to do this. But it’s other people being open to those conversations. And I’ve seen that with recruiting and other things. I’ve, I do a lot of recruiting for our team as well. I mean, we tend to, to either hire a lot, we have a pretty broad network here. But you know, there’s times where I’ve been doing recruiting myself, and I’m actually surprised at how open people are now to having conversations. And even if they’re not necessarily interested in moving where they’re at, they’re still open to having a dialogue. And I think that may be a byproduct of we’ve all been home for 15, 16… a lot of people have been home for 15, 16 months and wanting conversation with people. But I think your point is a really good one. We have to unlearn a lot of things that we believed and the biases we had. And I think that the answers aren’t always the obvious ones, we sometimes have to dig a little deeper. And it’s some of the softer conversations that I have that I find really drive more insight than sometimes the data itself at times. So especially as it relates to people,

Christian Klepp  21:43

Yes, yes, that’s absolutely right.

Christian Klepp  21:46

Hey, it’s Christian Klepp. Here. We’ll get back to the episode in a second. But first, is your brand struggling to cut through the noise? Are you trying to find more effective ways to reach your target audience and boost sales? Are you trying to pivot your business? If so, book a call with EINBLICK Consulting, our experienced consultants will work with you to help your B2B business to succeed and scale. Go to www.einblick.co for more information.

Christian Klepp  22:14

Verl, I’d like to get your thoughts on this. And so we’re gonna do a little bit more shoptalk here.

Verl Allen  22:18

Great.

Christian Klepp  22:19

So there was a report that was released by a company called Corinium Intelligence. All right, so they highlighted results from surveys they conducted with more than 300 Senior data executives to understand how these executives are managing enterprise data assets to support reliable data driven transformations. So here are three of the key findings. Okay. So 82% of those surveyed, say data quality concerns represent a barrier to Data Integration projects. So that’s point number one. Point number two, nearly 80% find it challenging to ensure data is consistently enriched with proper context at scale, which powers more informed business decisions. That’s kind of going back to the point you mentioned a couple of minutes ago, I would say. Point number 3, 60.5% of employees who work that the companies surveyed will only trust data driven insights that confirm their existing gut feel. That was an interesting one, for me, signaling the gut instinct amongst employees are strong. What are your thoughts on the above?

Verl Allen  23:23

First off, let me say amen. (laugh) This is what we’re preaching every day of the week. So it’s interesting, a couple a couple insights here. Yeah. You know, when we talk about data standards, we talked about data integrity. I think what you’re seeing here is, there’s this first that statistic about, you know, 82% of them talking about challenges with data integration projects because of the data quality, that’s exactly the way we see the world, which is most organizations, I think, again, it’s interesting that these are data folks that were surveyed, they’re being handed a problem. And I think goes into the second point without a lot of context and asked to solve it. It’s I think we’re asking, that to me is you’re setting people up for failure, you’re setting organizations up for failure. Again, having more context, having a bridge between the data folks and the business folks is critical. And that’s I think that’s number one. That last point is an interesting one, I think, which I think what you see there is people talking about wanting to kind of rely, you know, validate what they believe their own gut, it comes back a little bit to what you’re saying earlier about having to unlearn things, but I think it also, it also points. I think, at the first two, the first two points of that survey, which are people are not trusting the data. They don’t, especially as data becomes cross channel and it’s integrated. I think what’s happening is there’s a lot of times where that those integrations are not helpful or the data is not necessarily… a lot of data gets thrown out. And so you’re left with a smaller set of data. And so there’s a lack of trust in, I think, if it’s my data from my channel, I can make decisions that are kind of subopt…, they’re optimized for my channel, but they’re enterprise wide, they’re suboptimal in a lot of cases, I think what happens is that once you start to try and create a cross channel, or cross organizational view, people lose fidelity and trust in that data. And that’s where they kind of default back to what they know, which is I’m going to do what’s best for me and my channel, because I trust that data, I don’t trust or really understand the data that’s outside of my purview or my area of responsibility. And I think there is one of the big opportunities in the enterprise, probably the biggest opportunity as it relates to optimization as it relates to creating value and extracting value from data. But I think it’s also one of those areas where, inherently, there’s a lack of trust, because of a lack of transparency in that data, and a lack of understanding about how this all fits together. And again, when we think of the world, those are the kind of areas that we you know, are purpose built to help organizations dig into an address.

Christian Klepp  26:16

So to put it in layman’s terms, there’s plenty of room for improvement then right, like…

Verl Allen  26:20

I think they’re I think there is, but that’s, that’s but I think that to me, is the real opportunity in all this, I think we’re getting to a point where we have to get past these some of these barriers, and I think we will, I think we’re at a point now, where, you know, if you think of the last, you know, the 2010s to 2020s is really the age and I was part of this at Adobe, right I, I ran, I ran the M&A practice on the experience cloud, we were effectively, arms dealers of applications for enterprises, to automate, to accelerate their ability to operate in this much more kind of fragmented channel world. But the flip side of that is, the 2020s to me are really the area era of data, it’s now taking it and saying, Listen, we’ve got all the applications in place, the next big opportunity from a… to extract value is actually the data itself. And that’s how you’re going to see that the organizations and the companies that are good at that, and are very strategic about that, and disciplined, are the ones that are going to win.

Christian Klepp  27:26

Yeah, no doubt, no doubt. So you talked about it a little bit in the past couple of minutes. But what are… what is rather one of the biggest challenges that you see in your industry today, like what is one of the biggest challenges your industry is facing?

Verl Allen  27:41

I mean, I think it comes back to the stats that you just that you just talked about earlier, I think that organizations are coming to a realization that we live in a… Even we see us as individuals, we live in a very connected world, right, much more connected than it was 10, 20 years ago. And I think data in the enterprise is exactly the same way. There has to be… and we have to get really intentional about this. And realizing that the user experience, the way that we execute has to become much more integrated. And that’s ultimately those decisions are going to be driven by data. And so that it also inherently means that the data has to be much more integrated. And I think that if, if you can, if companies can solve that, it allows them to execute in a much more holistic and kind of really kind of frees up I think the way we think about execution and the way that we think about engagement with customers and both on the B2B side and on the B2C side.

Christian Klepp  28:49

It sounds to me, at least from what you’ve been talking about earlier on the conversation, it seems like a lot of organizations are still have a long way to go in this regard. Am I right to say that?

Verl Allen  29:01

Yeah, I think they do. But again, it’s shocking to me how much value you can extract from small changes, it doesn’t, it doesn’t mean that you have to have every single element of your house in order because, again, this is a… I think every organization out there is on a journey on this. And but again, I’m a big believer that you have to start and put the foundations in place. If you don’t have those in place, it’s really tough to scale. And it’s really tough to kind of get a head on it and get to the point where you’re really kind of able to be proficient or excellent at using data to drive insights and drive value. And so I think a lot of organizations have is… some of this is just inherently the way that that things have evolved, as you continue to add applications there’s more each one of those applications is generating data and they missed that next step, which is okay, as we, as we evolve to the data side, let’s hand the problems off to the data team, let them fix this. But the reality is, is that there has to be an enterprise strategy across the business and the data side, and there have to be standards put in place. And, and we have to view this as a, it’s a team approach to winning this. And this whole idea of like, this is the data teams problem, the analysts problem, it’s not my problem, I got them the data, that’s just not going to be… that’s not sufficient, I don’t think going forward. But again, I think a lot of organizations see this and are moving quickly. And the great thing is, there’s a bunch of real advancements that are being made right now. And you see it you see in the last year, a lot of companies, you know, Snowflake and others that are and Databricks, there’s a lot of companies that are really kind of moving data infrastructure into the cloud, and becoming much more this kind of the way that we saw the evolution on the on the application side coming to the data side.

Christian Klepp  31:03

I love the use the term evolution, because I would, I would almost liken the situation to something, you know, if you remember, back in the day, our biology class like is this whole thing is an ecosystem, right? And you think about every organism and that particular organism’s role within that ecosystem to make it prosper and flourish. And you know, the symbiosis and how one feeds off the other and whatnot.

Verl Allen  31:26

Yeah, and I agree, and I think there’s a little bit of natural selection that happens as well, yes. Let’s be clear, like in that in that model, if you are able to quickly evolve, and you aren’t able to, to really change the way that you as an organization operate in this new world. I think you’re going to see an acceleration of disruption, if companies are not careful. But I do still fundamentally believe in this notion that large organizations are sitting on what I think is a really interesting opportunity to slow down if they’re and to push out that disruption because of the size and scale of the data that they have, if they are able to leverage that properly. And that is going to require a real change in the way we think about solving data quality, data integrity and enterprise data problems. It has to be enterprise approach, we can no longer have people handing off the problems to the data teams, the business has to be involved. The data teams have to be involved, they have to be aligned. And I think that to me, is where you’re going to see a huge separation between companies that do this well, and there’s a there’s a number of examples out there of this already.

Christian Klepp  32:42

Yeah, just something else came to mind. And I thought it was really important to bring up on while we’re on the topic of data world. So you can’t, you can’t like not talk about data without talking about how to prevent data from being compromised. And all the threats that are out there, especially as everything becomes increasingly digital. So what are your thoughts on that?

Verl Allen  33:03

It is a big challenge. I think that everyone’s confronting, I think it’s becoming more of an issue recently. What I would say is this, that it’s there are issues internally around, we talked about data security, there are threats from the outside, there are threats from the inside, there are also real challenges in the way that you think about, on one hand, you’ve got a business users that need real time decisioning. And real time, we live in a real time world. And on the other hand, what that means is that your, you know, your data, you know, the the runtime of the data, the data analysis, and everything is happening closer than to where the execution happens. And there’s and this notion that there’s security on the data side, but then there’s not necessarily the same level of security on the application side becomes a real challenge. When you think about the demands that are being put on the data teams, and the need for the organization to have runtime and execution so much closer to each other. And I think it’s going to be something that organizations have top of mind. We see it in all the discussions we have, I think organizations are taking this very, very seriously especially in the enterprise. But the other part of this is and I don’t want to I don’t want to downplay this at all. It’s surprising to me how many breaches I think we sort of become a little bit numb to it as well. I think consumers now here this week, this company, you know, this organization had a data breach and here and here and here and it’s shocking to me how organizations have learned to kind of duck and then reemerge and it’s kind of like, the assumption is the storm is blown by so they hunker down, the storm has blown by and it’s somebody else. And so, but I think that is a short sighted way to address this. It’s something that there are a lot of companies out there. It’s startups specifically that are literally focused on solving these problems. And it’s going to become one more element and, and level of complexity in how companies are going to have to architect their solutions to address, again, not just data quality and data integrity and integration, but also, you know, you overlay on that, the security and other requirements, and it does become challenging, and it does become, it require, again, this whole idea of putting the foundations in place is critical. In the same time, the world is shifting constantly, and that’s where I think you’re gonna see some of the challenges that exists continue to be for the next. So I think for the next 10 years, this space is going to continue to evolve. And emerge in ways that we probably today don’t even see because the threats are continuing to evolve and emerge.

Christian Klepp  35:56

Yeah, those are some really great observations. But um, so just to…. those points that you’ve brought up, going back to something you said earlier on the conversation about prevention before detection, because you know, you don’t want to… you don’t… organization’s probably shouldn’t be like reacting to a data breach, or to data being compromised. So what can they do now, to get these systems and processes in place that will help to probably not completely eliminate but minimize or reduce threats to their own data?

Verl Allen  36:28

Yeah, I think some, some of this has to do with, you know, understanding in the, in the enterprise that we’ve seen what the kind of emergence of this cloud data infrastructure. No longer is, is it enough that you know, data is no longer centralized in one kind of data warehouse or, you know, one location, I don’t want to call location, but in a single instance. We have situations now where it’s much more distributed, people have, you know, people in the organization, with a swipe of a credit card can stand up a data instance, and flow a bunch of data into an instance that IT or other organizations do not even have purview into. And so what it’s going to require, again, I go back to, there has to be an organizational strategy that’s not just the IT or data teams have a data strategy, but it has to extend to the business. And if we don’t have an enterprise wide data strategy, it’s shocking to me how many, the data does show that very, you know, the minority of companies out there have this. It’s not just about a data strategy around how to utilize and how to, you know, really kind of benefit from the data. But it’s also around security, because the technology that that’s out there now from a kind of cloud based infrastructure creates much more flexibility and much more ways to, to for organizations to use leverage data. But it also creates another set of set of challenges, which I think are, are becoming even more incumbent on the organizations to be what I call proactive and have a much broader kind of, in place a much broader data strategy, but it’s one that can evolve, and it’s has to be one that has input from both sides, and is enterprise wide, and not just certain parts of the organization are owning, and managing and enforcing. Business people, again, are real, real serious access to data, which is important, but also do such challenges.

Christian Klepp  38:28

Well, and again, it goes back to what you said before, it’s a collective responsibility, and organizations can’t afford to be complacent and sit on this now.

Verl Allen  38:36

And I think and I think people have a much better think generally, not just, you know, it’s much broader enterprise, across the enterprise awareness of some of what these challenges are, because we’re hearing more and more about it, and people read more and more about it.

Christian Klepp  38:50

So on to the next question, which we’ve talked about before, you know, a commonly held belief, status quo that you passionately disagree with, in your area of expertise. And why?

Verl Allen  39:04

Yeah, it’s interesting. I think there’s a couple things that come to mind.

Christian Klepp  39:07

Yeah.

Verl Allen  39:08

One is, and I hear this a lot, like, I hear people talk about, you know, and you hit on a little bit earlier, which is we’re, we call it organ, we call it kind of organizational entropy, which is, people get comfortable with making decisions off of incomplete data. And assuming they have to kind of continue to live that way. And there’s a lot of it’s interesting that we’ve come this far, and everybody talks about data driven decisions. And yet people are making decisions off their guts, time and time and time again, and I’m always shocked by that. And we also hear this a lot of times when I talk to organizations, they’re like, you know, we’ve become comfortable living with the notion that a certain percentage of our data 20, 30 percent. It’s just, it’s just we know it’s problematic, 40%, it’s problematic, but what do we do and they kind of just keep operating that way. So I think this what we call organizational entropy is one of the things that out there, I think that this is changing, there’re companies that are looking at this, saying that’s not good enough, and especially as they’re seeing the implications downstream. Now, in some of the investments they’re making in machine learning, I was talking to a very senior person at Deloitte, and he just said, Listen, we’ve worked with organizations to help them stand up huge investments in machine learning. And what we’re seeing time and time, again, is the size of the data set that they’re able now to decision off of, because of the constraints on as it relates to how that how the data is not, they’re not able to, you know, kind of integrate data well, he creates a sense a situation where we’re really struggling for them to create ROI off those investors, because the size of the data becomes so small and he was talking to me about, you know, if you enrich the relationships between these data, you do a better job at allowing this integration, you can massively improve and increase the size of that data set. So I think, you know, I think that is this whole idea that data quality, the way it is, is good enough. I just don’t think that’s I don’t think the organizations that are thinking forward are looking at saying. Let’s start chipping away at that. Because there’s, there are too many, there are too many issues here, we’ve got to start chipping away at the one on one. And it’s not all going to be solved down in the data pipeline. It’s it’s not all gonna be solved by the data teams, you’ve got to involve the business people have a lot of the context and a lot of the insights as to what the stuff is that they’re that’s being handed off downstream. And yeah, so…

Christian Klepp  41:44

Yeah. And there’s an incredible amount of moving parts too right. I mean…

Verl Allen  41:47

Sure.

Christian Klepp  41:48

Yeah. Okay. So just to wrap up the conversation, just, you know, a piece of advice that you give people out there. So one thing that you think they should start, and one thing you think they should stop when it comes to data integrity.

Verl Allen  42:03

Yeah, again, I think that I think one things I would stop is, data integrity is not the data teams problem. I think that’s one thing I would stop, have companies stop thinking about, like, you can’t think of the world as data quality, data integrity, that is the data team, that is the data now analysts, that’s data engineering folks’ problem, and it’s “I, on the business side, it’s not my issue,” you know, I hand it off, it’s their problem. I think that’s one thing I would say that needs to stop happening. And I think the other side of that is, on what I was think start doing is really enabling your business, the business to be part of the… the business folks to be part of the solution here. You know, I think there’s sometimes this notion that, you know, the, the data teams that I think there’s also bias on the data side, they believe they have the business doesn’t really understand the problems, I think there’s a real need for organizations to start communicating more clearly between those two sides, you know, between the business and the and the data teams, because that’s where the solutions are going to come out. And that’s, again, that goes back to what I was saying much earlier, from our organization, this communication has to happen. There has to be a common way and a language at which these organizations talk about these challenges. That’s how you’re going to solve these problems.

Christian Klepp  43:22

Yeah, no, that’s some really great advice. And, you know, to your point, it’s really about like, you know, looking at the organization and its culture, its mindset, the systems they have in place, or if they don’t have them in place, how they’re going to implement them in order to enact these changes, right?

Verl Allen  43:37

Yeah. Yeah. And I think you have to be careful. Some of this is going back to the basics and making sure you have the foundation in place.

Christian Klepp  43:44

Yes.

Verl Allen  43:45

To create that scale, because the scale is is critical. And that’s the opportunity.

Christian Klepp  43:51

Yeah, absolutely. Absolutely. Verl, this has been such a great conversation. So thanks so much for your time. Please do us the honor of introducing yourself and let us know how folks out there can connect with you.

Verl Allen  44:04

Yeah, so it’s Verl Allen, you know, I’m a father, I’m a husband, I’m a entrepreneur. I love what I do every day, I feel blessed to be able to come work here with the people that I get to work with. And the customers. We have some of the greatest biggest brands in the world that work with us, and it’s a privilege every day to help them solve really interesting challenging problems. You know, prior to Claravine, I spent 11 years, almost 12 years in enterprise software at Adobe and you know, we did a great job there. And they continue to do a great job of kind of building out this incredible resource for marketing organizations and you know, across the whole experience kind of digital experience landscape. And I think that you know, the thing that really gets me excited is this, this evolution and this explosion on the application side, to me, is really pushing on this next big problem around data. And that is what really gets me excited and really kind of motivates me to kind of keep going every day. So I love what I do.

Christian Klepp  45:10

Fantastic. And it was really a privilege to have you on the show. So thanks again for your time.

Verl Allen  45:14

Christian, thanks so much. It was great to meet you and great to spend time with you today.

Christian Klepp  45:17

All right, take care, be safe and talk to you soon. Bye for now. Thank you for joining us on this episode of the B2B Marketers on a Mission podcast. To learn more about what we do here at EINBLICK, please visit our website at www.einblick.co and be sure to subscribe to the show on iTunes or your favorite podcast player.