Listen: How FIs can address data management challenges

Proper data management is something all financial institutions strive for , but it is not easily achieved due to data siloes and drift. While the process of organizing and gathering the right data can set banks apart from their competitors , using the right tools to accomplish this is critical, and something all FIs need to address, Jocelyn Houle , senior director of data governance at Securiti.Ai tells Bank Automation News in this episode of “The Buzz” podcast. “Most banks and financial institutions are looking for some sort of AI or machine learning that can probabilistically guess what ’ ' s in that data, in real-time streaming,” she said. “The needs and obligations are rising, but also the multiplicity of data stores and types of data are so great now that you must use tools of some kind to investigate and identify what data is sensitive across all of these stores.” Drawing on her experience as Operating Partner for Capital One Ventures , Houle says that to govern and manage data properly, dbanks need to ensure that the information is not siloed and does not cause data drift for financial institutions. Listen as Houle discusses which tools can organize the data and prevent data drift and why the unification of data controls is so important to banks on this episode of “The Buzz.” Subscribe to The Buzz Podcast on iTunes , Spotify , Google podcasts , or download the episode . The following is a transcript generated by AI technology that has been lightly edited but still contains errors. Brian Stone 0:02 Hello and welcome to The Buzz, a Bank Automation News podcast. My name is Brian Stone, and I'm the Associate Editor at Bank Automation News. Joining me today is Jocelyn Houle, Senior Director of Data Governance at Securiti.Ai. Jocelyn discusses how elimination of silos can help with data organization at banks, what tools and solutions banks are utilizing to organize data, and what data points banks are looking for, to collect and organize for business cases and uses.Jocelyn Houle 0:32 So you've got your baseline data that banks need to collect and organize in terms of sensitive data, which is usually PSs, PCI, or PCI. So that's your baseline. But then they also are looking at business metadata that they want to discuss the olden days, you might have someone who like me, they would say, oh, Jocelyn knows all about this dataset. But you know, companies have such a massive load of data. Now there's really no one who can hold it all in their head. And so they need some sort of artificial intelligence or some way to collect all of that baseline data that says, Yeah, I'm sensitive data. And then all that contextual business data as well needs to be tagged and connected to the baseline data sets.Brian Stone 1:14 So how can financial institutions ensure that they have proper data management?Jocelyn Houle 1:22 Yeah, so we're kind of at an interesting inflection point where a lot of banks and financials still use what has been the industry standard have declared human centered at addition of data. So a couple of things, they'll point towards a list of datasets, and they will probably put that in Excel and send it to individuals who then go through and tag it by hand. And that really worked well before the explosion of cloud data. But now there's so much data. And there's so much to know, that most banks and financial institutions are looking for some sort of artificial intelligence or machine learning that can probabilistically guess what's in that data, can give them a sense of where the dark data is, and then has a kind of overview approach of integrating everything you can find in your structured data warehouses in your on prem data in that new, you know, fancy real time streaming. And so the needs, the obligations are rising, but also the the multiplicity of data stores types of data are so great now that you must use tools of some kind, to investigate that data and identify what sensitive across all of these stores. Brian Stone 2:37 Yeah, that actually sort of leads into my next question. I'm glad you you mentioned tools, from a technology perspective, are there specific tools or solutions that banks should be looking at in order to, I guess, not only collect the data, but to accurately you know, organize it as well? Yeah, I Jocelyn Houle 2:54 have a particular sensitivity around this since I was a practitioner trying to collect and identify sensitive data in my past lives. So it's really important, I think, to remove silos, as you're going through this process, you know, there's going to be data always requires a ton of thought work and rework and you know, multiple points of contact. But big organizations like banks really have to focus on doing data management and governance doing protecting their data in a way that is both accurate and complete. And you can't do that with a bunch of silos. So let me just summarize the last six years of data. So everyone was like, yes, let's take our data to the cloud. Let's democratize data. And then the shoe drop. We've got to govern this data, we've got to protect it. And we really aren't clear on what's inside it. And what you're seeing in banks right now is you'll have the retail bank group, creating their own silo of data management with their own rules for data, you'll see the auto finance people doing the same thing. And what happens there. It's not just a silo, technically, it's a silo of data policies, if you have slightly different rules for what constitutes sensitive data for what I want to do with it downstream to protect it over time, in a large organization, you have this kind of drift, and it is not uncommon for banks to not be able to answer simple questions such as how many accounts to reopen how many commercial real estate deals do we have in the pipeline? Because of this kind of data drift that comes from silos, Brian Stone 4:28 piggybacking off of that, how common is it for banks not to be able to answer this sort of questions? Jocelyn Houle 4:34 Well, close your ears, my banking friends, but I think it's pretty common. It certainly has been true in a lot of different organizations that I've worked with, not just in my like my past resume, but I partnered with a lot of banks and financials through my career. And it's very typical that even for like Core Data, you might expect to be easily summarized. It's very difficult because you have so many silos that have no unified approach. to how the policies for protection, the policies of who should access it, if you don't have that unified, things start changing in ways that you can't really see in the data. And that's why it's really important. Whatever tool, you know, as I said, you kind of can't do it by hand anymore, you've got to use a tool, whatever tool you pick has to help you fit over your whole organization in a unified way that avoids these kinds of silos. Otherwise, the answers could be wrong. Brian Stone 5:25 So that that sort of rolls actually into my next question as well. How important is this unification of data controls? Jocelyn Houle 5:33 Well, as I just mentioned, you know, I, we got risk we want to avoid right, in terms of obfuscating the ability to be accurate and make sure you meet all your data obligations. The flip side of the coin is it slows down innovation and it can slow down your ability to grab revenue, which you know, all businesses but especially banks and finance really care about. Innovation has never been more important at banks, you know, we've got a huge FinTech sector coming on, we've got a lot of new thinking about how people interact with banks, it's incredibly important for them to innovate quickly, use, you know, the most advanced all this data has to feed into the most advanced tools for underwriting the most advanced tools for new cross cutting offers across all of their banking, assets, like mortgage or retail, all of that stuff is fed by having a correct usable set of data under the hood. So you can sort of think about what's really important about having the right tools and a unified approach is that it unlocks all the things that people want to do with their data to capture revenue. Brian Stone 6:38 So it's interesting that you mentioned fintechs, I wanted to get your, your thoughts on this. So we're now some time removed from, you know, everything that happened with Silicon Valley Bank and Signature Bank and banks like that, how important for smaller, I guess, community, regional, you know, financial institutions, how important is it to make sure that their data is all properly connected, collected and organized? I think it's, Jocelyn Houle 7:09 of course, I'm gonna say it's the most important thing. I think you're focusing kind of on this regional component, and people who might be audited at the drop of the hat, people who have to be ready, perhaps to be investigated for an acquisition or some way that they may want to collaborate with another bank or by another bank. In that case, you have to think about your, you know, often cyber risk is evaluated as a liability when a bank's posture or health is evaluated. Similarly, sensitive data is now really part of that investigation of what are the likely true liabilities of a company and we're even seeing more m&a firms looking at the degree to which a company or bank has managed their sensitive data, as a negotiating point in acquisitions. Brian Stone 8:03 Has the collection and organization of data? You know, we've talked about what's been done in the past? Has this always been a big focus for banks? Or is this becoming now even a bigger priority? And if if so, why? Jocelyn Houle 8:17 Yeah, it's becoming a bigger priority. It's always been a priority, right to protect personal data. I mean, that's been 20 years, right. But it was very easy in the old fashioned straight through data warehousing world, we had just structured data, straight through tools was not distributed across the cloud, you didn't even really have that many different teams using it, you might have one analytics team. And so definitely was always been important. But the degree of difficulty of meeting those obligations now is like much, much more difficult because you have so many environments, so many data types. And now as your data is even distributed to the cloud, it's even being used in complex data sharing arrangements. So it's more difficult than ever, and then I cannot emphasize enough, it can't be understated. The volume of data and how fast is changing, right, and I'll give you an example. In the mortgage business, we used to have like 60 days to work with all the data, make sure it was correct, distributed to people for analysis, and it was a long pipeline. Today, you have to do underwriting and sub second speed. And that really requires this exceptional notion of what data you can use or not used in any given moment of your business transactions. Brian Stone 9:36 You've been listening to the buzz, a bank automation news podcast, please follow us on Twitter and LinkedIn. And as a reminder, you can rate this podcast on your platform of choice, be sure to visit us at Bank automation news.com.

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Listen: How FIs can address data management challenges

Data silos create roadblocks to managing, governing bank data

SVB collapse spurs demand for new solutions

Morgan Stanley using GPT-4 as financial adviser solution

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