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'Consolidating Data Lakes and Silos for Financial Institution's Cost Savings and Benefits,' with Bobby Childs, Vice President of Business Development, NXTsoft.
Good afternoon. And thank you for joining us today. My name is Bobby Childs and I'm the Vice President of Business Development with NXTsoft, and on behalf of our entire team at NXTsoft I'd like to thank you for joining us as we continue on with our Pausing the Pandemic Panic series.
And today we look at consolidating data lakes in silos for cost savings and benefits specifically for our financial institutions. Data really is the lifeblood of any organization today, specifically in the financial industry. And what I'd like to do is simply just walk through some statistics, talking about the power of data and the value of it.
And then talk through some of the challenges we face with dealing with that and dealing with different data lakes and silos, and then talk about solutions to be able to really bring not only benefit to you and your organization by the utilization of your data, but also a [better 00:00:46] fit and how to bring that data into one central location.
Again, thank you for your time today. If you have any questions at the end of this slideshow, there will be a slide with a contact number and email address on it, or you can feel free to drop questions in the chat bar, and we will try to field those as we go through it.
Now, with that, let's jump in and look at a few statistics and talk about really the power of data. So wanted to start by really just looking at some specific statistics, owned data, in and of itself.
Having been in the financial technology industry for as long as I have, specifically on the data management side of things, it was really common to me to see some of these numbers, but at the same time, it was very shocking how large these numbers have grown as I researched this to prepare for this webinar, just over the past couple of years.
To start with let's look at this very first line. Over 2.5 quintillion bytes of data are generated worldwide daily. I didn't even know what a quintillion was. So I actually had to go out and look it up. And I wrote the number out for you, literally two comma five, and then sets of zeros for six straight runs to get to a quintillion. That's how many bytes of data every day are generated worldwide.
And this is pretty staggering as well too. And now, I just don't want to read from the slide show. You can read all these, but more data was generated in the past two years than in the entire human history before. So let me say that a different way. In 2018 and '19 combined, we generated more data in those two years than 2017 prior all the way to the beginning of time.
In fact, 90% of the total amount of data ever created was produced in the past two years. It's unbelievable how exponential growth we are right now in the data generating world. Every day, people will look at statistics on usages, on social media, on Google, and things along that lines, and we'll see those shortly, but it's unbelievable how much we're generating.
Google sees over 40,000 unique search queries per second. That's over 1.2 trillion annually, and people, literally individuals, we're not talking business now, share more than a hundred terabytes of data on Facebook, just in a day's time. In 2019, Twitter users sent over a half million tweets every minute. By 2022, there will be 4.8 billion internet users.
Now, keep in mind, our population right now is roughly about 7.8 billion, if I'm not mistaken. I think it was 7.6 last year. It was 3.4 billion in 2017. So we're jumping by over a billion users by 2020.
I love that, the average person spends roughly six hours and 42 minutes a day on the internet. If you think about that and you back out sleep time, it's amazing how long we're on the internet. I actually looked at my time and my usage for the internet, which was over that amount by how much I actually work on my computer, especially this day when a lot of us are working remote. We're doing zoom meetings and things along that lines.
Every second, this one stat caught me, every second every human creates 1.7 megabytes of data. And this is a pretty staggering one as well too. 1.2 billion years is the number of years humanity collectively has spent online in 2019 alone. These statistics are just staggering in what they look like and staggering in how much data we're creating.
Now, let's look at the value that this data brings. So we have all this data out there and the beauty of it is we're able to actually glean a lot of value out of this.
In fact, different organizations that I'll share some stories shortly, have used these to make huge financial impacts on their business. 90% of enterprise currently say that data analytics are key to their digital initiatives. The number of firms investing over 500 million annually in data science has grown by 10% alone from 2018 to '19.
Roughly $187 billion was spent on data analytics in 2019 alone. If you don't think there's value in the data that you have specifically as a financial institution, look at that. 187 billion spent last year alone. Banking accounted for 13.6% of that global big data revenue.
The global big data market for software and services was valued at slightly over 50 billion in 2018 and jumped to over 180 billion in 2019. And then, by 2022 we're expected to get over 275 billion.
Now, I told you about some stories, they're utilizing their data. Netflix saves over a billion dollars annually on customer retention. I'll talk more about that story here very shortly, and 70% of businesses believe that a data warehouse optimization is critical to success.
If you don't believe that you're sitting on data, all you got to do is look around at the records that you have and that you're having to keep, specifically as a financial institution. Even your legacy and your archive data, you're required to keep for a minimum of seven years. Things like check statements, loan documents, et cetera, et cetera. Sometimes up to 10 years. All this data has power in it and can be gleaned and it has value.
Now that we've talked through some of the value of data, let's talk about some of the footprint that it's got. It would take an individual, one person sitting down at a computer, over 181 million years to download all the data from the internet. That just absolutely blows my mind to think about that.
50 years from now, this one got me too when doing some research, 50 years from now, there is projected to be more deceased people than living on Facebook because accounts aren't getting closed and they just sit out there forever. Facebook doesn't force the closing.
In 2019, this is what I spoke a little bit on earlier, the global population was 7.697 billion. Now, out of that 5.1, 1 billion we're unique mobile users, meaning using a cell phone or device, that's 66% of the global population. And now, keep in mind some of the poverty-stricken countries, and third world countries, and everything else going on, but still, 66% of our entire global population were unique mobile users.
58% were unique internet users, 4.437 billion of them. Now, we have 45%, almost half the global population, are active social media users, either mobile or internet-based, roughly 3.49, 3.429 billion of each one.
Data not only is everywhere, it not only has a huge value, but it has a massive footprint in our society, not only of what we're generating, but what we're leaving behind, even when we pass away, even for that matter.
So now that we've talked a little bit about data and its value, let's talk a little bit about it specifically in business. To start, a little bit of the story to elaborate more on Netflix. So Netflix has data collected on its more than 58 million subscribers, which is how it creates this algorithm, which has really been found to be quite successful.
There's a quote actually by a gentleman, a man named Enrique Dans, who was at the IE Business School and teaches innovation there, that literally says that Netflix new series is not being made because a producer had a divine inspiration or moment of clarity, but it's because the data model says it will not work.
Netflix puts so much weight in this data model and it's algorithm that they will trump that over intuition and everything else and go by the data model. That gives the company a huge leg up on the competition, such as other big entertainment companies like Disney or Hulu. And to tangent off that, now this is Netflix who obviously has quite more data than a lot of us in our regular organizations would, but at least a hundred terabytes of data is stored by most US companies across the board.
Now to give you a visualization of that, that is enough, if you combine all that data, that is enough to fill 10,000 Libraries of Congress each year. And we all know how big the Library of Congress is. We're talking 10,000 of those. Over a billion global workers are predicted to have their behavior altered by these data-driven algorithms, like we just talked about with Netflix.
Our industry, the financial industry alone, spends $9 billion on data investments. And think about the data that you're sitting on, the data on your clients, on your customers, and how much you have on it. By the end of 2020, the customer will manage 85%, this is crucial for us to hear, your clients will manage 85% of their relationships with any business with zero human interaction.
They want to go through an app or a web portal, or something along that to bypass the human interaction, which generates data for us, but also something for us to look to from an innovation standpoint. 79% of executives believe that failing to embrace data analytics will lead to bankruptcy, and proper data utilization by a business can bring up to an 18% increase in revenue, which is a staggering number to really think about.
So as we continue this talk, let's talk another example. Sometimes it seems that when you go onto Amazon's site that they almost know you better than what you know yourself, or they at least know what you're thinking, in terms of buying ahead of what you may even know. The reason being Amazon, again, has pools, and pools, and pools, and pools of data.
And those targeted messages from Amazon and buying suggestions are a result of data analytics that lets the retailing giant know about when you make your purchases, how you rate them, and what other customers with similar buying habits are buying. Just for an example, Amazon, and this makes common sense, but Amazon's learned that those who bought TVs, atypically buy a TV mount. And if you go on Amazon right now and you look to purchase a TV, it's immediately going to suggest a mount or other accessories for it.
Seems common sense, but it also tracks that through other avenues. When you think about when you're buying shoes or apparel, or you bought those before, it will suggest, "Do you want to buy this again or look at other styles?" They're constantly taking the data they have and utilizing it to grow, and as we said on our last slide, grow their revenue up to potentially 18% or more, and continue to grow their business from that.
So now, I want to take just a moment and look at the data gap that we're really talking about. And some of the stretches that we would have. 99% of organizations think the data is essential to their success. However, 95% of businesses state that they need to manage their data better. And here's the reality, and this is scary, thinking about all the data we're sitting on, 88% of that data is all ignored. So most businesses are only utilizing about 12% of the data out there, or they have access to.
27% of executives are worried about the accuracy of their data. And that takes us into a little bit more of our next phase of this webinar, and to really some of the data challenges that are out there for us and the things that we're looking to that can really keep us from capitalizing on the value and the amount of data that we have within our organization.
So what are these challenges we're talking about? Number one, we're talking about the amount of data that's being created. As we talked earlier, we are generating uncountable numbers of an amount of data all the time. You, as an organization, your financial institution, your clients, your customers are generating data daily in and out.
So just managing that amount of data is one thing. What happens to it? Well, the data comes in and it sits somewhere. Well, where does it sit? That's leading into our conversation on our next few slides. It sits on some type of a server or on some device within your organization, maybe in the cloud, but that basically becomes a data silo or a data Lake of some sort.
The other challenge is meaningful and real time data. We have so much unstructured data out there, or obscure data, or random things. We need to make sure that we're focusing on the amount of data and the meaningful data within that. Sometimes the old saying is, quality over quantity. It's not necessarily always how much, it's how good it is. As said earlier, data from multiple sources. When data's coming in from all over from different things, again, looking at the financial industry, we're talking about checking account histories, we're talking about loan documents. We're talking about all different personal histories coming in, all this data.
Again, we're not even talking about necessarily searching it granularly, but you have all this different types of data coming in from all different types of sources coming into your one institution. You, as a challenge to be able to capitalize on that, have to manage that.
Also we've got inaccessible data, just meaning stuff we can't get to. Maybe it's encrypted, maybe the files are fractured. Maybe there's issues with it. Maybe it's sitting somewhere on a data silo in your institution that's just a pain to deal with.
And finally, one of the bigger challenges is going to be poor quality data, incomplete data. Again, it would be almost like having a phone number and missing the area code. That is a big issue when we get, or false data for that matter as well too.
So now that we've looked at the challenges, we've got to talk about how to tackle those challenges. And now we're looking into the nitty gritty of it. And really what we're talking about doing is this consolidation, conversion, or migration of this data that we're talking about.
Again, specifically speaking to the financial industry, you're talking about data coming in from multiple sources. Maybe it's mobile banking, maybe it's your LOS, maybe it's bill pay, whatever it may be. Constant streams of data coming in from different points and it's different types of data, and whatnot.
And it may be housing in different locations. Maybe you have everything sitting in one central repository and go into that. Maybe you've gone through an acquisition before, and you have an old legacy archive system for your check images or your statements, et cetera, et cetera. Maybe you've gone through a core conversion and you've got some of your legacy databases sitting there that were never converted over.
All these are not uncommon things. And these data lakes and these data silos are created. So then, we've got to talk about, well, if we want to utilize our data and capitalize on it and really turn that goldmine, in a sense, that we have actually into real numbers and real profit, we're looking at these migrations.
But if you're anything like me, what comes to mind when I think of migrations, consolidations, conversions, is I literally pull my hair out and I start thinking about things like, "Oh my gosh, it's going to be so expensive. It's going to take so much time. It's going to be a headache. There's going to be roadblocks and challenges. And I've got to get this data from A to B and I can't focus on that." Long, sleepless nights. Other different things that really just make you really want to pull your head out and really look like this guy, screaming at his phone there.
It's a challenge. And the frustration is real. I've used the example before about getting a new phone, being someone that's worked in technology for a long time, I love new gadgets, I'm a gadget guy. So getting a new phone is always exciting.
It's always fun getting the latest and greatest, but then the moment comes when I'm getting a new phone or getting a new device, and I remember I got to cut everything over from my old one. Did, I back it up? Did I get all my pictures moved over, did I get all my contacts moved over? Oh my gosh, I've got to redo my passwords on everything out there now.
All these different things to start moving this data from one device to a new one. And again, I'm talking about a cell phone here and just consolidating that data. Now, with today's technology it's made it a lot easier. I can take my entire phone and I can back it up, whether you're an Apple person or an Android, or whatever that may be for you, you can back it up into the cloud. And that means pictures, contacts, et cetera. You can even use things like a password app or whatnot to consolidate everything in it.
But again, look at the general theme of what we're doing. Instead of the old ways of having to convert stuff over and manually move it over and make sure we got our contacts, now we've created a way to actually do away with the data silos, move everything into the cloud, or whatever it is, and then put it on your new device. And I'm just talking about phones here.
Now, in our industry, in our world, we're talking about a lot more data and a lot more challenges and things that are not necessarily as smooth and as cut and dry in there. And we really handle these in about three different methods, really, the usual suspects. As an industry, we can do nothing, which is a top choice a lot of times here.
Data lakes, here's the bottom line with it. You have this data, it's sitting somewhere in some type of a data lake. To be able to be utilized you need to get everything together, but that is an arduous, time-consuming, expensive task that may not be a high priority right now, or may not be something that you can leverage your team to go do.
So what's left to happen is it's left to sit and do nothing. Maybe you'll get around to it. Maybe next year, maybe six months down the road, maybe we'll have a project for it. In the meantime, we'll stick it over there on the shelf, for lack of a better example.
Secondly, we can to rely on internal teams, happens all the time. Let's go get a couple of guys from IT and let's try to start migrating this stuff over. Let's move things through and go that different route. Let's leverage the people we have to try to bring our data into one location. We'll talk through some of the challenges of these shortly.
And finally, we can outsource to somebody else to do this migration of sorts. And a lot of times that goes to an analytics provider, somebody that's going to be doing the actual analytics itself, they can move that over.
However, there's individual challenges that come up with all three of these. And I'd like to talk to you briefly about it, and then talk to you about just a different way to really look at that and capitalize on your data.
Number one, the challenge I really want to look at is really expense, it's the cost, it's the monetary issue with it. The loss of doing nothing is too great to chance. We talk about that. So when we talk about that first usual method of letting it sit, we also talked about, on average, an 18% increase in revenue by utilizing the data an institution is sitting on. So that's 18% revenue increase that could go out the window by just doing nothing and let it sit there. And we need to take that into account when we're making the decision on how we're going to manage that.
Now, your team internally and leveraging them, is very obviously doable, and a lot of people have large enough teams to be able to do that, large enough personnel to be able to actually bring up a project and set a team up to do that. Unfortunately, some of us do not have as large of a team to be able to leverage, and it's a couple of people in IT that make and do these projects. Either way, there's a couple of things that really come up in there from a cost standpoint.
Again, as soon as we create our internal teams to doing it, we're pulling them off of different production things as well, too. So they may be now moving data and migrating data where they could be doing development and writing tools for you to actually increase revenue, bring in new customers, things along that lines.
And if we're leveraging people who are not really specialized within doing this, then we're also actually raising up our human error issue, which can, again, cost us more money to fix things that are done incorrectly in the beginning. And also, analytics companies are fantastic at analytics. I'm a big believer in, do what you do and do well.
With our particular industry, we focus on data management. That's what we do. That's all we do. We stay right at that. We always like to use the saying, "What's your one thing?", and find that one thing and do that with excellence. Analytics companies are fantastic at that and doing analytics, but not always versed in the migration process and making sure that's done to get everything, not just moved, but moved correctly, properly, and in the format that it needs to be to actually be utilized.
And again, for challenge number two, and while we're really on the page of cost, I want to look at cost a different way, looking at in time. I think it was Ben Franklin that originally said, "Time is money." And this particular slot I even wrote, "So is data, so let's not waste it." I love Jim Rohn's quote below, "Time is more valuable than money. You can always get more money, but you can never get more time."
Doing nothing creates a burden on your personnel to locate things. It costs time. Sometimes leaving things alone costs us more in time than doing something with it because now, A, we're not utilizing any data if we just do nothing and let it sit there, and B, if we decide to go and ever do something with it, by doing nothing, we're accumulating piles and piles, to give a visual to it, of this data, or in really the terminology we're accumulating data lakes, data silos, different locations where it may be.
We're not going to keep touching it, we're just going to keep stacking it up somewhere. So by doing so it's basically creating more work on the back end of it, which is going to take more time. And we, again, like we're saying, time is money. Internal teams, we can definitely sometimes cut the costs by leveraging those, but what are we saving in cost versus time?
If I leverage our internal teams and having them do that, it could also pull back from them doing something in production, like we talked about before. But if this is a task that could be done by an expert in a month, or four or five weeks, six weeks, whatever that looks like, but it takes your team five to six months, or maybe it's a year project, because they're having to be pulled off and do other things like that. What are we gaining by saving the little bit of money, but yet losing so much time in the process of doing that?
And finally, again, even an outsourced company that's focused on migrations can slow the process. We've actually seen some companies out there, even the core providers in the financial industries when it comes to the migration process, you'll have a backlog of anywhere from six months to 18 months, and can take up to that time to get the conversion or the migration process done.
Again, it just takes an exorbitant amount of time in which during that time, you, as the financial institution are carrying all the burden of that time being wasted. You're having to still continue doing things the same way, still use the spirit system, still look for data, still try to utilize it. And you still can't fully capitalize on the data that you have because it's not there.
And lastly, I want to talk about is risk, your risk. I think to me, those are the big three: cost, time, and risk. And there's a lot of risk going on here a lot of different ways. The first one I want to look at is that risk of potential revenue. We are not in business for benevolent reasons. We are all in business to make money. We want to grow. We want to grow as a financial institution. We want to grow no matter what we're doing. And the thought of leaving money on the table in any business application is not something we want to do. It's not smart business.
So one of the risks we have is by not capitalizing on what's at our fingertips. There has been so many stories, and I could tell this forever, of how institutions have leveraged data to actually grow in revenue.
One of my favorite stories was with Bank of America. This was years ago, I can't remember the exact date, but they actually started putting pictures on credit cards. It was a security thing. That way, if somebody stole your card, or at least it was pitched as a security thing, that way, if somebody stole your card and your picture was on there, obviously the pictures didn't match. You knew what was going on.
Reality is they capitalized tremendously on facial recognition software and on data analytics. By using the data that they had accumulated for all of their clients for so long, they had this readily available at their fingertips, so with facial recognition software, when somebody would walk into a branch, or walk into any one of their locations, they would immediately be able to recognize who the person was with the software because they had a picture of them now, bring that information to their tellers, or whoever they were meeting with, and suggest what type of product fit them best based on the data that they had, whether it be a credit card offer, whether it be a loan offer, what it was, and they generated millions of dollars in revenue off of doing this.
Again, there's so much revenue at your fingertips, just in the data that you have. Letting it sit doing nothing is risking losing it. You also risk data mismanagement. Again, it's that whole, let people do what they do well and focus on that. I am a big believer in it. I am one of those.
I've had multiple surgeries for shoulders and knees. I'm a joint disaster in a lot of ways of what's going on in my life. And we're in the Birmingham, Alabama area, there are a lot of great orthopedic groups all around the area, but there's one in particular that has worked on famous athletes that has worked on presidents. They have operated on all of them. This is Dr. James Andrews. He's the team doctor for multiple colleges. Again, like I said, he's worked on all kinds of famous athletes and done their surgeries.
And in my mind, now I am Bobby Childs. I am nowhere near that level of an athlete of anything like that, but my shoulder hurts, so I want the best of the best. I want the guy that's going to fix my shoulder to be the same guy that's going to fix the famous athlete's shoulder, that's also going to be the same guy that can work on a president.
I don't want to just to leave it to anybody hands to get it done. I want the best. It's the same thing with our data. If we're relying necessarily on internal teams and that's not their specialty, and that's not their background, ad getting this data migrated over and matched properly and correctly, we run a high risk of data mismanagement.
Even if we're relying on teams externally, somebody coming in a company that focuses on analytics, but not necessarily the moving of data, we run the risk of a breach. Maybe we have to send that data out, which is what a lot of shops require. You actually have to package it, put it on a drive, send it to them for them to actually do the type migration. What if there's a data loss in the process?
All of it is too high of a risk. I want somebody that's focused on what they do and the best to work on me personally. And I would want nothing less with my financial institution and the data that I have to sit on.
So with the challenges being addressed, what is a way to tackle those challenges? What is the way to do something different, a better way, a new way of looking at it? I put this quote up on the right hand side of the screen, and it's one of my favorite quotes. And I've heard, it said a lot of ways. It was a rear admiral in the Navy that originally said it. And that is, "The most dangerous phrase in the English language is, 'We've always done it that way.'"
I've also heard it said, "The seven most dangerous words for a business is, 'We have always done it this way.'" It's what sometimes we go to because it's comfortable, but it also gets us stuck in a rut. It keeps us from seeing innovation and capitalizing on it. We get stuck with tunnel vision, and sometimes we can get left behind in the industry, or we can rely on things to be done by those who are not specialists in it.
So the better way to look at data management, the better way to look at getting this data consolidated and brought together so that you can capitalize on and use it for your industry, is really just as simply as focusing on a company like us at NXTsoft, who focuses a hundred percent on data management.
We're not pushing into the different worlds, and cores, and imaging software, and things along that nature. We are a hundred percent focused on migrations and data management. Not only that, but even just specifically within the financial industry. Our team has over 25 years of experience doing that one thing, and has worked with over 3000 financial institutions to try to bring a better way of handling this, to get your data done, not only moved, and migrated, and consolidated, but done right, so that you can capitalize on it and you can reap the rewards of that 18% revenue that we talked about before.
So what are the results? Lower the cost, Quicker the return. With a specialized team, you can actually get your data migrated fast while mitigating your cost, and also mitigating your risk with it. Your extreme time savings. Most of the time, our team can give access to data within 48 hours of the implementation process of some of our procedures.
This cuts your time way down. Again, time is money, like we said earlier from the Jim Rohn quote, "You can always make more money, you cannot always make more time."
Thirdly, and these are probably all in different ranking order for every organization out there, but you can also cut down on risk. Nothing will ever leave your secure site, a hundred percent focused on your financial institution and the security for that, which is crucial.
And I think another one that I put in there is now the burden's not on you. The burden's not on your team. The burden falls on a team that is a hundred percent focused on doing just this. Taking those data silos, taking those data lakes and bringing them together for you to reap the benefits out of it.
With that said, again, my name is Bobby Childs. I thank you so much for your time today. I thank you for allowing me to walk through this webinar to explain some of the benefits of consolidating your data, some of the values of just data in and of itself. If you have any questions, we have an email over there on the side email@example.com. Please feel free to reach out. There's also our number as well. And again, I would like to thank you for your time and hope you all have a great day and a great rest of the week.