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Hi. I'm Barbara Call, Senior Director of Content Operations and Strategy at IDG. And I'll be your host and moderator
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for this on-demand webcast. Today, we'll be talking about Data-Driven Banking in the Digital Era. According to IDG, global banking spend on big data and analytics will approach $26 billion in 2019,
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and is growing at a pace of 13% year over year. It's clear that banks are prioritizing data and analytics above many other technology initiatives. Given the need to converge data within the institution and begin using data from external sources,
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data storage is becoming an increasingly strategic resource to achieve digital transformation. Joining me today to explore this topic is Jerry Silva, Research Director of Global Banking at IDC Financial Insights.
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Welcome, Jerry. Thanks, Barbara. Thanks for having me. Also joining me today is Mihaela Risca, Financial Services Content Marketing Director
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for Pure Storage. Welcome, Mihaela. Thank you, Barbara. Happy to be here. So now I'm going to hand things over to Jerry
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to run through his portion of the presentation. Jerry, take it away. Thank you again, Barbara. So let's talk about the importance of data, particularly within the banking industry.
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But in order to do that, we need to set some context here, some background which is really important to understand to get into the minds of the banking executives who are facing some significant challenges these days. And I need to take you back to the economic crisis of 2008,
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2009 during the meltdown, and some of the things that happened immediately after that. Globally, consumer trust and loyalty in financial services dropped precipitously, down about 50% globally. And it varied from country to country.
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At the same time that that happened, there were a number of other factors that created kind of a perfect storm that was not good for banks. One was the availability of much better mobile devices,
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the smartphones that got placed into the consumers' hands. For the first time, they had a lot of power, a lot of connection to the internet. At the same time, the telecom networks were offering more and more bandwidth for rates.
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And what this allowed fintech competitors to do was to start offering financial services in terms of point solutions-- so not the entire financial spectrum, but certainly in terms of things like payments and lending. It allowed them to come into the market
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and start grabbing some of the attention from the consumers. As if that wasn't bad enough, banks all of a sudden started feeling the pressure from more onerous regulations as well to see that this never happened again. So this perfect storm of consumers
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having more power in their hands, fintech competitors coming into the space and trying to steal some of that market away, as well as the need to fund more and more compliance from regulations really posed a huge challenge to the banks in 2010 to 2013 or so.
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So what banks are focused on today is how do I regain that customer loyalty? How do I bring them back? How do I retain them? As well as, how do I compete in this brand new world
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where there are non-traditional financial institutions coming up threatening that market? And how do I do all of this, by the way, when I'm having to spend more and more money on regulatory compliance?
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And so if you look at a couple of things that they use to respond with, we break it up in terms of a couple of different areas. In the front office, that is the part of the bank that deals directly with the customers,
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whether it's a retail consumer, a small business, or corporate entity, they really wanted to improve the customer experience. How do I make that look better? How do I get decisions faster in order
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to compete with folks that are doing lending, for example? How do I use AI, some of these new technologies, to engage the customers better? At the same time, the banks were looking at how to develop products.
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Are there any products that are being offered outside the banks that I need to keep up with? Are there any new products that I can develop that'll engage customers better as well? In the middle office, there was a focus on and still is
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a focus on making the processes better, making them faster, making them more efficient. Given the crunch on the budget because of regulatory compliance, it's very important to try to look for [AUDIO OUT] anywhere we can find them.
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And then finally in the back office, in the infrastructure, after the banks had started working on things like mobile banking and better online banking, they came across this challenge of-- the back office, the banking core systems, just can't keep up.
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They're not flexible enough. They're not agile enough to give us what we need to go into the future. And so we saw a lot of work around the infrastructure, replacing the core banking systems, transforming them.
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Data was a big part of that. How do I start managing the data better? I think it's just generally known throughout the industry that banks have a lot of great data, lot of valuable information that they're just not leveraging.
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And here on this slide, you can see the relative progress. So that front office, because they can be quick in terms of buying solutions that address mobile banking or online banking or bringing in something else that a vendor offers, that was done pretty quick.
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On the products and infrastructure side, they're still kind of struggling a bit, but finding their way. And then processes seems to be lagging behind most of those areas. Now, the reason they're doing all of this
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is something that we kind of encompass and we call digital transformation. And by the way, when you're looking at digital transformation, for IDC, this is not about technology.
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This is about the continuous process that institutions are going to have to start using to make their enterprises as agile and as responsive as possible, if not, to just plain disrupt their own industry, their own business value.
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And so that, to us, is digital transformation. However, it is supported by some very key technologies. For banking particularly, there are three fundamental technologies that we're talking about here. One is cloud, one is big data and analytics.
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Social doesn't play as much of a role these days in financial services, but certainly mobility does. And then surrounding those three fundamental technologies are what we call innovation accelerators.
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And again, in the case of financial services, the ones that are really important to look at our next gen security the internet of things-- certainly for insurance if not banking-- and cognitive.
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Those are the three biggest innovation accelerators that will help the adoption of the fundamental technologies of cloud, big data and analytics, and mobility. When IDC looked at the banks in a global survey, we found that most of the banks that we
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talked to have started strategies around digital transformation. A lot of them have gotten to the level that we call digital player, where they're actually starting to create technologies and processes and experiences
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that align with digitally transformed banks. Just a fewer of them are what we call digital transformers, which means these are the people that are actually starting to create innovative solutions around the digital transformation paradigms.
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And then finally, just a few organizations around the world are what we would call digital disruptors. These are the banks that are actually changing the way the industry looks at products, looks at the customer experience,
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looks at efficiencies. And so you know, it's a typical bell curve. And one of the goals-- clearly as an institution, when you look at this-- is to see, well, where am I on this chart?
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And where do I want to be? And how long will it take to get there? And what kind of things do I need to do to get to that digital disruptor phase? When we asked those same banks about why would you
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want to undertake a digital transformation in your institution, the top answer was to improve customer experience. Again, remember back to the factors in 2008 that caused a lot of that distrust and disloyalty
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from the consumer base, and you'll see why that's important. And then again, heading into this new world where you've got non-traditional competitors in the industry, you want to try to retain that competitive advantage as well
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in what you're doing. And then kind of interestingly, this new partnerships with suppliers-- we're starting to see this now in terms of open banking. How do I create new products and services using
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either the fintechs, what I said are competitors, or other partners to start bringing in different sources of value? In fact, when we look across all of banking, we've actually come up with a list of, at this point,
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it's about 50 different use cases. So the banks that we talked to said, we understand the need for digital transformation. We understand why it's a good thing. What should we actually be doing?
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Where should we spend our money in terms of discretely funded projects to drive business value in the name of digital transformation? So the IDC, we've come up-- and we're in the process of reviewing this today, even--
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of 40 or 50 use cases. These are actual discreetly funded projects within each of these areas, whether it's customer experience, payments. We're adding one around lending currently, corporate banking,
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digital trust and stewardship, efficiency and agility, which is really the back office, or the use cases surrounding how do I partner with the external ecosystem? And so there are a number of things that banks are looking at.
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When you look at this thing, almost every use case you see here has data attached to it, has data involved with making those use cases real. And in fact, when we go out and ask them about their priorities with regard
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to spending, with regard to strategy, it's clear to see here that business analytics-- the use of data, or I should say, the better use of data in terms of achieving digital transformation-- is the top-line highest priority
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that these institutions have, along with digital transformation, which is kind of the all-encompassing thing. And then things like internet of things, big data technology-- this is incredibly important as well.
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And we're going to talk a little more about that in a minute. And then you can see the rest of the priorities around cloud services, mobile applications, next gen security. But it's really striking how important that business
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analytics line is. When we look at analytics and data in particular, we would ask the questions, then, to what end do you want to use these? To what end are you making the investment
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in big data and analytics? And these are the top ways they're telling us. That top one-- improving security-- is not a surprise, either. Security's a huge concern for institutions
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everywhere, particularly given the news over the last five years with regard to data breach and fraud. In fact, in the recent conversations I've had with institutions around artificial intelligence and machine learning, the biggest use
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case for [AUDIO OUT] happens to be fraud detection. How do I start making better decisions about is this a fraudulent transaction or not? I don't want to turn away a good customer just because my analytics was telling me
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that it's a fraudulent transaction, where it may not, in fact, be-- so the false positive, if you were. But there are lots of uses. Immediately after that improvement of security, you see a lot of use cases that are just as important--
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standardizing product lines, avoiding risk, supporting IoT, understanding patterns and large data sets, becoming more efficient in the processes that I'm using. So there's a number of activities that are absolutely based on the ability to have data
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and to be able to use data. The other thing I'll point out in this chart, by the way, is again, it's not just the use of data, the need for data. But most of these, if not all of these, require characteristics around real-time data,
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aggregated data. How quickly can I turn around a decision? How quickly can I determine that this transaction is being held? For example, I travel all the time. If I'm outside the country, I want my bank to be able to say,
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well, we know that he travels a lot. He's in Dubai right now. Let's not turn down this transaction. And that needs to happen in a fraction of a second while I'm standing at the retailer
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or while I'm checking out of the hotel or something like that. So all of these use cases for data and analytics have those characteristics around aggregation, around real-time access. And as we see further on, it'll need
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to have the characteristic of location-independence as well. So when we talk about all of these needs and driving the needs from within the institution, we really talk about three different areas. The line of business-- their needs
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are a little different than the rest of the organization. Their needs are all around understanding the customer. They want to get to that market of one. And so for them, the use cases around things like key lifestyle indicators, where in my life am I,
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what kind of needs will I have going forward, as well as giving that same kind of information to the staff-- it's amazing. Sometimes we find the customers are getting better information than the people at the branch or the contact center.
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So those are the things that the line of business is trying to address. At the operations level, these are the folks that are responsible for processes. And so they want to use AI/ML to support the processes lending
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origination, account opening for a deposit, a credit decision on a credit card. And so they're trying to make those processes as efficient and as quick as possible. And they are the use cases on things
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like mortgage origination, where it costs the bank close to $8,000 these days to originate a mortgage. A lot of that has to do with the human intervention required to process an application.
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And again, I mentioned the need for real time. How can I make real-time credit risk decisions based on the information I have already on the customer? And then finally in the IT department, it's about agile development, building the infrastructure,
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managing and governing it. These are the folks that want to start making product development faster. I can't take a week to develop a brand new development environment and start building a new product.
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I want to move to agile, where I want to have multiple copies of a production environment available for my developers to develop on, and then take care of the testing and the staging and implementation of those product sets
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on much more of a daily basis or hourly basis. And so there, the use cases are around agile development environments. Evidence-based fraud if you're talking about risk management or security-- and so each of these different states
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within the institutions has kind of a different need for data. Now, there are challenges. There are always going to be challenges to this kind of work. And one of them is fundamentally really difficult
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to get around-- the organization being too siloed. This is a challenge that's been happening in financial services for a long, long time, decades. But we see banks starting to overcome this challenge, mostly because at the base of digital transformation,
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it was a need for commitment at the CEO and board level. And we do see that happening a lot with particularly those banks that we found to be in that disruptor phase. They're getting it done because they have commitment
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from the very top. So hopefully, that will become less and less of a challenge over time. That kind of ties into the second one as well-- no digital transformation strategy.
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We see banks getting over this as well. The revenue models are still difficult, trying to tie those together with digital transformation as well. A lack of understanding of what transformation is--
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and these are the areas where we're helping the banks as well, understanding what digital transformation is all about, and how do you tie those back to the technologies that we need? So these are challenges. But we do see banks overcoming these challenges
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on a day-to-day basis. The reason it's important, by the way, to overcome these challenges and to actually start thinking about digital transmission-- we talked about customer experience
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and how do you improve that. But the next stage is something that we call connected banking. And I'm sure a lot of you have heard about open banking, what that means. For IDC, we've kind of redefined open banking,
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where open banking was really driven by regulation, particularly overseas, where we don't have that in North America. And we have it to some extent in Asia-Pacific, but not as much as in Europe.
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For us, connected banking is about the bank's recognition, proactive activity that become involved in the ecosystem of the customer's lifestyle. So it's not just about supplying a checking account. It's not just about supplying a mortgage.
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Can I, as an institutional, a financial institution, start partnering with real estate agents, with building inspectors, with cities and towns, and actually offer the experience of buying a home instead of just selling a mortgage?
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And so this idea of connected banking packs not only the technology and not only the infrastructure of the bank, but the data. One of the use cases I love to talk about is when the bank is making a decision on something
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like a mortgage or some sort of lending product, wouldn't it be great if they could go out and gather information from sites like LinkedIn? Look at the employment history of a person who's applying for a loan.
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And then it's part of your credit decision on the data that you can bring in. Now, lots of value in that kind of a data, fundamentally different than the structured data that banks are used to working with day in and day out.
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It's not the transactional data anymore. This is unstructured data that resides somewhere else. Now here, clearly I'm talking about outside of concerns around privacy. Maybe it's an opt-in situation.
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But just the technical ability and the appetite to be able to go out and get unstructured data from a social network or structured data from one of the partners, whether it's a real estate agency or a town or a municipal district that
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has home information, title information, that kind of thing-- but the ability to go out and grab this data, manage it, and use it in a real-time fashion to help your customers with their businesses
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will be fundamentally important. Now, the technology needed to underpin all of this looks inherently different than current technology at the institution. In today's world, what you would see here
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is essentially one or two big black boxes that are all based on COBOL programming that was developed in the '60s and '70s. In the future architecture, everything in the institution has to be open with an eye on two different things.
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One is how do you make product development or application development faster, cheaper, and better by using open APIs? But secondly, and just as important, is the acknowledgment that data, the data you need to be able to do this, to be able to connect
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to external ecosystems for the benefit of your customers, that data could be either on premise, aggregated or not-- and most often than not, it's not aggregated. There are hundreds of data silos today within the institution. But then, how do you start managing external sources
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of data as well, whether that's your own data on a public cloud, partners' data on a public cloud as well or maybe their private cloud? Data becomes the-- somebody once said data is the new oil. In this picture, data has become basically the new oil
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as a lubricant to make sure everything is flowing fast. But again, that data has to have those inherent qualities of real-time and location-independence. And so finally, the call to action when we talk to institutions, talk
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about these kinds of things at the organizational level in order to become digitally transformed and overcome the challenges that were brought up during the economic crisis in 2008, as an organization, you have to commit to transformation.
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This is not a technology initiative. This is absolutely a change in the way that banks are doing business. In terms of the line of business executives, we say, look, that you need to drive speed, agility
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with an eye toward connecting the bank to the customers' lifestyles. Again, whether that's a retail consumer or a small business or a corporate entity, you really have to be in line with what they need
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and not just offer financial products. Within operations, we're helping banks, again, with improving their agility, increasing their efficiency, and at the same time, trying to improve or maintain security boundaries as well, as well as we have compliance.
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And then finally, IT is driving [AUDIO OUT].. You can't do any of this without IT. And so for them, it's about creating the agility and making sure everything stays secure, being as efficient as possible, and again,
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acknowledging the fact that that future architecture is virtual in terms of its location. This is no longer strictly on premise. This is data and process that lives everywhere, and you somehow have to manage it and find the best solutions
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to let you do that. So with that, I thank you. And I'll turn it back over to you, Barbara. Thanks, Jerry. That was great.
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So now, I'm going to move over to Mihaela. She's going to run through the second portion of our presentation. So Mihaela, you're on. Thank you, Barbara.
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And just to continue the flow of the ideas from Jerry, he talked a lot about the importance of data. And the data is not getting smaller, right? We have now zettabytes of data flowing from sensors, applications in real time.
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More data is now created by algorithms. And 80% of this data is unstructured. In enterprises, about half of revenue is now driven by digital processes and services. Exponential data growth requires new approaches
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in how we manage and store the data. And that's what I'm going to talk about today. Competitive advantage comes from sharing, processing, and delivering real-time intelligence to create value. So how can we help turning data into a digital banking asset?
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As you can see, this is a data-centric approach. And to make the parallel with one of the images that Jerry showed, the customer is now in the middle of a banking architecture, of the new bank architecture.
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So data overlaid with new technologies like artificial intelligence, machine learning, is the new way of making money in today's banking industry. Data has to be fast, has to be shared, has to be automated. You cannot wait for an ETL process to happen anymore.
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Data pipes have to be dynamic to feed these new AI-powered applications. And also, it has to be globally available, reliable, secured regardless of location. And that's a very important point
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because you need consistency of data governance, data security, policies across on prem, hosted, private cloud, or public cloud. So we're going to talk a little bit about how Pure can help you achieve these capabilities to enable your digital transformation.
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In one sentence, we help you build a solid data infrastructure by consolidating, connecting, and accelerating your data. What do we mean by that? Consolidating means eliminating the silos that Jerry mentioned.
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Because of the product-centric approach of the past in a bank, you'd have all these departmental silos, product-based or division-based. If you put your data in one place, you actually get the benefits of one source of truth.
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You get the benefits of less duplication of data. You get the benefits of a better view of your risk. And also, you can put all your data to work-- no more cold data or data trapped on tape. You also have with Pure, you can achieve
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better total cost of ownership. And that's operating budget. You free up the operating budget for innovation instead of just running the bank. By connect, we mean having all this data
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available for business analytics, for risk management. And you can correlate better for different processes, for example, for evidence-based fraud detection and even prevention when you add machine learning and artificial intelligence on top of that.
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Better data security-- if you consolidate all your logs from different applications, from different devices in one place, you can detect potential vulnerabilities faster. And you can act on them.
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You can also enable automation. It's the dynamic database that I talked about. By accelerate, we mean a lot of things. So basically, faster applications, faster devops-- and I'm going to give you examples on how we can achieve
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that with our technology. Real-time analytics-- basically faster time to market. Real-time customer insights, and then faster backups and recovery-- recovery is very important. More efficient service delivery for a bank--
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in a word, faster innovation and operational excellence. Speaking of examples, I picked just a few of them because I know we have time constraints. But I can tell you, for example, that global top 10 banks can manage more than 30 petabytes of storage
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with one storage admin. I think this is an achievement. If you work in IT, you know what I'm talking about. Another example is a foreign investment company-- this is a public reference, Man AHL--
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was able to accelerate 10 times to 20 times their Spark workload that are used by their quants for investment models. That translates directly into better intelligence, better investment decisions, more revenue. And we mentioned developers.
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So Jerry said that you cannot wait for a development environment to take hours or days to be brought up. And you cannot work with old data. If you want to-- if you use our features, one is called rapid recovery,
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you can actually build-- in minutes-- a new development environment. So you can also enable self-service so the developer doesn't have to wait for a storage admin to do that, because our interface is very intuitive.
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I also included a quote from AWS. "Pure Storage is delivering a unified hybrid cloud experience providing consistence, APIs, and automation for developers, and offering uniquely differentiated backup and data protection on an AWI.
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I think that's important for the capability of moving data or moving workloads without having to change the application, regardless of where the data is-- location-independence, like Jerry mentioned.
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I just want to give you a sense of the innovation that's built into our platforms, software, and also cloud capability. And this is a timeline from 2012. I'm not going to go over all of them.
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But I want to pick just a few. Basically my point here is that storage is an important enabler of your digital transformation. As Jerry said, you have to have agility. You have to have security.
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You have to have performance, speed, and location-independence, or flexible consumption, is how we call it. All these are layered here in our timeline. So let's take a look first at something not a lot of people
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are aware of-- predictive maintenance. So Pure actually has instrumentation in each array. And those sensors can send data back to our tech support. And we have an AI engine called Pure Meta that
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can ingest about 1 trillion data points per day from all the arrays for customers that subscribe to this service. And we can generate warnings for potential vulnerabilities before they occur.
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We saved a lot of time and money for our customers with Pure1 Support and the Pure1 Meta. I encourage you to look it up on our website. This is a very important innovation. Pure Service Orchestrator is also part of our software.
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This is an on-demand provisioning storage for stateful containers based on policy. Because a lot of banks are using right now containerised applications, this is important, because storage can be consumed on demand.
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And this has high availability. It's self-healing. It's one layer for any type of array from Pure Storage. Another innovation is the Evergreen Storage Program. This was the first in the industry back in 2012, '14.
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A lot of our competitors have tried to maybe copy that. But we've evolved it over time. And the newest addition to this is ES2. We call that Evergreen Storage Service On Demand. So with ES2, customers only pay for used sector
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of capacity measured daily, not provisioned capacity. And the result is up to two times a rate of cost efficiency compared to provisioned approaches. So this is for a no balance sheet impact
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under new accounting guidelines. And finally, AI-- Pure Storage has partnered with NVIDIA and Cisco to create a technology stack that gives you a turnkey way of jump-starting an AI project.
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For example, a North American bank data science team is able to run four to five times more projects than if they used self-built solutions. This is called AIRI-- AI-Ready Infrastructure.
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Now we talked about cloud, right-- location-independence. Many banks have a cloud first initiative. For new applications, this makes a lot of sense, especially if they are cloud native. For software development, the agility of the public cloud
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makes sense. But what if the application that needs to be moved to the private cloud because it needs access to sensitive or regulated data? Do you need to recode the application?
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The reality is that a lot of critical data is still residing in departmental silos, like I said before. A lot of old customer records have been saved to tape and are not available for analytics.
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The future of cloud is a hybrid cloud, a multi-cloud where applications, services, data, and users can reside anywhere and still be connected. Pure Storage hybrid cloud solutions enable automation, application mobility--
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that's why we talked about containerised applications-- seamless management, and data protection across on prem, hosted, and public cloud resources. With Pure software running natively atop of AWS storage, you get the same data services, reliability,
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and APIs as on your private and public cloud environments. This enables you to develop applications once and then run them in a different cloud with ease. Some of the technologies that you see here are Pure Storage Cloud Block Store and CloudSnap.
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They run atop of AWS, EBS, and S3. And they simplify the cloud migration and enable bidirectional data and application mobility. The cloud can also be used for backup and recovery processes, reducing both costs and system restoration time.
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A new product that you may not know about yet is Pure Storage Object Engine, which actually reduces the time it takes to move data between a private cloud and a public cloud. I gave you just a few examples of how
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Pure Storage can help a bank to turn their data into a digital asset. You can find out a lot more by visiting our website. Please see the link in the resources widget. We have a Financial Services Industry page
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with a lot of content. And also, I encourage you to read the new IDC Industry Spotlight for Retail Banking. And you'll see the link in the widget as well. Also, please follow us on Twitter
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@purestorage and @jerrysilva_pgs. Thank you very much. Barbara, back to you. Thanks, Mihaela. That was great.
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So Jerry, I do have a question for you in closing. In what areas of the bank are you seeing the biggest interest in the use of data? Yes, so we covered that a bit. If I had to pick a couple, one would be certainly
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in terms of the customer. How do you improve the customer experience? And there are a number of ways banks are using data or trying to use data to do that. One is certainly understanding the customer better.
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As Mihaela, during her part of the presentation as well, data is so siloed right now within the institution that the head of deposits-- for example, the people that manage your checking account-- don't always know about any kind of lending history you have.
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If I have-- for example, I have a small business account with my local bank. And there's no connection between that relationship and the relationship with me as a consumer, as a retail personal checking kind of person.
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So that's certainly one area where they're really interested. And I mentioned credit decisions as well. Having as much information, both information you've got in-house as well as bringing in external information,
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in order to approve maybe a loan while I'm at Best Buy wanting to buy a laptop-- so that, all those uses of data for customer experience are incredibly important these days. And then maybe the second one is where we see,
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again, the biggest attraction these days, is how do we use data? Again, data that resides within our walls as well as outside data-- how do I use that data to make better security and risk
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decisions? Do I turn somebody down? Is this a fraudulent transaction? So using data for those kinds of purposes-- and the back office is just as important.
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It also adds to the customer experience because then you've shown that you really care about the relationship and you're trying to protect the customer. And those have the same characteristics of you
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don't know where the data necessarily is. It could be coming from inside. It could be coming from outside. And it has that same characteristic of we need this real time.
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We need to be as quick as possible in the analytics of the data, which then drives where do you store the data? How do you manage the data? So I would pick those two areas in terms of the biggest
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areas of interest. OK, great. Thank you. So thank you to my guests, Jerry Silva, Research Director of Global Banking at IDC Financial
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Insights, and Mihaela Risca, Financial Services Content Marketing Director for Pure Storage. For additional information on this topic, please click on the green resources icon on your screen. And thanks for joining us.
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For IDG, IDC, and Pure Storage, I'm Barbara Call.
