Developing Data Strategies: Guiding Your Journey from Early Stages to Strategic Vision

Welcome and Housekeeping  

Lindsey Weldon 

00:32 – 02:26 

Welcome, everybody. We’re going to give it just a few minutes to let everybody come on in. 

Alrighty. Let’s go ahead and get started for today. 

I am Lindsey Weldon. I’m a member of our Meridian Link Marketing team, and we are so excited that you are here to join us today for Developing data strategies with our presenters, Christy Ma, the director of data insight at Meridian Link, and Joe Mern, director of product management. 

Before we get started. Just a few items. 

We are definitely recording today’s session. So if you object, please disconnect at this time. 

We will have polls in our webinar today, and they will take place to the right of your screen where you can vote. There will be time for questions at the end of the web You can submit those either through the chat or through the Q And A, and we’ll be monitoring that live as we go. 

And if you would like to receive this webinar on demand, and we will be email but opt, this afternoon or tomorrow. So, you’ll be able to share that with, other coworkers and rewatch different areas if you need to. 

One quick plug. We do have our Meridian Link Live in Nashville coming up April 29th through May 2nd. 

We will load the link into the chat for you to be able to register if you’re interested and to learn more information. And one other moment, as the lawyers love, we would love for you to read every word of this and digest it, and, we’ll go from there. 

So May I hand it over to our first presenter of the day, Christy Ma. 

Introduction to Data Strategies 

Chris Dumas 

02:26 – 32:54 

Yeah. Hey, everybody. 

Good afternoon. Good morning. 

Depending on what side of the continent you’re on. Thanks for being here. 

I’m excited about this topic, and getting the opportunity to talk with with hundreds of professionals in our space, both credit union and banking industry like, I think this is this is this has to be a heightened focus going into 2024. We’re entering kind of the 5th major innovative wave of, bi and analytics through generative AI. 

We’re going to talk a little bit about that. We’ll talk about some of those trends that we see playing out in our market space, we’ll talk about some strategies to effectively ready ourselves for those trends to be able to leverage, some of the new technology that’s becoming available. 

We’ll go into detail on where to start and how to get a plan in place so that we’re ready, for this wave of innovation, and then we’ll go into q and a. And so what I’ll do is I’ll start by talking trends. 

Joe will take it over to the second half of this webinar, and really kind of take a hands on approach as to where we have an opportunity to double down and get things going for our own organization when it comes to, developing implementing and executing insta data strategy. So with that being said, let’s jump in. 

We are going to have a first pull here just to kind of take the temperature of the room, how many of you are looking at leveraging generative AI in 2024 creating a process to improve data accuracy and reliability, leverage data insights to accelerate growth or are just kind of lost. Right? This is this is a thing called data, and it’s 2024. 

What next? Just if you could, go over there. I think it should be a pop up it shows up. 

Go ahead and check that poll. Respond to that poll. 

And we’re going to talk about all of these Right? So we’re going to we’re going to talk about what generative AI introduces, to our organizations. We’re going to talk about how to prepare for generative AI so we actually can get some business value out of it. 

We’ll talk about where to start. And we’ll absolutely touch on the importance of data accuracy and reliability. 

So go ahead and answer that poll if you get a chance, and then we’ll jump into it. It kind of it looks like it’s kind of spread out. 

Right? So this is good. Diverse group. 

Some of you look like you’re ready to leverage generative AI, others are saying, hey. It’s 2024. 

Let’s just talk about data and see what happens next. So pretty diverse group answers are spread across the board. 

It’s a good mix. So that’ll give us a lot to talk about. 

So let’s jump into it. One thing I will say As we go through this, I would encourage you to use both the q and a, buttons and options there, as well as the chat. 

I’ll be referencing the chat, as we go through this. But also, Lindsey will be moderating our Q and a and kind of watching that stream as well. 

We can answer those questions as it come in. So 2024, this is going to be a big year, for data analytics, Data Sciences, AI, Machine Learning. 

This is going to be a huge year with some, really, disruptive forces. That are that are being made available to us through generative AI. 

And so if we look back prior to 2024, there’s been kind of 5 major waves of innovation in, data and analytics in that space. The first one, if we jump way back to, like, 20, 10. 

What we’re going to see there is, the innovation that enabled report developers to actually build reports without having to write SQL. Right? So your SAP business objects, your your IBM Cognos, that was kind of the first wave. 

That took 15 years. To come to maturity, by the way. 

The second wave, gave us the ability to visualize data that was locked in, like, cubes, spreadsheets to spirit data sources and warehouses. And there we saw the introduction of Tableau, click, Power BI And that kind of started to give the business users the insights that they were looking for, without having to scratch through spreadsheets and do some of that data munching on their own. 

And then we saw augmented analytics, which brought about the introduction of NLP, so natural language processing algorithms, which were very, algo algorithmic centric approaches to sifting through text or to mining through patterns. And so we integrated that with some BI solutions, and so we saw thought spot was a leader in that space to time. 

As well as, SAP business objects rebranded as SAP analytics. It’s cloud. 

And then the 4th wave was the modern data stack, and this is really what brought about the digital transformation. This is accelerated by the pandemic. 

We saw a lot of cloud migration happening here. Looker Mode, Sigma, those are some solutions that came on the scene. 

And this, actually, this wave never matured. And part of the reason it didn’t mature was because of the introduction of generative AI and the explosion of generative So when we talk about generative AI, I want to start really fundamentally what is generative AI, and it is, an artificial intelligence solution that generates something. 

Usually requiring a prompt input. And so if we were to say, what are the top 10 skill sets needed for a c d a I o. 

Right? If we go to chat GPT, it’s going to come up with the top 10 list of skill sets We can upload pictures and say, I want you to recreate this image, in, I don’t know, colonial time frame, and we’re going to see it regenerate that that that picture. So generative AI generates new data and new output. 

And at this point, just by the publicity that that chat GPT has gotten, we should all be familiar with that. In fact, and I’ll go to the chat for the first time. 

Anybody want to throw out there the last time they use chat GPT, and I’ll go first. I used it on my business review, and I’m not going to hide that. 

Right? So I, business review times came around. Right? I’m writing all these business reviews, feed it into chat GPT, cleans it up, makes it pretty easier to consume and read. 

So generative AI is already having an impact. Right? I’d be surprised if anybody on this this webinar hasn’t used ChatGPT for something, even just out of curiosity. 

So it creates, though, a number of questions for us. Right? We can’t sit there and say that we want generative AI. 

It’s not that simple. Generative AI is going to force us to rethink traditional pipelines, to rethink traditional data processes, and it’s just not as simple as saying to gender today, I, hey. 

How many new members between the age of twenty and thirty did we grow our business by in the last month? Right? Eventually, it gets there, but there’s a lot of back end work and data cleaning and consistency that needs to happen in order for us to leverage that. And so we really need to think about when we talk about gender to AI, the bigger question is, how are we going to prepare our data for that? And I’ll talk a little bit more about that, in some other slides. 

AI is going to make products better. It’s going to make them worse, as well. 

So in the race to, install and start utilizing generative AI, what I’m afraid is going to happen is we’re going to see a lot of companies and organizations kind of put wrappers on top of chat GPT. And it it’s really easy to make a chatbot, but it’s really hard to make a good chatbot. 

And so we need to be thinking through, how are we going to leverage generative AI, but also how are we going to put, processes in place that allow for hundreds of test cases and validation of those models? How are we going to be able to integrate with metadata repositories? How are we going to put infrastructure in place, to evaluate the usefulness of this new solution? And so it’s really exciting. It it it’s moving fast. 

The distance between those waves that I’m talking about is getting shorter and shorter. So I’m expecting a lot more innovation to come out as we progress into 2024, but we also need to think about how we’re going to prepare ourselves for that. 

It it’s a lot of times, and, I mean, iridium leak is no exception to this. We have to fight the earth to just grab onto that shiny thing at the end of the road and really think about, okay. 

What’s the journey look like to get there? And so we’ll be talking about that during this webinar. Dark data, unstructured data. 

It’s more accessible than ever. So we’ve gone from NLP algorithms to LLMs, which are large language models. 

This gives us the ability to synthesize and aggregate 100 of 1000 of emails, audio files, videos, But as we take advantage of that, we have to keep in mind that without consistent reliable data, AI is impossible. It just isn’t. 

And so even with these new opportunities coming onto the market where it’s cheaper, and faster to get some of this unstructured data, what we need to think about is how do we build an infrastructure and architect an environment that allows us to actually take advantage of and rely on this data. Most companies, when they talk about data, their use of that term is synonymous with this with structured data. 

Right? So it’s easy to aggregate thousands of data points think of an Excel spreadsheet where every cell is a data point. But what we have to do is retrain ourselves and start thinking about the value that lies in the emails we received, in the phone calls that are service center kits, in the interactions we see, even with our chat bots. 

So how do we aggregate that? To gain insights and to accelerate our business. Ecosystems, Samantha layers are seeing a lot of pressure. 

Excuse me. So the Samantha clears, they’re starting to become our kick or least they have to be willing to shift when we talk about the dynamic and innovative, opportunities that generative AI brings to us in terms of how we structure and store our data. 

So think of a semantic layer as something as a single data layer that would bring together common definitions across the business. So for marketing, we might call a client a lead for sales, they may call it a client, and finance is going to call it a customer. 

So in these semantic layers, we’d bring multiple sources of data together and create consistent definitions across the entirety of the business. It costs a lot to maintain these layers. 

So my question is, and I don’t have an answer to this, but in 2024, are we going to start looking at these Samantha layers as kind of gatekeepers to what would have been in archaic analytics era? And we don’t know yet, but we have to be willing to entertain the idea that generative AI is going to produce a more efficient more robust solution than traditional Samantha layers, but not gone yet because the idea of self-service analytics still remains present. And in order to achieve that for the business, We’re going to need some space where these different business elements could come together and talk in common language. 

And then we have data contracts. And, really, this is this is kind of, a look back to the modern data set where data mesh really looks at kind of enabling domain specific, data cataloging, data storage, and really data as a product where data contracts focus kind of on data as a service. 

And so, there’s SLAs that go along with these different datasets. They’re still domain specific, but it’s really as a service. 

And so, these two things come together. In my opinion, there’s a debate on whether one’s more efficient than the other. 

I don’t think we’ve solved that yet. But the point here is that regardless of whether we’re subscribed to an infrastructure of data mesh, or data contracts or a hybrid or both, we have to make sure that however we structure our data, whatever our our technical stack is like, that we promote quality data if we want to achieve generative AI. 

In 2024, data fluency, technical fluency, those two things are joined by AI fluency. And I’ll talk about what that looks like in a second here. 

Point being is that anybody who has started a data fluency program within their organization and it’s supported at a senior level is going to have a head start. These are all kind of compounding languages, and so the organizations that speak in terms of data, and I’ll actually I’ll show you, a study that McKinsey did that actually showed the value to an organization that, appropriately address data or put it kind of at the forefront of its growth opportunities. 

But anybody that’s started down that path and has kind of a corporate sponsored data films program is going to be ahead of the race. Not only that, but everything just got more confusing. 

I can remember, I started this 15 years ago, where the role of a data analyst, a data engineer, and a data scientist were well defined. And I’ll go through some examples of of where this is getting really confusing. 

But now what we’re going to see is that everybody wants AI in their title. And the individuals that own AI and work with AI aren’t just going to be in data positions. 

So we’re starting to see the introduction of things like, AI engineer or prompt engineer. So understanding the differences between these kind of data centric, or analytical roles is getting more confusing, and I think that’s going to continue into in 2024, user experience. 

So are we still of the mindset that we want 1 BI solution to rule them all? We want to put one solution across the organization, or does generative AI give us the opportunity to kind of create more tailored user experiences So when we deal with the CEO that wants, analytics on their phone, and then we deal with finance that just wants everything in a spreadsheet, and we deal with the business that wants to be able to toggle and manipulate dashboards. Is that found through one BI solution, or are there multiple solutions at play? So Are we looking at a good enough solution that meets maybe 5 or 6 use cases, or are we able to kind of pivot and get the best solution for each use case? And then lastly, one of the things that Jennifer generative AI brings about is, a lot of the, bespoke training technical training that we would need to interact with our data and transformative insights, it’s going to go by the wayside So you’re not going to need to be as technical. 

You’re not going to need to have as much technical fluency when we talk about things like SQL, Python, are JSON. So all of these technical proficiencies, you’re not going to need those to interact with your data. 

So what does that look like from a business perspective? Does the business then take more ownership of some of these AI initiatives? And so these are some of the trends that we’re going to see in 2024, and there’s a few things we can do to get ready for it. Right? Number 1, and I’m going to emphasize this multiple times throughout this this webinar, we need to prioritize use cases. 

So it’s really easy to say, hey. We want generative AI, but we really need to step back and look at what the business needs most and where we’re going to we see the value of that generative. 

And then we need to practice with it. We need to get on hands on experience. 

There’s a lot of open solutions out there that do allow us to practice and test some of these ideas about how we might use generative AI. And so that’s what we need to do. 

We can’t I said don’t hear, but we really can’t over size the need for clean and usable data. I don’t want to sound too cliche, but 15 years, it still rains true. 

It’s garbage in garbage out. AI might help us clean our data, but without clean data, the value that I’m talking about getting back to that business is impossible. 

And so we really need to emphasize, the cleanliness, reliability of our data, and I’ll show you another example of that. And then we need to embrace a cultural shift. 

So giving more power to the business launching that data fluency program now and really disseminating that across the entire organization. And then lastly, build a strategy, have a plan in place, but make sure that that plan’s flexible allows for shift and changes, the business shifts and changes. 

So a lot coming down the pipe in 2024. If we go to the next slide, it’s evidenced by the data So in terms of getting ready and being able to interact with this this new AI, I look at a survey from Geekwire that actually looked at 1500. 

It surveyed 15 100 Americans, 84% of them were literate about AI. So even today, a lot of people have a hard time grasping the idea of a prediction. 

Right? When we look at weather patterns, we look snowstorms, even our Uber ride. So grasping the idea that we’re talking about a margin of confidence, not an absolute and understanding kind of what goes into training that model may not be appropriate for what our business is trying to do. 

And so, really, expanding on data fluency, which a lot of organizations kind of confound data fluency and technical fluency, There’s a difference. Data fluency is all about understanding, where data comes from. 

It’s having an understanding of some statistical concepts It’s having an understanding of how the business uses data. Technical fluency is is just that the SQL, the Python, the actual tech stack to the solutions you’re going to be using. 

Databricks. And so when we look at that, there there’s a pretty big gap, in this survey as to who’s ready to actually interact or has an understanding about how AI works. 

We talked about that dark data There was actually a study by Experian, that said organizations lose 15% to 25% of their revenue as a result of bad data. There was another study in the Harvard Business Review that says it cost the US $3,100,000,000,000 due to data related inefficiencies in a year. 

So think about the time it takes to identify an issue or an error in processing correct that issue in processing, put processes in place so that doesn’t happen again. You know, how much time and energy and resources you’re putting into that? And so again, just hyper emphasizing the criticality for clean, reliable data. 

And this is where we talk about some of the roles that are coming out. So, in 2012, data scientist was declared the sexiest job of the 21st century. 

It was echoed in 2016 by Glassdoor, but in 2022, analytics engineer is the sexiest job. And so now we have things like, AI researchers, prompt engineers coming on to the scene. 

And so the taxonomy of data analytics is getting very confusing. And we might even ask that it I’d be curious to hear the answers that I got if I asked, what is a data scientist? The best definition I’ve ever heard ever heard is it is an individual who understands more about statistics than an engineer, and more about engineering than a statistician. 

So even in that, which I think is the best definition I’ve heard, it’s a little vague in terms of what they actually do. So be prepared for that. 

Start looking at, when you’re hiring for these positions that you think are going to help you leverage generative AI. Consider the titles that are out there because they are changing. 

And then the other thing I would say too is that we look at 2024 and we’re excited about expanding kind of how we’re using data and leveraging to drive value. Be careful of costs. 

There was a team pays, recent blog, Adobe was spending $80,000 a day on cloud costs and didn’t even know it. They had no idea. 

It took them a month and a half to figure it out. So I know we’re all excited to get here, but these are just a few things to keep in mind. 

So some trends coming down the pipe in 2024. Evidenced by some of the data that we see here. 

And so within our own industry, there’s still some fundamental challenges that we need to overcome. And so this is from, digital banking. 

Data reliability. I’ve said it many times that this is this is our ability to trust in the data that we’re getting. 

If we can’t trust in that data, how can we feed it into an AI model to give us greater insights? Right? So that still is the predominant challenge that we face when it comes to leveraging our data and to really accelerate the business. Generating insights is still a challenge and then lack of resource to understand the data, to analyze the data and inadequate data management tools. 

So these are all things that I’ve talked about in consideration of the trends that we see maturing in 2024. So as we go to take advantage of some of the newer opportunities that we see with these technologies coming out in the marketplace, these are things we’re going to have to keep in mind at every step of the decision making process. 

But if we can get it right, There’s also evidence that it will result in tangible value to our business. And so here, this is from an older study, actually, but still rains true. 

So this is looking at leaders and laggards across industries where a leader is defined as an organization with more than 10%, revenue growth that they can attribute to their use of, data and analytics. And what we see here on the left hand side is that 51% of leaders have a hybrid center of excellence. 

That means they have a cost center built out and set up for the purpose of cleaning of munging, of structuring, of storing, and taking advantage of the data that their organization produces and consumes in order to return and add valuable insights back to the business. And what it does is when you have that hybrid center, a center of excellence, a central cost center, it creates synergies. 

It creates focus. What we see in kind of less mature organizations is we see a lot of decentralized or pocketed analysts, and these are these are your spreadsheet gurus, right, These are the ones that go into the system. 

They pull it down. They bring it to a spreadsheet, and they give it back to one person. 

With your center of excellence, you’re distributing it across the entire organization. We also see that leaders actually just spend more time talking about data and analytics executives do. 

47% of leaders spend more than 31% of their time versus 78% of laggard spending less than 10% of their time. So the focus, the hyper focus on data and analytics is is, helping promote and drive these organizations to success on the left hand side to the point where It’s a part of their hiring criteria for both management and non management. 

70% of high performers had at least one data leader on their on their c suite team. So that might be your chief data analytics officer, your chief data officer, It could be your your CTO playing the part. 

And I act I actually expect those titles to change a little bit in 2024. We’re going to start seeing, chief data, or CAIO, chief artificial intelligence officer. 

But if we can best those challenges, we can prepare ourselves 2024, there is evidence that it’ll drive tangible value to our business. And so as we strive or get ready to achieve that tangible value, we can break it down and make it really simple. 

And we can look at 7 key elements of successful data strategy. What you’re going to notice, the one takeaway I want you to have from this diagram, is that it’s not just technology. 

Can’t just go out and migrate everything to the cloud, spend, 6 plus figures on the resourcing resources needed to actually architect, build out, analyze, synthesize this data that now sits in the cloud, and expect to be successful. There’s a whole other side of it that’s driven strictly by the business. 

Who are your stakeholders? What are your value propositions? What is it going to look like in the end? We need to turn to the business to actually set the target. And then we can go back and forth in terms of how do we execute against or how do we achieve this target capabilities are afforded to the business with the introduction of new technologies. 

And Joe is going to talk more about this, in a minute here. When he gets to use cases. 

So Meridian Link’s no different. We’re doing the same thing. 

We want to be able to provide solutions, innovative solutions, to our clients, to our users that help accelerate and grow their business, that help automate their business, that helps make their financial institution successful in a digital market space. From a consumer standpoint, from a mortgage standpoint, a business standpoint, a collection standpoint, we’re going to underpin all of that with the data needed for that acceleration. 

And the insights needed for that acceleration. But it’s going to start with the use case of the automation and the insights that we want to give to our users. 

And then we’re going to support that with data and cutting edge, innovative analytics. Specifically, what you’ve seen over the last year, where we’ve made big investments with Meridian Link is in Meridian Link Engage, insight, and data connect. 

As well as the scorecard, which isn’t mentioned here, but I’ll mention that in a second. And so we are putting our money where our mouth is. 

We are, looking at these innovative ways. We’re looking to adopt and leverage, newer technology, especially that that’s coming out in 2024 with generative AI. 

There’s a lot of exciting stuff going on there. But we’re going to continue to invest in these spaces and we’ll continue to return that data driven value back to our users and our clients. 

One of the ways we’ve done that is with the scorecard. So when we talk about developing those use cases, those business use cases, we can use data to help us do that. 

Right? One of the things that we’ve done at Meridian League has developed, a series, actually, of kind of peer to peer performance views. And with these views, we’re looking at things like processing times. 

We’re looking at things like cross sell. We’re looking at things, like fallout, to really understand where our users are over performing their peer group and where they’re under performing, where they have an opportunity to improve that performance. 

So these scorecards, these peer to peer performances don’t fix the problem. But they do give us the very unique opportunity to break down our processes, right, in our own organization and really hone in on where it is that we have opportunity to grow and where it is that we’re really excelling and doing well. 

And so on the left hand side of the screen, if I were to break down loan origination, which the scorecard does, we might look at it this way, right, where we get the number of applications. So year over year, how many at bats are we seeing? Are we seeing an increase in the number of times we’re at the plate? I’ll use baseball analogies for this, but that’s going to look like membership growth rates. 

So are we seeing more applications? Are we seeing actual member growth, not account growth, but member growth. Then are we seeing account growth? Do we see that increase or decrease? And then how is that changing real to our peer group. 

So those are ad bets. If we get the ad bat, how many positive decisions are we making? Are we able to target the right member or the right customer, the right time with the right product? Right? So even if our at bats increases by 15%, we may lose that if we’re targeting the wrong person, the wrong time, wrong solution. 

And then once we get those, if we have that batch, decision needs base hits, so if we go over to funding, right, how often are we closing the day? How often are we bringing renters home? Are we increasing our share of wallet? Do we see that? Are we innovating products and solutions that drive that increase? And then is where Meridian Link again with what I showed previously, wants to partner and actually help our customers do better and succeed in this space and really identify those opportunities to develop those business use cases and then and then execute on those opportunities with solutions like Meridian Link insight. Where regardless of where you’re at in your data kind of maturation or your data strategy, we have opportunities to plug in and accelerate in certain aspects of that growth cycle, Meridian Link insight being one of them. 

So whether we’re looking at 15 minute q data refresh rates so we can implement a workflow, a work, workflow management process for our underwriters or originators, or whether we’re looking at peer to peer growth and really trying to trying to take a strategic view on what we do next. We have solutions that help drive that forward. 

And then again, with data connect, when we talked about data reliability and data consistency being key to leveraging AI, or being able or getting ready to leverage AI, we need to make sure that what we’re talking about here is data that we can actually sift through. To understand its reliability, to understand its application, and understand its use case. 

And so here, what we want to be able to offer, we it’s hard to do a webinar like this where we talk about the significance of everything that’s coming in 2024 significance of clean, reliability, reliable data, the significance that generative AI is going to have on our current processes without being able to say, hey. By the way, as your partner here, we’re going to offer, a comprehensive data solution. 

We’re going to offer a way to accelerate that journey in a way to prep yourself for 2024. And so you can see here where we where we have solutions that go directly to the business, we’re going to directly to your data users to help accelerate that process. 

And so, as we look at this, again, I think one of the most important things or takeaways here is that we do need to try to understand where we’re going to start knowing that all of this is coming. And then what are some of those use cases do we develop those use cases? What are those summons use cases we’re going to target first? And so at that point, I’m going to go ahead and turn it over to Joe. 

And he’s going to start talking through that. 

Where To Start With Data Strategy 

Joseph Mearn 

32:54 – 33:57 

Thanks, Chris. That was great information there. 

Before we get started to talk about kind of, you know, where do you start to begin your data strategy? Just going to go into a quick poll here. So, again, if you can jump over to the poll tab, and the question here is, where are you at currently in your journey to data sophistication? Do you have a data strategy? Do you need one? Are you currently executing 1 and driving growth through that? Are you all set and you’re already at that kind of data excellence? Position as Chris mentioned there. 

So, jump over. Take a couple seconds here to get some answers in, and then we’ll keep going. 

Alright. So how can data be a growth enabler for your financial institution? And kind of what we see here is an optimized data strategy and, you know, effectively using data analytics will help you to compete within your market. 

Data as an enabler. 

Chris Dumas 

33:57 – 33:57 

It’s. 

Joseph Mearn 

33:57 – 55:16 

going to help you drive lead generation. It’s going to help you fill your funnel with opportunities to grow your lending and deposit portfolio. 

And data can improve your decision in, your pipe top pipeline optimization. It can also allow you to improve your product offering and your services that is going to help drive consumer engagement, help retain relationships and really minimize bosses. 

You can use data to know how to approach and when to approach your consumers, know what best channels to use and know what products they have, analyze their behavior from a transaction for active and determine what their next best product is or what they may be in the market for. You want to show them that you know them through data and keep these primary relationships, grow your prospect funnel and use data to grow your, again, your lending and your deposit portfolio. 

And so that’s why it’s important from your data strategy, to look at partnerships. Right? Financial institutions are seeking fintech collaborations from a data and digital transformation perspective. 

These collaborations, they’re going to be dependent upon the needs of the financial institutions. And if we look back to some of those data struggles that Chris mentioned around, you know, reliability generating insights, having resources to analyze the data. 

This is where these collaborations can become very important. Right? And there are different areas, of needs categories that these partnerships can help cover. 

Right? The first is going to be operational technology. This is going to be used to enhance in FIs processes, their margin capabilities of data, their technical infrastructure for data. 

Right? There’s going to be consumer oriented technology, which is going to be used to enhanced consumer facing aspects of the business. Right? And then there’s going to be that front end fintech partnership that’s going to help, you know, diversify consumers and revenue in by leveraging those different fintech providers. 

And so, partnerships continue to increase, year to year. We see that financial institutions are partnering with third parties for their digital transformation to help drive, new relationships and data solutions. 

Primarily, we see that in the account opening onboarding platforms. We see that in, lending and consumer and business solutions. 

We see it for targeted marketing platforms. Digital banking, security fraud, and lastly, within that financial wellness. 

So, data plays a major role plays a major role across all of these partnerships and solutions. So again, knowing your FIs landscape and what you want to optimize, can help you from an internal and internal resource and partnership perspective there. 

So with all that, you know, how do you start? Right? Where do you start today to create that data strategy, what Chris provided. There’s a lot of information there. 

To me, it’s a little bit overwhelming. So you have to figure out, you know, What are the first steps that I’m going to take? And we see over 50% of financial institutions, they really lack that modern, infrastructure that’s required to support their data initiatives. 

The first thing you need to do is you need to first determine from your data strategy. What are your key use cases? Right? What are the goals and the key metrics that you are tracking to for your financial institution. 

They’re probably going to align with market trends in kind of what your business strategic priorities are going to be. So, you know, are you trying to improve your decision in? Right? Are you trying to grow loans? Are you trying to grow deposits? Are you just trying to grow the overall universe of your consumers? Right? That’s going to be some of those key metrics. 

Do you want to understand your existing relationships and what value they bring to your financial institution? Start small, aim for, you know, 3 to 5 scenarios, that you’re looking to drive towards. Right? And when you Think about transforming from a digital perspective, you want to think within the agile mindset. 

Right? Agile methodologies of incremental small improvements because there’s a lot there that Chris showed. There’s lots of trends coming. 

There’s lots of information. And you’re not going to get there. 

Immediately. Right? You want to look at small incremental improvements to help you along the way execute on that data strategy. 

Right? And so after you find those kind of, you know, use cases, you want to map your desired outcomes to them. Right? So just saying, okay. 

I want to do x is. But what am I trying to get to? Right? What are you trying to accomplish within these use cases? Do you want to increase loan volume or balances by a certain percentage or are you trying to get to a particular volume or dollar amount? Right? Make it measurable. 

If you’re trying to grow interest income by how much right, what’s the kind of value you’re trying to get to, make sure that your outcomes are very specific and measurable and meaningful, right, and they tie back to those use cases. You know, look towards again those partnerships to help support you along the way there. 

And then, you know, now that you know what you want to do and you have your outcomes, what do you need to do and you need to create that really data optimization strategy? So you have your key use cases. You have your desired outcomes. 

Do you have that data within your portfolio to get you to those desired outcomes? That’s where you need to complete a data inventory, identify gaps, and overlaps within your data access, your data strategy, that will deter you from maybe meeting those desired outcomes. Can you leverage external data sources, right, to enhance these gaps or improve on your data strategy. 

So really, you know, consolidate, optimize data, so that you can make it actionable and meaningful, or execution within that strategy. We talked a little bit about, again, all the you know, high performers, the leaders, and the lagers, and, you know, trying to ask that poll question. 

Where are you within your kind of journey to sophistication? Right? It’s a multi phase approach, and we’re not going to all be at data excellence right away. So we want to continue again to focus on those shifts in the agile methodologies to really train form you, and know where you’re at. 

Right? So is are you currently at a stage where it’s legacy reporting? Right? Do you rely on basic tools like Excel, for recording, outlook for, data sharing, what type of practices do you have in place in other systems integrated or not integrated. Right? Are your data analytics siloed? Do you not have access to decision making is it, you know, just focused in specific areas like executives or IT, right? That’s going to lead to mistrust and data driven decision in and you’re not going to have clear KPIs standards or processes in place there. 

Are you at that fundamental data adoption phase? Right? Here, these financial institutions, they employ a simple, but more regular reporting mechanism, right, where they begin to really recognize the importance of data in decision making, but they may lack some of the sophistication of dynamic or real time analytics. There’s a good understanding here of KPIs, but these are y’all not yet really integrated into, you know, the daily business operations and data driven cultures currently, I would say, adds probably early stage. 

But that’s when you start to move into, driving growth through data. Right? Have you develop that data driven culture. 

Do you have a small group of users that are leading in using analytic efforts? Creating key datasets that are constantly being updated. Right? It becomes more frequent, more timely and use in decision making processes. 

Right? They’re more automation, more scheduled, reported, and processes are probably established here. More users have access to the essential KPIs. 

You even start to focus here and align some of your business goals with the data insights that’s going to help and improve on those operational efficiencies and really start to push those data driven growth strategies. Right? Then you start to move down that road map, right, and you and you’re starting to grow into the data excellence stage where you’ve kind of established data center, get that center of excellence. 

You have actionable insights that are really fueling your growth and your operational efficiencies. Data is democratized. 

You’re really fostering that personalized consumer experiences. You have responsive decision making. 

You’re including and involving, you know, from evolution from prior stages into your data warehouse, real time delivery of insights. You’re using it for forecast and you have combinations of data sources, right, 1st party, 3rd party, from all different types of platforms into a single solution, It’s going to move you into that, you know, enterprise data expert where, you know, financial institutions here, they leverage the analytics across the entire value chain. 

And it’s governed by our enterprise wide strategy from a data perspective where it’s advanced data marketplaces, machine learning, artificial intelligence models, all driving consumer behavior and informal growth strategies here. Typically, this is where you get into that cheap data off sir, or the CAIO as Chris mentioned, right, that’s where that new, role will start to come in here. 

So, you know, that’s a that’s a journey. But you want to take steps. 

And so how do you take steps from maybe the beginning level, to move up to really that foundational level. Right? And so when we look at a data strategy, we want to really start to determine, you know, what are those use cases? Let’s put them into practice here. 

For example, let’s say financial institution priority is focused on extending wallet share. If you get opportunity, you know, that you have, you know, volume coming through your point of sale, within branch as well as online. 

And it’s good to know these consumers that are coming through, what type of relationships do they have with you how much of a wallet share do you currently have with them. Right? And the good traffic’s coming in on a monthly basis, and you know that you can auto cross sell these at the point of sale. 

Right? It’s a great time. You’re going to have data at your fingertips to kind of start that auto cross sell process. 

And you want to better understand your members by analyzing, during that application process, maybe some of the credit data, right, and what can you apply to that? But if you could generate new offers from these interactions with the new and existing members online and on branch, to really capture relationships elsewhere. Again, part of your data strategy is you want to be able to find that wallet share information and be able to act on that and leverage the data and analytics from your application pipeline to really drive new loan and deposit growth within that pipeline. 

So really turn that LOS into a into a demand generation platform as well. And with those kind of note key use cases, what are those desired outcomes? Enhancing your digital transformation by spend in your LOS strategies to include that demand gen, right, but what are you actually trying to do? How are we going to measure your success and let’s say you want to increase the number of products per household by 1, right, that’s tangible. 

That’s the goal that you can get to. You want to be able to improve your consumer lifetime value by you know, 3%. 

You want to reduce your cost of acquisition by 2%. And then you want to, you know, have more measurable outcomes the data points that you want to have access to really track this are going to be focused on the number of households, that you have. 

Right? The number of primary relationships within those holds, the number of accounts per household. What is your cost of acquisition and how do you budget for that? Right? And when you do act on these situations. 

What is going to be your return on investment? What can you expect? Do you have the right trade line data? Do you have the right first and third party data? To do the segmentation as well, do you have that marketing data that you can use from a communication perspective to grow those relationships? So you set up your use cases, you set up your outcomes, and now you want to optimize that strategy. Right? As you’re growing, you might not have the exact systems or processes in place that are right for that cross sell opportunity. 

Right? It might not be automated. It might not be simple. And they can be enhanced. You might have too much human intervention during this process today where you have to rely on, you know, those individuals who actually execute on this, rather than letting the data kind of talk for itself. 

Right? Maybe limited marketing technologies or no marketing automation available, you might not be able to get access into your current pipeline. So you might not know what opportunities are out there. 

And you might not be able to really segment or analyze. So going back to those problems around the reliability, the access, and the resources to analyze it, That’s where you, you know, you have some opportunities to optimize. 

You also see that, you know, your primary relationships, they have relationships elsewhere. We have data that shows that most consumers have, on average, just over 3 relationships with financial institutions. 

And at that primary, they hold, you know, just over four accounts within those primary solutions. So you do know that your consumers have relationships elsewhere and you have the opportunity by leveraging that data to acquire them, and build that loyalty so that it doesn’t really, you know, deteriorate over time. 

We also know that people are going to switch bank providers in, you know, the right offer at the right time, as Chris mentioned, data driven personalized offers within that data strategy can help really acquire that. And we’ve seen here that, you know, go members, your consumers, they are moving. 

Right? Balances are being shifted discretionary spend changes are happening, and they’re looking for products with better rates fees more rewards. Right? However, if the primary financial institution was able to give the consumers that kind of same offer that they left or look for, they’re more likely to stay and they want to stay with their primary financial institution. 

So we have having that data part of your strategy, be able to execute on that. It’s pretty critical. 

And so talk a little bit about running, like, one in the solution we kind of have in place that allows you to tap into those cross selling power, algorithm in one, right, where you can leverage the flagship consumer open end products. You can leverage Meridian Lake portal and tap into the ability to really cross sell those consumer products, that may have relationships, right, or, sorry, not may. 

They do have relationships with other financial institutions. An example here would be an existing or new member. 

Right? They walk into the branch, or they log on to home banking, or they come to your website, and they apply for a new vehicle loan. They’re not cross sold at the point of sale. 

Right? However, what if we could 7 days later put an invitation to apply to those consumers because during that application process, right, we found that relationship elsewhere. So how do we how do we get there? How do we get to that offer? It’s through the normal application process, right, where, consumables credit Merid, we capture that credit pull. 

We survey for, trade Cross sell product dashboards within MeridianLink that starts to build these different cross sell opportunities. Each product dashboard, it’s really going to represent, you know, who you can target, So who are those target applicants? What is it going to cost you, and what is your kind of estimated return on investment? You can also get to the underlying data, right, that’s very important around, existing targeted balances, average balances of trade lines, what do the remaining terms and the rates look like, and do additional segmentation that gets you to a target list that, you know, when you’re ready to acquire and go after those relationships. 

And so you can execute that, through Meridian Link where, again, just a couple days, we can do a multichannel approach through email, direct mail, SMS, and digital for these invitation to apply for new products here. Once that campaign’s running, we’re then able to come back in and provide some analytics in tracking of the success of that campaign. 

Right? So as you’re targeting these members, we’ll know who you targeted who applied, who is declined, approved, funded, what were the dollar amounts requested, approved? And again, what are the rate in terms of all of these different products? Again, that all comes with additional data that you can further of that campaign. So I want to talk about a financial institution today, Fedchoice who, within their data strategy. 

Right? They’ve called out, but they have key metrics that they want to capture more, share of wallet from existing members. And, you know, in a 12 month period, they’ve ran 5 campaigns, through this kind of data strategy. 

3 of them were personal loans, 2 of them were for HELOC and they targeted, you know, close to 4000 members for these personal loans, and they had a conversion rate of just 4. 79% so they’re capturing these relationships elsewhere. 

They’re growing their share of wallet with their members. And same thing from a HELOC perspective, right? They targeted 343 members out of this and had just over 9% conversion rate for that new product offering. 

So leverage in that data, of those relationships, they’re able to grow that share of wallet. And, you know, this was a challenge that they saw, where they were searching for a solution to, you know, offer their members additional loan opportunities. 

Had some successful campaigns, but they struggled with additional ways to increase their breath and their segment, their new audiences. But they knew that data was available. 

Right? They knew they could get to this data, and they knew that personal loans and home equity products were the best opportunities for them. Again, based off of their data analysis, to provide their members with, you know, potential rate and payment relief through these products. 

And again, through the platform, they were able to capture, these members. Right? They looked at application data over the last 6 to 12 months. 

Who was applying for consumer products, who was being approved, and they didn’t apply for that personal or HELOC, but during that time being, we saw they had these types of relationships elsewhere, and so that’s how we segmented and targeted these campaigns. And really good results came from these campaigns. 

They were close to 2000% return on investment. And, you know, I’m looking at 2 of these 5 that we did here. 

And they were, you know, good qualification, good communication, and interaction with the campaigns where there was products being opened that were being asked for, but there was also ancillary products that were being opened up along the way too. So it continued to grow more than just what they asked, but continue to expand really that share of wallet. 

And, you know, so kind of just to close here, for going to q and a. So, you know, in the end, how do you turn that consumer data into opportunities of growth? Right? And it’s leveraging internal and third party data, and partnerships to really deliver consumers what they want when they want to through the channels that they’re looking to be messaged through. 

Right? With that optimized data strategy, allowing for conversions in in targeted personalized offers, you’re going to be able to attract new consumers, retain existing relationships, drive new originations for loans and accounts as well as increase engagement within the existing product offering. So that that data driven strategy, it’s really going to guide real time opportunities, for cross sell, preapproval, prequal, direct and indirect lending, and really help from that full, you know, all of when you look at all of life financial moments here. 

So now that you get your you have your data strategy. You know where to start. 

You know how to create your your metrics and your desired outcomes. Remember, it’s all about those incremental improvements that are going to add up to those kind of big gains, over time. 

So with that, we’re going to go into, I think, one more poll here before we move on to Q And A. 

Q&A 

Lindsey Weldon 

55:16 – 55:35 

Alrighty. Well, just a few minutes left. 

I’m going to hop into the Q and A side of things. One of the first questions that we have is what is the biggest challenge you see in FIs that just want to get started? That’s probably for either of you. 

Joseph Mearn 

55:35 – 55:37 

Chris, do you want to start? 

Chris Dumas 

55:37 – 57:40 

Yeah. I’m just I’m hopping back on here. 

So I think, listen, one of the biggest challenges that that I see when I’m when I’m talking to customers that that are users that just that they want to get started I don’t know why there’s this there’s this sentiment or attitude. Like, I’ve got to invest so much money. 

I’ve got to build out, a central data repository. I’ve got to go hire staff. 

I’m got to come up with processes that move the data around, and then I have to sift through that data. You don’t. 

It can be iterative And I would actually encourage it to be iterative without overcommitting to something before you see the value of it. I remember companies. 

I’ve talked with companies that have hired data scientists $250,000 a year, and they don’t feel like you’re getting the value out of because they’re hiring a super technical PhD trained individual who is designed and built and has practiced and studied creating predictive models for the last 6 years. And then you want that person to go present to your executive team onto the value of the model they just built. 

Right, when they’re going to be talking about confusion matrix, KS scoring, rock curves to an executive team, because that’s the language they speak. And so I think it’s really important when I talk to users that they understand an iterative approach. 

Right? So that your executive team feels like they’re getting value out of what they’re investing in so that your users feel like they’re getting value, tangible value out of what they’re investing in, and then they’ll want to invest more. So I think one of the biggest challenges in talking with with clients that that are just trying to get started is they have the sense they have to They have to achieve generative AI today, and they don’t. 

It’s a process. And it’s more expensive to catch up than keep up. 

So incremental investments rather than trying to say, hey. This is our our big spend in this year. 

It’s not you don’t have to do all or nothing, to Joe’s point. It it’s a very iterative process and cycle. 

Joseph Mearn 

57:40 – 58:06 

Yeah. Just to kind of add to that, it’s definitely, you know, trying to take on too much. 

And some of that comes with, you know, proper resource allocation, knowing the kind of resources that are needed to execute on that data strategy, and then having the those right resources to actually follow through, with the execution and kind of build out that that data team or team needed to do the data strategy side. 

Lindsey Weldon 

58:06 – 58:13 

K. Two more quick questions. 

One is where can I find my scorecard? 

Chris Dumas 

58:13 – 58:49 

Yeah. So if, so the scorecard is something that that we’re making available to all of the Meridian link. 

Users and customers. So, reach out to your AE or your BDR, and they can schedule time to go through that. 

It’s sitting in the system. If you’re on the platform, we’ve built out multiple versions for you already. 

You just have to reach out to your A or BDR to schedule a session with them, and then they can walk you through it. You guys can identify some areas where you’re where you’re outperforming and also where you’re underperforming. 

You might want to focus some more effort and energy. K. 

Lindsey Weldon 

58:49 – 58:57 

And our final question for today is how quickly can I get started if I want to drive share of wallet? 

Joseph Mearn 

58:57 – 59:16 

You can get started immediately. Again, reach out to your AE. 

We have some, you know, new product offerings around share of wallet growth, new add on modules, that we can definitely start the discussion with to, get you moving on the Sheriff Allcro. 

Lindsey Weldon 

59:16 – 59:35 

Alrighty. Well, that is it for today. 

Thank you guys so much for joining us. And we will have a session very similar to this one at our Meridian Link Live, and we’ve got the link in the chat if you would like to look into more information. 

So Thank you again. We look forward to seeing you next time. 

Chris Dumas 

59:35 – 59:38 

Thank you all. Thanks everybody. 

Take care.