>> Announcer: Live from Boston Massachusetts. It's the cube. Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to the cube's coverage of the IBM Chief Data Officer Strategy Summit. I'm your host Rebecca Knight, along with my co-host Dave Vallante. We're joined by Sanjay Saxena, He is the senior vice president, enterprise data governance at Northern trust Corporation. Thanks so much for joining us Sanjay. >> Thank you. Thank you for having me. >> So, before the cameras were rolling, we were talking about how data governance is really now seen as a business imperative. Can you talk about what's driving that? >> Initially, when we started our data governance program it was very much a regulatory program, focused on regulations, such as GDPR, anti-money laundering etc. But now, as we have evolved, most of the program in my company is focused on business and business initiatives and a lot of that is actually driven by our customers, who want to clean data. We are custodians of the data. We do asset servicing, asset management, and what the customers have, are expecting, as stable stakes, is really clean data. So, more and more, I'm seeing it as a customer driven initiative. >> Clean data. can you ... >> So, many many businesses rely on data, financial services. It's all about data and technology, but when we talk about clean data, you're talking about providing data at a certain threshold. At a certain level of expectation. You are used to data quality when it comes to cars and gadgets and things like that. But, think about data and having a certain threshold that you and your customer can agree on as the right quality of data is really important. >> Well, and that's a lot of the, sort of, governance role, some of the back-office role, but then it evolved. >> Right. >> And begin to add value, particularly in the days where IBM was talking about data warehouse was king. You know master data management and single version of the truth. Data quality became a way in which folks in your role could really add business value. >> That's right. >> How has that evolved in terms of the challenge of that with all the data explosion? You know, how to do been big data it just increased the volumes of data by massive massive amounts and then lines of business started to initiate projects. What did that do for data quality, the data quality challenge? >> So the data quality challenge has grown on two dimensions. One, is the volume of data. You simply have more data to manage, more data to govern and provide an attestation or a certification, you say "Hey, it's clean data. It's good data." The other dimension is really around discoverability of that data. We have so much of data lying in data lakes and we have so many so much of meta-data about the data, that even governing that is becoming a challenge. So, I think both those dimensions are important and are making the jobs of a CDO more complex. >> And do you feel maybe not specific to you but just as an industry that, Let's take financial services, is the industry keeping pace? Because for years very few organizations, if any have tamed the data. Just a matter of keeping up. >> Has that changed or is it sort of still that treadmill? >> It's still evolving. It's still evolving in my from my perspective. Industries, again are starting to manage their models that they have to deliver to the regulators as essential, right? Now, more and more, they're looking at customer data. their saying "Look, my email IDs have to be correct. My customer addresses have to be correct." It's really important to have an effective customer relationship. Right? So, more and more, we are seeing front-office driving data quality and data quality initiatives. But have we attained a state of perfection? No. We are getting there, in terms of more optimization, more emphasis, more money and financials being put on data quality. But still it is evolving as a >> You talk a little bit about the importance of the customer relationship and this conference is really all about sharing best practices. What you've learned along the way, even from the stakes. Can you share a little bit with our viewers about what you think are sort of the pillars of a strong customer relationship, particularly with a financial services company? >> Right. So, in the industry that we are in, we do a lot of wealth management. We have institutional customers, but let's save the example of wealth management. These are wealthy, wealthy individuals, who have assets all around the world. Right? It's a high touch customer relationship kind of a game. So, we need to not only understand them, we need to understand their other relationships, their accountants, who their doctors are etc. So, in that kind of a business, not only it is about high touch and really understanding what the customer needs are. Right? And going more towards analytics and understanding what customers want, but really having correct data about them. Right? Where they live, who are their kids etc. So, it's really data and CRM, they actually come together in that kind of environment and data plays a pivotal role, when it comes to really effective CRM. >> Sanjay, last time we talked a little bit about GDPR. Can you give us an update on where you're at? I mean, like it or not, it's coming. How does it affect your organization and where are you and being ready for the, I mean GDPR has taken effect. people don't realize that, but the penalties go into effect next May. So, where are you guys at? >> So, we are progressing well on our GDPR program and we are, as we talked before this interview, we are treating GDPR as a foundation to our data governance program and that's how I would like other companies to treat GDP our program as well. Because not only what we are doing in GDPR, which is mapping out sensitive data across hundreds of applications and creating that baseline for the whole company. So that anytime a regulator comes in and wants to know where a particular person's information is, we should be able to tell them with in no uncertain terms. So we are using that to build a foundation for our data governance program. We are progressing well, in terms of all aspects of the program. The other interesting aspect, which is really important to highlight, which I didn't last time is that, there's a huge amount of synergy between GDPR and information security. Which is a much older discipline and data protection, so all companies have to protect the data anyway, right? Think about it. So, now a regulation comes along and we are, in a systematic fashion, trying to figure out where all where all our sensitive data is and whether it is controlled protected etc. It is helping our data protection program as well. So all these things, they come together very nicely from a GDPR perspective. >> I wonder, you, you remember Federal Rules of Civil Procedure. That was a big deal back in 2006, and the courts, you know maybe weren't as advanced and understanding technology as technology wasn't as advanced. What happened back then and I wonder if we could compare it to what you think will happen or is happening with GDPRs. It was impossible to solve the problem. So, people just said "Alright, we're going to fix email archiving and plug a hole." and then it became a case where, if a company could show that it had processes these procedures in place, they were covered, and that gave them defense and litigation. Do you expect the same will happen here or is the bar much much higher with GDPR. >> I believe the bar is much much higher. Because when you look at the different provisions of the regulation, right, customers consent is a big big deal, right? No longer can you use customer data for purposes other than what the customer has given you the consent for. Nor can you collect additional data, right? Historically, companies have gone out and collected not just your basic information, but may have collected other things that are relevant to them but not relevant to you or the relationship that you have with them. So it is, the laws are becoming or the regulations are becoming more restrictive, and really it's not just a matter of checking a box. It is really actually being able to prove that you have your data under control. >> Yeah so, my follow-up there is, can you use technology to prove that? Because you can't manually figure through this stuff. Are things like machine learning and so-called AI coming in to play to help with that problem. Yes, absolutely. So one aspect that we didn't talk about is that GDPR covers not just structured data but it covers unstructured data, which is huge and it's growing by tons. So, there are two tools available in the marketplace including IBM's tools which help you map the data or what we call as the lineage for the data. There are other tools that help you develop a meta-data repository to say "Hey, if it is date of birth, where does it reside in the repository, in all the depositories, in fact?" So, there are tools around meta-data management. There are tools around lineage. There are tools around unstructured data discovery, which is an add-on to the conventional tools and software that we have. So all those are things that you have in your repository that you can use to effectively implement GDPR. >> So my next follow-up on that is, does that lead to a situation where somebody in the governance role can actually, you know going back to the data quality conversation, can actually demonstrate incremental value to the business as a result of becoming expert at using that tooling? >> Absolutely, so as I mentioned earlier on in the conversation, right? You need govern data not just for your customers, for your regulators, but for your analytics. >> Right. >> Right. Now, analytics is yet another dimension effect. So you take all this information that now you're collecting for your GDPR, right? And it's the same information that somebody would need to effectively do a marketing campaign, or effectively do insights on the customer, right? Assuming you have the consent of course, right? We talked about that, right? So, you can mine the same information. Now, you have that information tagged. It's all nicely calibrated in repositories etc. Now, you can use that for your analytics, You can use that for your top line growth or even see what your internal processes are, that can make you more effective from an operations perspective. And how you can get that. >> So you're talking about these new foundations of your data governance strategy and yet we're also talking about this at a time where there's a real shortage of people who are data experts and analytics experts. What are what is Northern Trust doing right now to make sure that you are you have enough talent to fill the pipeline? >> So, we are doing multiple things. Like most companies, we are trying a lot of different things. It's hard to recruit in these areas, especially in the data science area, where analytics. And people not only need to have a certain broad understanding of your business, but they also need to have a deep understanding of all of the statistical techniques etc., right? So, that combination is very hard to find. So, what we do is typically, we get interns, from the universities who have the technology knowledge and we couple them up with business experts. And we work in those collaborated kind of teams, right? Think about agile teams that are working with business experts and technology experts together. So that's one way to solve for that problem. >> Great, well Sanjay, thank you so much for joining us here on the cube. >> Thank you. Thank you. >> Good to see you again. >> We will have more from the IBM CDO Summit just after this.

>> Narrator: Live from Munich, Germany it's theCUBE, covering IBM, fast track your data. brought to you by IBM. >> Welcome back to Munich, Germany everybody. This is theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise, and we're here at the IBM signature moment, Fast Track Your Data in Munich, where enterprise data governance is a huge theme. We're going to talk about that right now, I'm Dave Vellante with my co-host, Jim Kobielus. Sanjay Saxena is here, he's the senior Vice-President at Northern Trust. Sanjay, welcome to theCUBE, thanks for coming on. >> Thank you, thank you, thanks for having me. >> So, enterprise data governance is a huge theme here, we're going to get into that, but set up Northern Trust, your organization, and your role. >> So, I am the head for enterprise data governance for Northern Trust. It's an essential enterprise role across all the business units. I've been working with Northern Trust for the last three years to set up the program, and prior to this I worked with Bank of Montreal and other institutions doing similar things. >> So how is enterprise governance evolving? I mean, I go back to, sort of, 2006 for the federal rules of civil procedure when electronic, you know, records became admissible in courts, and that set off a whole chain reaction, and plugging the holes with email archiving, and it was just really scratching the surface. We've kind of evolved there, is governance a strategic imperative? Why is it a strategic imperative? And how is it evolving? >> Well, our program has significantly evolved over the past three years. Partly because of how the market conditions are, and what the regulators expect. We fundamentally started our program focused on regulations, risk and compliance, like most banks did. But now we are a very broad based program within the company. So not just a risk department or the finance department, but also the business units are asking for data governance and for quality. And we are in the asset management, asset servicing business. And a lot of our customers, we manage their data. So they are expecting this as stable stakes at this point in time. So we are realizing a lot of value of the data governance in the business units as well, in addition to the risk and compliance usage. >> So how has governance evolved? I mean I went back ten, eleven years which is like ancient history in these days. How has your, sort of, data governance strategy evolved, and where has it come from, and where are you now, and where are you going? >> So, about two to four years back there wasn't anything formal when it comes to governance, it was very specific to certain units of data, certain types of data. For example, most companies are very concerned about the pricing data. And that's where they would have governance. But it was never a broad based program. Nor was there an operating model around governance and organization, structure, teams of people, so over the past three or four years we've seen that evolution. So now I have a number of data stewards as part of my team, and within the business units, whose sole business is to do governance. We have formally establishing data governance principles and practices and policies. Years back, even three years back, you'd go to most organizations you wouldn't find any policies and practices of data governance. So those are two distinct ways that the governance has evolved in terms of the model. And also along with that has been an evolution of tools and technology and where IBM has heard this a lot. >> Generally the line of business people, you know, governance, compliance, even security, and it's changing but, generally if I hear those words as a business person, it's ugh, it's going to slow me down, it's going to cost me time, it's going to cost me money, bureaucracy overhead. How do you as a governance professional address that? Can you make governance a source of value? >> Right, so governance is a very abstract concept. Most people, most businesses, don't want they want to run away from anything close to governance, right? >> Dave: No accountability. >> No accountability, right? They want to be focused on their revenues, etc. So one way to make that, and what we've done is we've made it very tangible by showing them data quality in terms of metrics, in terms of dashboards, in terms of showing them cost of poor data quality, right? In terms of, for example, a simple example is, a customer names an address as being wrong, may not mean very much to a regulator, but it is really important from a business perspective for a relationship manager in our business. So what we've done is shown that to them and shown positive trending towards the mediation and tied it to the business outcomes. So I wouldn't say that we are there yet, it's a journey, but there's been a lot of evolution in the process, they are accepting my organization, they are accepting the roles, and they are accepting the work we're doing. And they want to be part of it. So that's how I see them evolve, I see this as a continuous evolvement even beyond that. And ultimately I see them using governance almost as a product. Right now it's, we provide a lot of data to our end consumers, to our asset management, to management companies, to fund administrators, and others, right? And data governance is an implicit component of that, right? We don't charge money for it, right? But in the world of the future I see that, depending on the tier of the customer, depending on the kind of data that we're supplying them, we can have different tiers of data quality and governance around that, and we could explicitly charge. So they're excited about that project, about that prospect, and they want to work with us on that. >> And you, do you have a chief data officer? >> Sanjay: Yes. >> Okay, so, is it a relatively new role? Or it's been around, I mean typically in your industry it's regulated and so you tend to have more propensity for CDOs, but has there been one for a while, or a couple years? >> Sanjay: It's been around for two years. >> Just two years? Okay. >> Sanjay: Yeah, two plus years, yes. >> Okay, so that chief data officer that emergent role, looks at things like data quality, looks at how to monetize data, tries to form relationships with the line of business, all those things. Companies generally are just starting to understand, all right, how do I, how does data effect my monetization? Not so much how do I sell the data, but how does data help my cut costs or increase revenue? >> Yeah, well, or, yeah, related to that very much is, for example, do you compute a metric such as customer lifetime value that you would sacrifice if you don't, if your business doesn't consolidate multiple inconsistent customer data sets down to one canonical data set that you can use then to, high quality, that you can use to drive targeting marketing, and better engagement. Do you report like a CLV, customer lifetime value, as part of your overall governance strategy or thought about doing that? >> We've thought about doing that, and those metrics are evolving in our organization, but even a little bit more basic metrics around is your customer contactable, right? Do you have the right information about them? Or, for the share of the wallet, is it actually a better example? Like we have different investment products, and we have different products that we sell to our wealthy individuals. What portion of those, what is the average number of products that they have from us? And to be able to monitor, and measure it, across a meter of time, is a really important thing for businesses to do. >> Okay, let's, I see your button here, your badge here, it says IBM analytics, global elite, I think there was a little reception last night by the lake, and you know, all the execs took you guys out and wined and dined you and, you know, that's good. We saw that action going on. But so, what is that mean, a global elite? So that means you're a top-tier customer, what's your relationship with IBM, and how has that evolved? >> Right, so yeah, so North Interest buys a lot of stuff from IBM, lot of technology, tools, consulting, so we are, we are one of the top tier customers, and that's why we are part of the global elite program. And our relationship has really really evolved over time, especially in the governance base I'm talking about, and IBM has been a significant partner for us in terms of the initial strategy around governance, which we implemented and we are still on track to get that fully implemented. Equally important is the tools and technologies that they brought into the space. So most of the vendors provide segregated tools for different portions of data governance. You'll find some people good in lineators, good in meta data, glossary, etc. But IBM has an end to end suite, and we've been able to integrate that, we've been able to make it a single solution, single integrated solution, and that's really benefited us. So that's really been the contribution of IBM. >> And, okay, so can you talk more about the business impact about that single integrated solution? >> So the business impact is that today, unlike ever in the past, we have data quality dashboards. And this is, we are measuring data quality across thousands of data attributes on a monthly basis. We are publishing trends around data quality. We have that, we are also, for people, developers, for business people who are interested in where the data is coming from, we have lineage, we have an enterprise glossary. So it's a one stop solution across all of those. The business people are able to look at that, whether it's risk, finance, or business units, they're able to look at that on a monthly basis. We're able to provide implications of quality, we provide trending, so it is really taking us towards making us a data driven organization. >> Have you been a user, at least a beta-user of the governance catalog that IBM has announced today? What are your thoughts about that? >> Yes, so the information governance catalog we've been using that for the last three years. We have, as I said, about several thousand data elements in the information governance catalog. And what that does is, it creates that single vocabulary within the bank, and you cannot even imagine how difficult that is. Because for two business units to agree on the meaning of a term, it requires a lot of discussions and deliberations. But having a one simple repository that has all of the meta-data is one aspect of it. The second thing is, which has got implications in terms of data security and protection, that we are able to tag the data as sensitive data. For example, for GDPR, so we are using the same tool to be able to tag sensitive data elements and, as I said, the whole lineage, where does it reside, where does the data flow into, all of those things are very very easy and have been implemented in the IGC. >> Sanjay, what would you say is your biggest challenge as an enterprise data governance professional? >> Team management is still the biggest challenge, it is. As I said, it's a journey. And getting to every individual in the enterprise, for example, to start using this glossary that I just talked about. Or getting people to systematically look at data quality across the board. The other piece is the funding around data initiatives, right? So everyone's used to large transformation programs, but when I come up with a list of, here are the top ten data quality issues that need to be fixed, everybody looks over everybody else's shoulder I guess, and says, who's going to pay for it, right? And is this really our problem, or is this the problem of somebody else, right? So we get into a lot of those discussions, but it's a journey, as I said. >> Well, so you need executive support. To get executive support you have to demonstrate how it drives business values. So that's where it's, there's some carrot and stick involved. Well the stick is, well, we got to comply. We've heard a lot about GDRP and how that's going to, you know, cause pain. Okay, so that's the stick. The carrot is the data monetization, and the data value piece, connecting data quality to data value is that, you know, enticement, is it not. >> That's absolutely right, and the more and more we can show monetization of data, or even the fact that, because of that data governance or quality, we were able to acquire a new customer. It doesn't all need to be tangible is what I'm saying. But the more and more we can show monetization, the better off we'll be in terms of selling the program. >> Excellent. Well, Sanjay thanks very much for coming to theCUBE and sharing your experience, we really appreciate it. >> Sanjay: Thank you, thank you very much. >> You're welcome. (techno music)