>> Live from Boston, Massachusetts. It's The Cube! Covering IBM Chief Data Officer's Summit. Brought to you by IBM. (techno music) >> Welcome back to The Cube's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host Rebecca Knight along with my Co-host Dave Vellante. We're joined by Jennifer Gibbs, the VP Enterprise Data Management of TD Bank, and Seth Dobrin who is VP and Chief Data Officer of IBM Analytics. Thanks for joining us Seth and Jennifer. >> Thanks for having us. >> Thank you. >> So Jennifer, I want to start with you can you tell our viewers a little about TD Bank, America's Most Convenient Bank. Based, of course, in Toronto. (laughs). >> Go figure. (laughs) >> So tell us a little bit about your business. >> So TD is a, um, very old bank, headquartered in Toronto. We do have, ah, a lot of business as well in the U.S. Through acquisition we've built quite a big business on the Eastern seaboard of the United States. We've got about 85 thousand employees and we're servicing 42 lines of business when it comes to our Data Management and our Analytics programs, bank wide. >> So talk about your Data Management and Analytics programs a little bit. Tell our viewers a little bit about those. >> So, we split up our office of the Chief Data Officer, about 3 to 4 years ago and so we've been maturing. >> That's relatively new. >> Relatively new, probably, not unlike peers of ours as well. We started off with a strong focus on Data Governance. Setting up roles and responsibilities, data storage organization and councils from which we can drive consensus and discussion. And then we started rolling out some of our Data Management programs with a focus on Data Quality Management and Meta Data Management, across the business. So setting standards and policies and supporting business processes and tooling for those programs. >> Seth when we first met, now you're a long timer at IBM. (laughs) When we first met you were a newbie. But we heard today, about,it used to be the Data Warehouse was king but now Process is king. Can you unpack that a little bit? What does that mean? >> So, you know, to make value of data, it's more than just having it in one place, right? It's what you do with the data, how you ingest the data, how you make it available for other uses. And so it's really, you know, data is not for the sake of data. Data is not a digital dropping of applications, right? The whole purpose of having and collecting data is to use it to generate new value for the company. And that new value could be cost savings, it could be a cost avoidance, or it could be net new revenue. Um, and so, to do that right, you need processes. And the processes are everything from business processes, to technical processes, to implementation processes. And so it's the whole, you need all of it. >> And so Jennifer, I don't know if you've seen kind of a similar evolution from data warehouse to data everywhere, I'm sure you have. >> Yeah. >> But the data quality problem was hard enough when you had this sort of central master data management approach. How are you dealing with it? Is there less of a single version of the truth now than there ever was, and how do you deal with the data quality challenge? >> I think it's important to scope out the work effort in a way that you can get the business moving in the right direction without overwhelming and focusing on the areas that are most important to the bank. So, we've identified and scoped out what we call critical data. So each line of business has to identify what's critical to them. Does relate very strongly to what Seth said around what are your core business processes and what data are you leveraging to provide value to that, to the bank. So, um, data quality for us is about a consistent approach, to ensure the most critical elements of data that used for business processes are where they need to be from a quality perspective. >> You can go down a huge rabbit whole with data quality too, right? >> Yeah. >> Data quality is about what's good enough, and defining, you know. >> Right. >> Mm-hmm (affirmative) >> It's not, I liked your, someone, I think you said, it's not about data quality, it's about, you know it's, you got to understand what good enough is, and it's really about, you know, what is the state of the data and under, it's really about understanding the data, right? Than it is perfection. There are some cases, especially in banking, where you need perfection, but there's tons of cases where you don't. And you shouldn't spend a lot of resources on something that's not value added. And I think it's important to do, even things like, data quality, around a specific use case so that you do it right. >> And what you were saying too, it that it's good enough but then that, that standard is changing too, all the time. >> Yeah and that changes over time and it's, you know, if you drive it by use case and not just, we have get this boil the ocean kind of approach where all data needs to be perfect. And all data will never be perfect. And back to your question about processes, usually, a data quality issue, is not a data issue, it's a process issue. You get bad data quality because a process is broken or it's not working for a business or it's changed and no one's documented it so there's a work around, right? And so that's really where your data quality issues come from. Um, and I think that's important to remember. >> Yeah, and I think also coming out of the data quality efforts that we're making, to your point, is it central wise or is it cross business? It's really driving important conversations around who's the producer of this data, who's the consumer of this data? What does data quality mean to you? So it's really generating a lot of conversation across lines of business so that we can start talking about data in more of a shared way versus more of a business by business point of view. So those conversations are important by-products I would say of the individual data quality efforts that we're doing across the bank. >> Well, and of course, you're in a regulated business so you can have the big hammer of hey, we've got regulations, so if somebody spins up a Hadoop Cluster in some line of business you can reel 'em in, presumably, more easily, maybe not always. Seth you operate in an unregulated business. You consult with clients that are in unregulated businesses, is that a bigger challenge for you to reel in? >> So, I think, um, I think that's changing. >> Mm-hmm (affirmative) >> You know, there's new regulations coming out in Europe that basically have global impact, right? This whole GDPR thing. It's not just if you're based in Europe. It's if you have a subject in Europe and that's an employee, a contractor, a customer. And so everyone is subject to regulations now, whether they like it or not. And, in fact, there was some level of regulation even in the U.S., which is kind of the wild, wild, west when it comes to regulations. But I think, um, you should, even doing it because of regulation is not the right answer. I mean it's a great stick to hold up. It's great to be able to go to your board and say, "Hey if we don't do this, we need to spend this money 'cause it's going to cost us, in the case of GDPR, four percent of our revenue per instance.". Yikes, right? But really it's about what's the value and how do you use that information to drive value. A lot of these regulation are about lineage, right? Understanding where your data came from, how it's being processed, who's doing what with it. A lot of it is around quality, right? >> Yep. >> And so these are all good things, even if you're not in a regulated industry. And they help you build a better connection with your customer, right? I think lots of people are scared of GDPR. I think it's a really good thing because it forces companies to build a personal relationship with each of their clients. Because you need to get consent to do things with their data, very explicitly. No more of these 30 pages, two point font, you know ... >> Click a box. >> Click a box. >> Yeah. >> It's, I am going to use your data for X. Are you okay with that? Yes or no. >> So I'm interested from, to hear from both of you, what are you hearing from customers on this? Because this is such a sensitive topic and, in particularly, financial data, which is so private. What are you, what are you hearing from customers on this? >> Um, I think customers are, um, are, especially us in our industry, and us as a bank. Our relationship with our customer is top priority and so maintaining that trust and confidence is always a top priority. So whenever we leverage data or look for use cases to leverage data, making sure that that trust will not be compromised is critically important. So finding that balance between innovating with data while also maintaining that trust and frankly being very transparent with customers around what we're using it for, why we're using it, and what value it brings to them, is something that we're focused on with, with all of our data initiatives. >> So, big part of your job is understanding how data can affect and contribute to the monetization, you know, of your businesses. Um, at the simplest level, two ways, cut costs, increase revenue. Where do you each see the emphasis? I'm sure both, but is there a greater emphasis on cutting costs 'cause you're both established, you know, businesses, with hundreds of thousands, well in your case, 85 thousand employees. Where do you see the emphasis? Is it greater on cutting costs or not necessarily? >> I think for us, I don't necessarily separate the two. Anything we can do to drive more efficiency within our business processes is going to help us focus our efforts on innovative use of data, innovative ways to interact with our customers, innovative ways to understand more about out customers. So, I see them both as, um, I don't see them mutually exclusive, I see them as contributing to each. >> Mm-hmm (affirmative) >> So our business cases tend to have an efficiency slant to them or a productivity slant to them and that helps us redirect effort to other, other things that provide extra value to our clients. So I'd say it's a mix. >> I mean I think, I think you have to do the cost savings and cost avoidance ones first. Um, you learn a lot about your data when you do that. You learn a lot about the gaps. You learn about how would I even think about bringing external data in to generate that new revenue if I don't understand my own data? How am I going to tie 'em all together? Um, and there's a whole lot of cultural change that needs to happen before you can even start generating revenue from data. And you kind of cut your teeth on that by doing the really, simple cost savings, cost avoidance ones first, right? Inevitably, maybe not in the bank, but inevitably most company's supply chain. Let's go find money we can take out of your supply chain. Most companies, if you take out one percent of the supply chain budget, you're talking a lot of money for the company, right? And so you can generate a lot of money to free up to spend on some of these other things. >> So it's a proof of concept to bring everyone along. >> Well it's a proof of concept but it's also, it's more of a cultural change, right? >> Mm-hmm (affirmative) It's not even, you don't even frame it up as a proof of concept for data or analytics, you just frame it up, we're going to save the company, you know, one percent of our supply chain, right? We're going to save the company a billion dollars. >> Yes. >> And then there's gain share there 'cause we're going to put that thing there. >> And then there's a gain share and then other people are like, "Well, how do I do that?". And how do I do that, and how do I do that? And it kind of picks up. >> Mm-hmm (affirmative) But I don't think you can jump just to making new revenue. You got to kind of get there iteratively. >> And it becomes a virtuous circle. >> It becomes a virtuous circle and you kind of change the culture as you do it. But you got to start with, I don't, I don't think they're mutually exclusive, but I think you got to start with the cost avoidance and cost savings. >> Mm-hmm (affirmative) >> Great. Well, Seth, Jennifer thanks so much for coming on The Cube. We've had a great conversation. >> Thanks for having us. >> Thanks. >> Thanks you guys. >> We will have more from the IBM CDO Summit in Boston, Massachusetts, just after this. (techno music)