Unlocking Growth by Tearing Down Data Silos

Jim Marous (00:11):

Hello and welcome to Banking Transformed, the top podcast in retail banking. I'm your host, Jim Marous, owner and CEO of the Digital Banking Report, and co-publisher of the Financial Brand.

Jim Marous (00:21):

I'm excited to have Kim Snyder, CEO, and founder of KlariVis on the Banking Transformed Podcast. Kim brings over a decade of financial leadership experience to KlariVis with a goal to create a leading edge, easy to use, transformational data analytics solution to enable community financial institutions to compete with the large mega banks.

Jim Marous (00:43):

We discuss how KlariVis compiles data across disparate banking systems, into unified dashboards, giving financial institutions and people within those organizations actionable insights to drive smarter decisions, making greater efficiency and accelerated growth.

Jim Marous (01:03):

I've been in banking for more than four decades, which is an extraordinarily long time, and there are a few issues that are part of the life of a banker today as they were 40 years ago.

Jim Marous (01:15):

The biggest one is that banks and credit unions have vast amounts of data spread across various silos, core systems, lending platforms, customer databases, and more.

Jim Marous (01:26):

The challenge is that data often lives in these disconnected silos, making it almost impossible to analyze relationships, spot crucial trends, and look for challenges and opportunities in the marketplace. The result is that major growth opportunities are often missed due to the lack of visibility, not the lack of data.

Jim Marous (01:48):

So, Kim, for those who are not familiar with KlariVis, can you share a little bit about your company, your background, your mission, and the challenges that you're trying to address for community financial institutions?

Kim Snyder (02:01):

Absolutely. Jim, thank you so much for having me on the podcast today. I'm super excited to finally get to meet you in person. I've been following you for quite some time and absolutely love what you're doing for the industry.

Kim Snyder (02:12):

So, I'm Kim. I'm the CEO and founder of KlariVis. I actually started my career as a CPA with KPMG. And I spent a few years along the way in a startup company where I would say I caught my entrepreneurial bug, if you will.

Kim Snyder (02:27):

After that stint though, I went to a community bank here locally in Roanoke, Virginia, where I'm headquartered. And I was there for 10 years as the chief financial officer and Jim, I drank the Kool-Aid every day. I loved what we were doing. I loved the impact we were truly having on our community.

Kim Snyder (02:43):

I loved the environment that we had as a company, the culture that we had created. And after TARP, we paid that off or after the great recession, I'm sorry, we paid TARP off organically, and all of a sudden everybody started knocking on our door. We were a publicly traded institution, and at some point, you just kind of have to listen to those knocks.

Kim Snyder (03:04):

And we made a decision back in June of 2015 to exit the company, to sell the company. And I like to say that that gave me the ability to figure out what my next chapter was going to be. I was not retained with the acquisition. The bank that bought us was a much larger organization.

Kim Snyder (03:20):

And so, I started a consulting practice focused on the financial institution space. That entrepreneurial bug I had caught a couple of years earlier, or 10 years earlier in my career really kind of manifested itself and I decided I wanted to try to see if I could make a difference for multiple institutions, not just for one.

Kim Snyder (03:39):

I was very blessed. I picked up some long-term clients that afforded me the capability to start hiring back a banking team to work with me. So, I hand selected 10 bankers to come join me on that journey. And Jim, I'll tell you, my biggest accomplishment to date is that I convinced 10 people to leave full-time jobs with all of the perks that those have, to come join me on the consulting venture and we wouldn't be where we are today without that.

Kim Snyder (04:10):

So, we did that for about four and a half years where we worked with banks doing a variety of different projects in every bank we went into, it did not matter the size. It did not matter their systems. It did not matter how sophisticated their IT team was or was not, everybody was struggling with data.

Kim Snyder (04:26):

We were finding that we were spending so much time helping the banks extract data and make sense out of it, that the light bulb went off one day and we just said, you know what? There's got to be a better way. This is not a small bank problem. This is an every bank problem. And that's really kind of what we set out to solve with KlariVis.

Kim Snyder (04:44):

And so, what we do is we automate that entire report writing process, if you will to start with, by bringing in data from the core system as well as the bank's ancillary systems. As you know, with this fintech evolution that we've seen over the past 10 years, a lot of banks are going best in class now.

Kim Snyder (05:03):

So, they use their core for processing transactions, but they're going to best in breed, with an alchemy for their digital banking. Something else on their mortgage side, their wealth is a whole different line of business. We think that if we can bring all those data sources together in one environment, in an automated fashion, and make it visible back to the bank in an enterprise solution, that that's a game changer for the industry. And that's essentially what we're doing.

Jim Marous (05:29):

So, you're really using the bank's data and outside capabilities to make this so it's more visible and more actionable. Correct, then?

Kim Snyder (05:39):

Yes, that's exactly right. So, a lot of banks, especially the larger banks we talked to, they have spent the last three or four years, maybe five years, and a couple of million dollars just trying to consolidate all of their data sources into one location, create their own internal data lake, if you will.

Kim Snyder (05:55):

And they haven't even really thought about, well, how are we going to disseminate the information back to the end user to where they could actually do something with that data. So, as a team of bankers, we actually started from that end user experience where we thought about how does the lender need to see their data in order to make decisions off of it? How does the chief executive officer need to see their data? What is going to make that easy?

Kim Snyder (06:22):

So, we started from that perspective, and that's how we built our entire platform. And so, it's a dashboard, but it's very interactive. It's very intuitive. One dashboard, four different people can come in and get the answer to why, and it may be a different question that they're each asking themselves, rather than having a bunch of manual report writers sitting in their bank trying to deal with a proverbial cue that exists in banking.

Kim Snyder (06:46):

I need a report. Okay, well, let me see where I can fit you in on my schedule. Because that's what's happening if you don't have a platform like this.

Jim Marous (06:53):

Yeah. Well, it's interesting because I remember — it's amazing how much my memory works when I look back in my banking days and realize when you bring up something like this, jeez it rang a bell that when I was in marketing, when I was working for marketing firms as well as when I was working in the banking industry itself, the biggest challenge was finding out who had the data that I could use.

Jim Marous (07:16):

You'd have to go to the IT department, or you want to find out the results of a marketing program, you have to wait for it maybe six months after the program, which is too late. And the biggest problem was there wasn't a democratization of data if you were to be able to make it so the people that really had to make decisions based on it had direct access to it as opposed to having to knock on doors and stand in lines. The data queue as it were.

Jim Marous (07:41):

As an industry veteran, what are the biggest changes that you've observed in the data infrastructure and analytics over the past decade? How far behind do community banks tend to be right now when it comes to leveraging data?

Kim Snyder (07:58):

I think they're very far behind. I mean, it's really unfortunate and I like to say it's not their fault. Because I don't believe it is their fault, Jim. So, the cores are really great at processing transactions. That's what they do best. They were built on, let's face it, before what the PalmPilot. I mean, I think I had a PalmPilot back ... I don't know, back in the early 2000s.

Kim Snyder (08:20):

And this technology was built way before then. And so, it's really hard to take a system on antiquated technology and use modern tools against it to be able to leverage it. The same thing with the disparate data problem. Even if a bank has all in with one of the big cores, those systems are not talking to each other.

Kim Snyder (08:39):

And this is a soapbox that I will get on and stay on for my whole career. Listeners, if you are negotiating any kind of technology contract, you need to get an automated data extract with all the data elements coming to you on a nightly basis in a CSV file. And it should be included in your contract.

Kim Snyder (09:01):

Data accessibility is a problem. These vendors, I literally was on a call with a vendor the other day and they said, "Well, it's not the bank's data, it's my data." I'm like, "That's not true. It's the bank's data."

Jim Marous (09:11):

That's not good.

Kim Snyder (09:13):

No, it's not. And they need to be giving it back to the bank in a way that they can leverage it. The problem is, is that five years ago, there was no way that the bank could even leverage it.

Kim Snyder (09:21):

Let's say they got all of those automated CSV files from all the systems that they were using. There were really no tools unless they stood up a strong data team that knew SQL and could do database and scripting and all of that to bring it together to make sense out of it.

Kim Snyder (09:34):

Well, now you can leverage it with KlariVis. And so, that's really what we're doing. So, I think the industry is behind in the ability to actually use the data that they have. But I don't think it's necessarily the bank's faults. I think it's just this ecosystem that we live in, and the fact that a lot of vendors hold data hostage from the bank, and it's just not right.

Jim Marous (09:59):

So, the beginning of your company really came at a instrumental time in the business where smaller organizations realized you can buy these composable solutions in a way that really allows them to do big things in a rapid speed and scale up.

Jim Marous (10:16):

So, you've really built the bridge between core providers, data providers in different platforms that have these silos that have been built and bring them together and show community banks how to leverage that data in a way that can be deployed. Correct.

Jim Marous (10:33):

And you've probably also helped on the deployment because as you're working with organizations, you realize the challenges they have, let's say in the deposit or lending area, and you can figure out how to access that, can't you?

Kim Snyder (10:45):

Yeah, that's exactly right. So, we are bringing in key data points from all the various systems into one environment. We're doing the transformation; we're mapping it to a common data model. And then when we're deploying it back to the banks, we're actually leveraging embedded Microsoft Power BI as the visual layer that we use for our tool.

Kim Snyder (11:03):

So, everybody can come in every single day and get the insights that they're looking for. What is the answer to the question? And the beauty, Jim, is that now that everybody is seeing data the same way, there's trust in what they're seeing.

Kim Snyder (11:15):

Trust is a big factor in the community banking industry with the way data is delivered today through reports. It just depends on who wrote the report as to what the numbers might be on the page. And it's not uncommon for two people to show up to a meeting and have what should be the same numbers that don't tie.

Jim Marous (11:34):

So, it's interesting. So, you can use any core system. I'm going to start there where your base is. So, there's only so many, but the reality is organization. So, say, so I get my data from here. I don't know what it's really like, you're familiar with each one of the core providers, so you know the things you have to look out for. So, you really work as part of the IT team or the data team at the community organizations you work with, right?

Kim Snyder (11:58):

Yeah, that's exactly right. So, we have done, I think, 20 different core systems so far as far as integration. So, we've hit the bulk of them for sure. We're now working our way through a bunch of the different ancillary system integrations, but we know the data points we want because it's a team of bankers that are actually doing the implementations.

Kim Snyder (12:18):

We are not a company. As a banker, I used to get so frustrated, I would buy a technology solution and I felt like I spent the entire implementation time period trying to teach the technology company how to use their technology in banking. That's not who KlariVis is.

Kim Snyder (12:34):

And we don't hand you a template and say, "Go fill this out and get back to us when you're ready." We roll our sleeves up; we are hands on in the implementation process. And that's really a differentiator for us because we're bankers.

Kim Snyder (12:45):

So, we do understand the systems, we understand the lingo, we understand the business value of the data. That's a big miss that banks that are trying to do this on their own, they go out and they hire some data scientists to kind of come on board and build out visualizations, if you will, from their data, but they don't understand the business value.

Kim Snyder (13:04):

And so, there's still this constant back and forth between the business team and the data team to try to get it right. Well, because we're a team of bankers, we bridge that gap. Back to your original analogy. So, we are bridging that gap, and we are making the data available on a nightly basis, it's updated every single day, so the banks can kind of come in and see what they need to see. It doesn't matter where you live in the organization.

Jim Marous (13:30):

I'm going to take a case study here. There's a lot of emphasis right now, especially with community financial institutions on generating deposits. How has your company built new solutions to help community banks understand the deposit leakage they have and how to not just as importantly as generating them, but keeping the ones they have?

Kim Snyder (13:52):

Yeah. So, timeliness is critical. So, timeliness of information is absolutely the key to the puzzle in my opinion. So, with KlariVis, our bankers are able to come in every day and they're able to see the major changes or minor changes all the way down to the customer level.

Kim Snyder (14:12):

So, you can drill all the way into and see what is happening at that customer level inside of KlariVis. And so, here's a great case study back in March when SVB failed and the issues with First Republic were happening and so forth. I actually had a couple of bank CEOs call me and say, basically, "Look, thank you."

Kim Snyder (14:34):

I was able to come in, I was able to see a large movement of dollars from a long-term customer out of my bank the morning, at eight o'clock in the morning, I was able to see that. Their relationship officer was looking at the same thing. We had a conversation, I, the CEO, because we're dealing with community banks, and that's what community banks do.

Kim Snyder (14:53):

I, the CEO, picked up the phone. I called that customer, I went to them, I showed them how solid our bank was, and explained to them the difference between our community bank versus an SVB. And they moved all the money back the next day.

Kim Snyder (15:05):

And what he told me is, had I not had KlariVis, it would've taken me a week to figure out that customer. And by that time, they might have already had their debit card in the mail to them. They might have already started that whole transition process, and it would've been so much more difficult for him to get that money back into the bank.

Kim Snyder (15:23):

And so, that's just one example. We have lots of those examples from using data and using our system. The other thing that we did during that time period, Jim, is we had what I call our deposit balance change dashboard that's in our platform today. And we enhanced that tremendously in about a three-week time period. We turned it around and we gave much more flexibility into that.

Kim Snyder (15:47):

So, they could drill down all the way to, as I said, see every customer with their deposit changes across the entire organization. So, they can see which products are moving the needle because they can slice and dice. And so, which products are generating the most deposit, where are we having the most success? They can drive their marketing dollars into those products, perhaps talk about a strategy to move some deposits out of a products that are not performing into better performing products.

Kim Snyder (16:16):

All because they can see the data, they can see what's happening, they can see what is being successful. And they can see that every single day. And everybody, that transparency across the entire organization is a game changer. Everybody can see it. You don't have to come in and educate the team because the team sees it every day.

Jim Marous (16:33):

That's so key. Because we had a guest on, boy, it must be a year and a half ago now, they said, "We started to analyze the flow of funds when the government gave COVID checks out," and said a lot of organizations were getting really high and mighty saying, look at all the deposits we're getting, look how big the savings accounts are getting. But they didn't realize how much of being transferred out at the same time because they were looking at the masses rather than the individuals.

Jim Marous (16:56):

And they realized that some of their best customers were building very strong relationships with fintech organizations that are offering higher rates. And with them being able to analyze that and actually see that movement, their organization was actually able to combat that as you brought up. Not only with special products and special offers, but to understand that there's a lot of silent attrition going on.

Jim Marous (17:20):

Many people listen to the podcast. I bring this up quite a bit, that I'll go to a room of 200 bankers. So, they already have a pretty loyal base there that they all probably bank with their current organization they work for. And I say, "How many of you in the last five years has closed a primary financial relationship?"

Jim Marous (17:37):

And firstly, nobody raised their hand. I said, "Okay, now how many of you in the last two years have opened a brand-new relationship with a fintech firm with some organization in the primary product lines in the last years?" And virtually everybody raises their hand, and then they look around and say, "Guys, look around. This has happened to you daily." You think you have this core customer, but every day they're building new relationships.

Jim Marous (18:01):

And if you're not measuring that, if you're not measuring flow, if you're not measuring what's going on behind the scenes, you're losing customers, you're losing relationships.

Jim Marous (18:09):

So, what impact does the improved access to data you brought up that everybody in the organization has access to the tool. How important is that on the employees, on the culture, on even hiring practices when you're really giving the keys to the kingdom to as many people as you can?

Kim Snyder (18:33):

It's huge, Jim. And honestly, it's not something I thought about when I embarked upon this journey. I was trying to solve this data problem when we started with KlariVis. And what we're hearing back, and what I can see so clearly now with our clients, is that KlariVis has become a game changer as far as the employee experience.

Kim Snyder (18:53):

Think about Google. If I have a question, a personal question, I can google and I can get the answer. Bankers can't do that. They should be able to do that, but they can't do that about their customers.

Kim Snyder (19:04):

So, that self-service access is just absolutely critical. We're empowering the employees to do their jobs more effectively. We're now the foundation for internal communication, because as I mentioned, everybody is now seeing data the same way.

Kim Snyder (19:18):

So, now the question becomes about why is this happening? Not what is happening. They're not spending all their time trying to figure out, well, was that a movement outside of the bank or was that just a movement within accounts? Because we're delivering that to them to where they can see it very easily.

Kim Snyder (19:32):

They're able to see what's working, what's not working, the coaching. Now this is a scary thing for some of our banks. That transparency can be scary. Because if I'm two lenders and maybe I'm not at the top of that lender leaderboard, now everybody in the organization maybe can see that. I think that's a good thing because that's just kind of the way I'm built. But not everybody embraces that necessarily.

Kim Snyder (19:57):

But that transparency in the organization, improving that employee experience, who wants to come to work and write the same report over and over and over and over and over and over and over again, and never know, here's the key, is anybody really using that report?

Kim Snyder (20:12):

Somebody asked me to write this report five years ago, and so I'm updating it every day and I'm maintaining that, and I'm sending into the universe at the bank. I have no idea if anybody's using that report or not. Who wants to do that? That's not any fun. Let's empower these data analysts to actually help analyze the business, use the data to drive the business, rather than spend all their time trying to create the data to put it in a format that somebody can leverage.

Jim Marous (20:41):

It's interesting you talk about the accessibility and the access to it, but underneath that, you also said, well you know how people use it, the fear of somebody seeing data they haven't seen before, the opportunities there. This is one of the keys, I think, with your company, is that because you're all bankers, because the majority of your organization is made up of bankers, these are frustrations and opportunities that your team can relate to.

Jim Marous (21:08):

So, I would imagine that on an ongoing basis, you kind of become that psychologist that talks about, "Yes, we know this goes on, but we can help you with the culture situations." Because I think giving people throughout the company access to data can be a scary thing for some leaders. I would imagine that because you come from the banking industry yourself, you can show, yeah, it can be scary, but here's how you can alleviate that fear. Is that true?

Kim Snyder (21:37):

Absolutely. 100% is true. And now, Jim, that we have the client profile that we have, we have so many use cases and our customers, it's really been fun to watch. So, when you're a young company, you leverage the current customers that you have for references and those kinds of things as people are vetting your solution. And that continues on.

Kim Snyder (21:58):

Well, what has happened with the KlariVis community of clients is they have formed their own little community now. And so, they are actually having conversations, and we want to make this more formalized and actually create a community for our banks. But they pick up the phone and they call, "Hey, I talked to you about KlariVis, glad you're on board. We're trying to solve for this problem. Can you help me?"

Kim Snyder (22:21):

And so, it's really kind of creating that outside of the KlariVis ecosystem, if you will, that community that's going on. But the consulting that we do along the way. So, one of the obstacles that we have, and this won't surprise you, when we're talking to a new prospect, is they're worried their data is dirty.

Kim Snyder (22:38):

And I like to say, look, every bank's state is dirty. It's the dirty little secret in banking. Because we have not done a great job with data governance and maintaining — I like to give the example, every time you go to the doctor's office, what do they do? They confirm your name, your address, your cell phone, your insurance. Well, banks don't do that. They should, but they don't. And so, you have a lot of messiness inside of the data, and it concerns people.

Kim Snyder (23:02):

What KlariVis does though, is affords the bank because now they're seeing data visually, they can figure out what elements do I need to clean up? And then we can have a conversation about how some of our other clients are leveraging that particular code or what have you in their core system to be able to make sense out of it, or to do something with the element itself and make it more meaningful.

Kim Snyder (23:26):

So, it is a very consultative approach, and you're right, because we're a team of bankers, we lean into that heavily. It's who we are at our core.

Jim Marous (23:36):

Well, and it's key because you're not just giving organizations a platform. You're not just giving them a technology. You're actually helping them through the way you've built this platform on how to use it. That is the key.

Jim Marous (23:54):

I kid about the fact that almost every organization of any size has Salesforce implemented. But if I went and said, "What are you doing with the information you get from Salesforce?" They're stymied because the reality is they pay for the subscription every year. It's not Salesforce's fault, but there could be more done on their part to become better consultants as to how to use this data become successful.

Jim Marous (24:16):

Because how many of us have platforms on the shelves that we've never used, but we can justify the investment. I mean, I do it in my own business. I justify an investment, something going at some point I'm going to do this. But that's where you can really serve the consultant way of implementing this and where your bankers helping bankers, it becomes even more key because you're not just technologists, which is a big deal.

Jim Marous (24:43):

Could you discuss a little bit about the whole hype around AI and what role you may envision as to AI improving the development of, and the access to data?

Kim Snyder (24:56):

Absolutely. So, I laugh. We just closed a capital round in December that I'm very proud of. But I do believe had I added AI into my name in some form or fashion, I probably-

Jim Marous (25:08):

You would've gotten more money.

Kim Snyder (25:09):

At a much higher valuation because of all the buzz that is around AI. But look, you don't have to boil the ocean in order to make sense out of your data. And so, at the very foundational layer, you cannot leverage artificial intelligence unless you have clean normalized data, because junk in to those models is junk out of those models.

Kim Snyder (25:33):

And so, what we've been doing over the course of the past five years since we started our company is really getting that foundational layer of all the banks and we're solving a major reporting problem in the process of doing it for them. And so, that is happening. So, now we have all of this data. And so, we have a number of different use cases, and that's how we develop at KlariVis.

Kim Snyder (25:56):

We come out and we talk to our clients and say, "Tell me what your highest priority use case would be. What would you like for us to solve for you? And we get that information back from them. And that really kind of drives what we're doing, how we develop our entire platform. And AI is no different to that.

Kim Snyder (26:11):

So, some of the use cases that we are internally working on is predictive analytics around deposit leakage, if you will. You brought deposits up a minute ago. So, if I can look at the behaviors of customers for the past six months or the six months prior to them actually leaving the bank, moving all of their money out, and I can take that and I can use AI to say, "Show me the other customers who are behaving that way, that's a game changer."

Kim Snyder (26:38):

And serve that up before the money leaves the bank. That that is a very, very specific use case but it's one that we're diving into at KlariVis. And so, we are going to be very use case driven with AI. We're going to make sure that we're doing it in the right manner with the right cleaned data.

Kim Snyder (26:57):

Because again, if somebody just sells you an AI tool and you've done nothing to clean up your data or to consolidate your data, or to link your data to do the data joins, how the heck are you going to make any sense out of it? You're not going to be able to.

Kim Snyder (27:08):

And so, that's how we are approaching it. And super excited about some of the things that we have on the drawing board. Using the generative AI, that's probably what we'll come out with first and we are a visualization layer. So, we have a KlariVis report builder that we've built all these prebuilt dashboards.

Kim Snyder (27:30):

So, bank buys KlariVis, and within 90 days, actually 60 days now, they can sign it. They'll have all their dashboards. They're 350 plus prebuilt dashboards. They don't have to do anything, just log into the system. And there they are with all their data elements. After we go through implementation.

Kim Snyder (27:45):

But we also now have this report builder that allows them to build a graph, a chart, a query using very easy because we make data simple at KlariVis, way if of something that we haven't built, well think about if we layer generative AI on top of that and say, "Make me a graph of X, Y, and Z."

Kim Snyder (28:02):

Type that in rather than the banker actually having to know what goes on the x axis and what goes on the y axis and what is the data element called and this, that, and the other. We see that as a path forward for us as well as far as AI is concerned.

Kim Snyder (28:15):

So, it's coming into KlariVis, it's not going to be there tomorrow because it takes time and we want to make sure that when we deliver it, we're delivering it accurately and correctly.

Jim Marous (28:24):

You've referenced it a couple times that organizations get concerned about any kind of data tool because they get concerned about their data, as all organizations do. Is that the primary stumbling block when you're knocking on the door of an organization trying to show them what you have?

Jim Marous (28:43):

Is that one of the first questions that they have or one of the reasons why they say they want to stall, because they don't want to give you anything until it's all right. But the reality is you can do a whole lot of things without it being all right. Correct?

Kim Snyder (28:57):

No, 100% correct. And I would say in the early days, Jim, we were hearing that, now whether it's because I'm on podcasts with influencers like yourself, or we've just been out there talking about this, and we have so many client use cases around it. We don't hear it that frequently any longer, but it is a fear.

Kim Snyder (29:18):

What I tell my banks though is that we have not one implementation, not one out of our a hundred plus implementations that we've done have we not had a dirty data problem. Has the CEO not looked at the platform and say, "Oh my gosh, that needs to be cleaned, that needs to be fixed."

Kim Snyder (29:34):

And because it's visual, it just jumps off the page at you. And so, now they're able to say, okay, "Well, let's focus the resources and energy into making sure that data element or what have you is corrected."

Kim Snyder (29:47):

Another big issue, and I'm going to get into the weeds just a little bit on this, but it's really important for your end users to know. So, when I was in banking back in the 2005 to 2015 era, we used a lot of user defined fields inside of our core system and other systems because the cores just didn't have the fields that we wanted to track.

Kim Snyder (30:05):

User defined fields are the death of AI, they're the death of any kind of phenomenal reporting. And so, what I encourage, and we encourage all of our banks to do, the cores have gotten better at opening more fields and creating more standardized fields.

Kim Snyder (30:19):

There could be a standardized field there today that you're not leveraging because you're still using a user defined field. Go back and do an analysis of your user defined fields and make sure that if there's a core field, go through the effort of using the core field because now you can report on and it's not freeform.

Kim Snyder (30:36):

You're going to be able to leverage that, utilizing machine learning and artificial intelligence much easier than you can a user defined field. If you're tracking something really important in a user defined field, you need to find a standard field to track it in, in order to leverage it.

Jim Marous (30:51):

I think that's a key element for your company, is that not only are you providing the visualization of data and how to use it and expand the democratization of data across the organization is that you understand the rabbit holes that financial institutes can get into.

Jim Marous (31:08):

You understand the challenges and also you understand because of the number of clients you have, what are some opportunities that you didn't even think people would use your data for, that they're now doing that? I mean, I think that's ... everybody's looking for a friend. Everybody's looking for somebody to get them over that hump.

Jim Marous (31:24):

I mean, that's why composable solutions have done so well in our industry is that organizations can't spend the time to say, "How do I go from a 15-minute account opening process to a five minute or a three minute." Organizations can't develop a brand-new digital lending platform that works for them.

Jim Marous (31:42):

They're looking for those professionals, those specialists that can get that right. And I think those are the rabbit holes, especially when you're looking at data that are so important, is that your organization has the ability to work from the perspective of the customer as opposed to the perspective of a data company.

Jim Marous (32:01):

And we just finished the Super Bowl, and the winning team was that team that worked so well together to get the ball down the field without everybody running along, that everybody let the people do what they did best and then work together.

Jim Marous (32:16):

Well, that's kind of what you're talking about here. You're saying, "You know what, we are really good in this element. We're not as good in this. We don't say destroy your core. We say, no, let's leverage all the data that's available across many different cores and many different systems for your benefit."

Jim Marous (32:32):

So, with that in mind, there's not an organization out there that doesn't have problems with IT budgets or budgets in general. And they also are looking for IT talent or data talent that's probably not resident right now and is extraordinarily expensive to get. With that as the foundation, what advice do you offer for pragmatic first steps when organizations are trying to modernize their data journey?

Kim Snyder (33:01):

Yeah, so as with anything that is an enterprise initiative, it has to start at the top because there's going to be silos that have to be broken down, not just from a technology perspective, but just by the way your teams interact and work together.

Kim Snyder (33:15):

And so, moving to an enterprise data strategy is critical, but it has to be at the direction of the c-suite from the board. So, if you have that buy-in, and here's how you — if you need assistance in getting that buy-in or putting that business case together, call me. Because again, I can talk about that all day long as well.

Jim Marous (33:37):

You've done it before.

Kim Snyder (33:38):

I've done it before. That's exactly right. But just think about it, Jim, again, when I was in banking, and I would argue across many organizations today, it's the operational folks who were deciding what codes they're going to set up in their systems and what data elements they're going to actually collect. That's the wrong group. It needs to be coming from the business perspective.

Kim Snyder (34:00):

And so, an enterprise strategy brings all of those constituents and stakeholders together when they're embarking upon this journey. And that's why it's so important. It can't just be about marketing. It can't just be about finance. It has to be about moving the bank forward as a whole. And so, start there.

Kim Snyder (34:19):

The second one you hit on a minute ago is identify where all your data sources live, your key value data elements. Now, there's a lot of data elements that's noise that you don't need to worry about. But there's some key elements that drive your business. And you need to know where they live.

Kim Snyder (34:33):

Are they on the core? If they're not on the core, do we want to put them on the core? Can we get them out of the other systems that they're in? Be careful. They may be in somebody's desk drawer on a piece of paper. They may be there.

Kim Snyder (34:45):

And so, you need to identify where are all those data sources? Where do they live? And what are the items that are really going to help us transform our organization and move it forward?

Kim Snyder (34:56):

And then the third step is how are we going to bring those data sources together in one place? And that's really where KlariVis can kind of come in and help you. You do not need to go invest and build your own internal data lake.

Kim Snyder (35:08):

I would argue that that is not the right approach at all. And I would say that whether I had the system or not. And the reason for that is because with KlariVis, you're able to leverage not only our expertise, but how all of our clients are utilizing data. Because all of our clients have their fingerprints on our product.

Kim Snyder (35:28):

If you go and you build this in a silo, all you're going to have is a silo, another silo inside of your organization that you've built. And you're going to go hire data scientists who do not understand the business value. And so, that proverbial cue that we talked about a few minutes ago was still going to exist.

Kim Snyder (35:43):

"I need you to build me a dashboard. Okay, well what does the dashboard entail? Well, I don't know, I just wanted to have these elements on it." Okay, the data person goes and builds it. "Well, no, that's not what I wanted. You know, I saw something the other day, gosh, if I could just put my ...

Kim Snyder (35:55):

They can't articulate what it is that they want until they see it and when they see it as golden and it's really hard to solve that problem internally. It's just really hard to do it.

Jim Marous (36:07):

So, I'm a ABC credit union in a small town in Ohio, and I have limited budget, but I also have a limited idea of what the potential is for your solution. Could I leverage your solution, let's say in a composable way to handle just my data with regard to deposit generation, deposit flows without touching the loan side of the business or the commercial or small business?

Jim Marous (36:32):

Do you work with clients that want to start in a smaller environment than the overall organization? Is that possible to be done?

Kim Snyder (36:40):

Yeah, absolutely. So, we have packages inside of KlariVis. And it starts from somebody who's at the very beginning of their data journey and just really needs basic reporting all the way to somebody who wants an API wrapper and get the data back so they can do the row nail mill techniques on it. So, we kind of cross the gamut and our packages work that way.

Kim Snyder (37:01):

I would say I would never bring somebody up with just deposits. And here's why, Jim, because you're losing some great intelligence there. I want to know, if I just bring up deposits, I don't know if they're a single deposit account holder or do they have loans with me also. That's important in seeing that whole holistic customer picture if I'm really trying to figure out how I'm going to leverage and understand what is happening with that customer.

Kim Snyder (37:26):

So, we don't offer anything where you just bring up deposits, but you can just bring up deposits and loans in a basic fashion. That's kind of our starting package, if you will. And I will say we have banks on our platform as small as 80 million, and as large as 8 billion today. So, our price point really, again, think of my background. I was a community bank, CFO-

Jim Marous (37:49):

Friendly for who you're trying to serve.

Kim Snyder (37:51):

Exactly. I want this to be affordable. We are building in a way that it is affordable. And the efficiency gains that our clients are seeing are three, 400% ROI in their first year, just based upon making the data ... that doesn't even count the rev gen that they're generating as a result of being able to leverage the data.

Jim Marous (38:10):

So, last question. Looking three years ahead, we don't talk about 5 or 10 anymore because we know we're going to be wrong. But looking let's say three years ahead, what excites you about what you are going to be able to provide to the financial services industry and what's going to be available out there is tools that can make banking better.

Kim Snyder (38:29):

So, the KlariVis journey includes so much. So, we have a very robust roadmap for the next three years. But really where do I get excited is that AI component where we're really able to move from the analytics we're delivering today to really more predictive analytics, to where they truly can start leveraging their data to serve their customers better ahead of be proactive instead of reactive to me. That's where we need to get to.

Kim Snyder (38:59):

And I'm super excited about the path forward on that, but the first step was bringing all the data together. And that's the hardest step, quite frankly is getting it all together. Do you know how many systems are in the banking ecosystem?

Kim Snyder (39:13):

I mean, it's kind of crazy. And so, getting data accessibility is huge and data accessibility is going to be the second thing that I hope in three years is a non-issue. I hope in three years every bank has access to their data in a way that they can meaningful leverage it regardless of the system that they're on. Again, that's kind of a personal soapbox that I'm on, but it's so critical.

Jim Marous (39:37):

Well, it's the foundation upon which everything's built. And we're really in a situation right now in the industry where we can't wait. We say that a lot and it's like the sky is falling, but the reality is the consumer gets predictive analytics when they tag into Hulu or into Netflix.

Jim Marous (39:58):

They get predictive analytics as they get in a car and look at the roadmap. And last night I had to cross Florida from my son place to mine and having the tool that said, "There's a problem up here. You can't use this freeway because of smoke from grass burning in the Everglades."

Jim Marous (40:17):

If I didn't know that I'd probably still be on the road trying to get home. And that's what we're talking about here. We're talking about predictive analytics, and everything happens so fast. You talk about Silicon Valley Bank, and you realize that an organization can have a run in less than an hour because everybody has simply a button they can push. How do other organizations react to that happening in a way that works well for everybody involved?

Jim Marous (40:44):

Kim, this has been an exciting conversation. I realize now as we got deeper into the conversation, what you do for the banking industry is what my son does for pharmaceuticals. He is a visualization guru that really takes data from pharmacies, from healthcare companies, from medical firms, the producers of pharmaceuticals, brings these together to make it so organizations can leverage that data for a better revenue flow for pharmaceuticals, for pharmacy.

Jim Marous (41:16):

So, it's a very interesting field that I'm by no means that I prove by some of my questions, not a pro at doing. But the reality is, if you don't make this data accessible in the most basic ways, if you don't make it visible, if you don't make it so that a non-data person can access it and do something that impacts the consumer, we've left a lot of money on the table.

[Music Playing]

Kim Snyder (41:39):

We sure have.

Jim Marous (41:40):

Thank you so much for being a part of this podcast and I'm looking forward to talking to you again.

Kim Snyder (41:44):

Absolutely. Jim, thank you so much. Have a lovely day.

Jim Marous (41:48):

Thanks for listening to Banking Transformed, the winner of three international awards for podcast excellence. If you enjoy what we're doing, please take some time to show some love in the form of review.

Jim Marous (41:58):

Finally, be sure to catch my recent articles on the financial brand and check out the research we're doing for the Digital Banking Report.

Jim Marous (42:06):

This has been a production of Evergreen Podcasts. A special thank you to our senior producer, Leah Haslage, audio engineer Chris Fafalios and video producer Will Pritts.

Jim Marous (42:15):

I'm your host, Jim Marous. Remember, the key to success in banking is bringing data to life, improving decision making, operations, and the customer experience.

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