>> Announcer: Live from Las Vegas, it's The Cube, covering IBM Think 2018, brought to you by IBM. >> Welcome back to The Cube. We are live in sunny Las Vegas at the inaugural IBM Think 2018 event. I'm Lisa Martin with Dave Vellante. Dave, this weather has got to beat Boston hands down, right? >> It was beautiful yesterday, about 15 degrees in Boston, snowy. >> So you thawed out since you've gotten here? >> I took the snowshoes out, actually. Life makes lemons. >> Exactly, and we have another cold-weather guest who's probably thawing out as well, Ranjana Young, the senior vice president of Enterprise Data Services from Northern Trust, welcome. >> Thank you, thanks for having me. >> We're excited to chat with you. You have a role at Northern Trust, and your mission is all-around data, five-core competencies, including data governance and stewardship, data quality, master data management, enterprise integration with data platforms. Tell us a little bit about your role, how long you've been doing that, and really what this focus on data is enabling for Northern Trust. >> Sure, I want to talk first about our mission as you had mentioned. I think it was critical to establish a broad mission for Northern Trust. We wanted to make sure that we establishing an enterprise data program that enabled our customer needs and overall our customer experience, but also truly helped support our regulatory needs that we had, and it was critical to establish those two as the main goals, not just one or the other. And then the role, I call myself a change agent because establishing capabilities that you talked about, it is difficult to do, with a lot of legacy that we have. The firm has been in existence for 128 years To establish a data-driven culture was very different. I think we were known to do provide good business solutions, but a lot with the gut, given that we were good at it, but how do you make sure that you change that culture and have a relationship managers and others really think differently and use data to provide those solutions to our clients. >> I remember when I met Inderpal Bhandari, I'm sure you know him, and he said that he has a framework for a data leader, and he said there are five things a data leader has to do to get started, and three are in parallel, or sorry, three are linear, two are in parallel. I don't know if you've heard this rap, but I'd like to sort of explore them and see how your three are generally. He said you start with understanding how the organization monetizes data, not directly, maybe selling data, but how it contributes, and then the next one was sort of data access and then data quality. Those are the sort of sequential activities, and then the parallel ones were form relationships with a line of business and then re-skill. So those are his five. How did you approach it, what was different, what was similar, what were some of the challenges that you had in doing that? >> Sure. If I had to think about kind of, to correlate some of the components of the strategy, skills is an important thing. When I started establishing the team three years ago, it was critical that we had to bring some of the core skills within the firm because they had the business capabilities, they understood the systems, they understood kind of the skeletons that were in the closets and knew the culture and also embraced the challenges and still could find solutions. And then you had to bring external folks that really had the capability to drive that change, had the mastery of management skills to really support and set up an account domain and a party domain, a reference data domain, especially an asset domain, et cetera. So we had to look at kind of a conglomerate of individuals to do that. And then if you look at kind of where was the starting point in terms of really establishing the program was, we were going through a transformation to really re-platform a lot of our legacy, whether it was our valuation system or our cash platform, others, and data was a thread throughout all of those programs, so it was critical to establish and think and take bite-sized chunks, it was important to think about, okay, throughout all the programs, what is the important data that we could kind of understand, so we focused quite a bit on initially looking at critical data and looking at critical data from a master data perspective, so asset data, which is very critical to the work that we do on the institutional side. As you know, we had a management asset servicing company. Data is an asset for us, we enrich the data. We provide services around that today, and have been, and so embedding data governance through that process was important, and also our clients were really looking for the enriched data but also were looking for clean information but also were looking for where did that data come from? Where does the definition of this data? So kind of giving them that external catalog of here's the data, but here's the enriched data and here's the metrics for data quality around it, and then here's the definitions for it. So to some extent, that drove change because of customers were looking for it, and a lot of the capabilities that were foundational to the firm, we're starting to externalize, especially the meta-data catalog, et cetera. >> So if I could play that back, so you started the team, all right, you said, okay, I need to build a team. I think I heard that, and then the data quality, and then presumably, okay, who has access to this data? Is that about right? >> So I started with the mission to say, we have to do this for both arms, the left arm being our customer experience and making sure that we change the way we're doing our work there, or enhance the work so that our customer experience was better, and then obviously the regulatory, make sure that we need the regulatory. So for that, we needed five core competencies. We knew that we had to establish a role of the steward, a role of the custodian, so the team started to become very critical then, and then we knew that we had some gaps in our master data management capability, a complete gap in having integrated data platforms. I notice I've talked a little bit about we established a whole strategy and architecture for ING. I totally relate to how we had to do the same. Each silo did their own particular thing. The management did their own thing. >> David: By data. >> The institutional side did their own thing. Asset management was, I would say, a lot more mature. So I would say if you were to think about it, it's establishing the mission and establishing the team. >> And then, just one last follow-up. The services that you're providing, data services, those are delivered through your organization, the IT organization, what's the practice? >> We have a partnership, a very collaborative partnership that we work together. The technology team does all the build for the work, we work collaboratively to kind of build a strategy of what solutions need to be first versus later, given the client priorities and our institutional side, our business unit priorities, so that's a collaborative effort, working together. >> So speaking of collaboration, you mentioned earlier that it was really key to have both the veterans within Northern Trust and their expertise that you said kind of the skeletons, that they know where things are buried, as well as that maybe external, you might say more fresh perspective. You also talked about, we chatted before we went live, about governance. Seems like what you guys have done is kind of flipped governance from being viewed as potentially an inhibitor to really empowering, being an empowering capability. Can you tell us how you've leveraged data governance to empower a data-driven culture within a business that is 128, I think, years old, you said? >> Yes, that's right. So, for us, I think that while we were establishing the program, it was very critical to understand kind of the challenges on the institutional side first because they had the maximum number of challenges with data. Again, because we're an asset servicing company, a data is an asset, we enrich that information and provide that information, but what was happening was it was taking us so much longer to provide these solutions to our clients, so we've embedded, now, the data governance framework as a part of that solution, and our clients are seeing the value, so if you look at one of the customers that we're working with, we actually have externalized our catalog where they understand now what data that they're receiving, and you're speaking the same language, and that was not the case before. But again, as I said, if we didn't do the foundational work of cataloging the information, understanding what the data is, where the data is, what the data assets are, we just couldn't have done that, so it's really paying off because of that. >> How has that affected your ability to be prepared for GDPR, which obviously went into effect last year, the fines go into effect in May of this year? What was the relationship there? >> So we have worked very, very closely with our chief privacy officer, and we've really done a phenomenal job of identifying where our highly sensitive data assets are. We're in the process of cataloging all of them through the unified governance framework that we've established, so we leverage IBM's IGC NIA to do all that work, and the lineage all the way to the authentic source, which is something the regulators definitely are looking for, so are we fully, completely done yet? No, so we're in that journey, and with unstructured data, we're looking at discovery tools to kind of provide that. We have a solution that's a little manual at this point, but we hope to kind of make more progress on that side. >> I got to ask you, so around 17%, the data suggests, 17% of the IT, technology industry is women, but I was at an IBM, it was a Data Divas breakfast that I crashed, I snuck in, one of the few guys there. >> Oh, very cool. And there was a stat that around 30% of data leaders are women, I don't know, it was a sort of a small sample, who knows? Sounded a little high. Somebody said it's because it's a thankless job and women have to take it on, so thoughts on women in tech, women in this role, perspectives. >> So I am excited to meet a few here at the conference. That statistic is pretty high that you're stating. I don't see that. >> David: It's outside that. >> In the industry, I do find myself sometimes as a lone warrior, at least in the industry forums, but I think it's growing. I think especially women in technology, women in leadership on the line of business side is growing, and Northern Trust, I'm very proud to say, is big around diversity and providing opportunities to women, so from that perspective, I think I'm excited that women are taking interest in data, yes, it is a very hard job, so I think, I feel like we are organized, we get a lot done at the same time, so I think it's really helped. >> Other than it's the right thing to do, are there other sort of business dimensions? Is it Mars versus Venus? Are there sort of enrichments that a woman leader brings to the equation, or is it just because it's the right thing to do? >> I've seen tenacity women have. No offense to anyone, I think the higher tenacity to be persistent. >> I don't take offense. >> To be methodical, to be methodical, and also to have the hard discussions in a very factual way sometimes, but also in, yes, this is the right thing to do, but is there ways we could make this change happen in a systematic, bite-size chunk way. Sometimes I think those coercive conversations help a lot more than the others, and I think, to me, I would say tenacity, tenacity. >> I love that word. I have to say, that's a word that's oftentimes associated with males. A lot of times a tenacious woman, it's a different adjective, right? It's a term, I don't know, Lisa, what your experience has been, so that's good, a good choice of words in my view. >> I've heard pushy before, and I think what they really meant >> David: There you go, okay. >> Is persistence. (laughs) >> That's right. >> A man is tenacious, a woman is pushy. You hear that a lot. >> Right, I think it's persistence. So last question for you. Here we are at the inaugural IBM Think 2018. You guys are an IBM Analytics Global Elite Partner. Can you talk to us a little bit about that strategic partnership and what it means for Northern Trust? >> This partnership has really helped us tremendously in the last three years while we were putting the strategy to action while operationalizing data governance, while operationalizing a lot of the capabilities we thought we would have but really kind of bringing that to life. We're also really excited because lot of the feedback that we've provided has gone into kind of redoing some of the products within IBM, so we've definitely partnered and done lot of testing for some of the ones, the beta versions, and it's also helped us, I think, sometimes it's been like a marriage. We've had hard times getting through certain hurdles, but it really has paid off, and I think the other thing is we've really operationalized governance to the core at Northern Trust. I think IBM is also seeing value in sharing that our story with others because others have started the journey but may have taken certain different approaches to making that happen, so all in all, I think that the unified governance framework has really helped us, and I think we really love the partnership. >> As a client, what's on their to-do list? What's on IBM's to-do list for you? >> So I think one of the things that we've been talking quite a bit is we have a new CIO, and he's really interested in the cloud strategy, I know you've been talking about that. Again, we're a bank, so due to regulation there's strategies in terms of private versus public cloud. That's one conversation we'll definitely want to take further. We want more integrated tooling within the unified governance platform. That's something that's been a topic that we've discussed quite a bit with them. AI, machine learning, robotics is huge for us, so how do we leverage Watson much more? We've done a few POCs, how do we really operationalize and make sure that that's something that we do more of, so I think I would say those three. >> So sounds like a very symbiotic relationship. >> Ranjana: It is. >> Slash marriage that you have. Ranjana, we want to thank you for joining us and sharing how really kind of you're exhibiting the term change agent in a tenacious way. >> Okay, thank you. >> I feel like I want to say I'm flanked between two data divas, you don't take offense at that, do you? >> No, not at all. It's a compliment. >> You crashed an event. I'm seeing a new >> I like that. >> Twitter handle come up here. We want to thank you so much again for stopping by and sharing. Congrats on your success, and we hope you have a great time here. Enjoy the sunshine! Maybe bring some back to Chicago. >> Will do, will do, yeah. Thanks again, very much. >> And for Dave Vellante, I'm Lisa Martin. We want to encourage you to check out thecube.net to watch all of the videos that we have done so far and will be doing at IBM Think 2018, and of course on all of the shows that we do. Also, head over to siliconangle.com. That's our media site where you're going to find pretty much in near real time synopsis and stories on not just what we're doing here but everything around the globe. Again, for Dave Vellante, I'm Lisa Martin, live from IBM Think 2018 in Vegas. We'll be right back after a short break with our next guest.

>> It's the Cube covering IBM cheap Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts Day villain day and still minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. We here at the Chief Data Officers Summit that IBM is hosting in Boston. I'm joined by Courtney Abercrombie. According your your title's too long. I'm just gonna call you a cognitive rockstar on >> Alec Crane is >> here from Yusa. System by President, Vice President at that firm. Welcome to the Cube. Great to see you guys. Thank you. So this event I love it. I mean, we first met at the, uh, the mighty chief data officer conference. You were all over that networking with the CEO's helping him out and just really, I think identified early on the importance of this constituency. Why? How did you sort of realize and where have you taken it? >> It's more important than it's ever been. And we're so grateful every time that we see a new chief data officer coming in because you just can't govern and do data by committee. Um, if you really hope to be transformational in your company. All these huge, different technologies that are out there, All this amazing, rich data like weather data and the ability to leverage, you know, social media information, bringing that all together and really establishing an innovation platform for your company. You can't do that by committee. You really have to have a leader in charge of it. and that’s what chief data officers are here to do. And so every time we see one, we're so grateful >> that just so >> that we just heard from Inderpal Bhandari on his recommendation for how you get started. It was pretty precise and prescriptive. But I wonder, Alan. So tell us about the chief data officer role at USAA. Hasn't been around for a while. Of course, it's a regulated business. So probably Maur, data oriented are cognizant than most businesses. But tell us about your journey. >> We started probably about 4 or 5 years ago, and it was a combination of trying to consolidate data and analytics operations and then decentralized them, and we found that there was advantages and pros and cons of doing both. You'd get the efficiencies, but once you got the efficiencies, you'd lose the business expertise, and then we'd have to tow decentralize. So we ended up landing a couple of years ago. What we call a hub and spoke system where we have centralized governance and management of key data assets, uh, data modelling data science type work. And then we still allow the, uh, various lines of business to have their own data offices. And the one I run for USAA is our distribution channels office for all of the data and analytics. And we take about 100,000,000 phone calls a year. About 2,000,000,000 webb interactions. Mobile interactions. We take about 18,000 hours. That's really roughly two years of phone conversation data in per day. Uh, we take about 50,000,000 lines of, uh, Web analytic traffic per day as well. So trying to make sense of that to nurture remember, relationships, reinforce trust and remove obstacles >> for your supporting the agent systems. Is that right? >> I support the agent systems as well as the, um, digital >> systems. Okay. And so the objective is obviously toe to grow the business, keep it running, keep the customers happy. Very operate, agent Just efficient. Okay. Um and so when you that's really interesting. This sort of hub and spoke of decentralization gets you speed and closer to the business. Centralization get you that that efficiency. Do you feel like you found that right balance? I mean, if you think so. I >> think you know, early on, we it was mme or we had more cerebral alignment, you know, meaning that it seemed logical to us. But actually, once the last couple of years, we've had some growing pains with roles, responsibilities, overlaps, some redundancy, those types of things. But I think we've landed in a good place. And that's that's what I'm pretty proud of because we've been able to balance the agility with the governance necessary toe, have good governance and put in place, but then also be able to move at the speed the businessmen. >> So Courtney, one of things we heard one of the themes this morning within IBM it's of the role of the chief Data officer's office is to really empower the lines of business with data so that you can empower your customers is what Bob Tatiana was telling us, right? With data. So how are you doing? That is you have new services. You have processes or how is that all working >> right? We dio We have a lot of things, actually, because we've been working so much with people like Allen's group who have been leaders at, quite frankly, in establishing best practices on even how to set up these husbands votes. A lot of people are, you know, want to talk, Teo, um, the CDO and they've spun off even a lot of CEOs into other organizations, in fact, but I mean, they're really a leader in this area. So one of the things that we've noticed is you know, the thing that gives everybody the biggest grief is trying to figure out how to work with unstructured data. Um, and all this volume of data, it's just insane. And just like I was saying in the panel earlier, only about 5% of your actual internal data is enough to actually create a context around your customers. You really have to be able to go with all this exogenous data to understand what were the bigger ramifications that were going on in any customer event, whether it's a call in or whether it's, uh, you know, I'm not happy today with something that you tried to sell me or something that you didn't respond too fast enough, which I'm sure Alan could, you know, equate to. But so we have this new data as a service that we've put together based on the way the weather data has, the weather company has put their platform together. We're using a lot of the same kind of like micro services that you saw Bob put on the screen. You know, everything from, I mean, open source. As much open sources we can get, get it. And it's all cloud based. So and it's it's ways to digest and mix up both that internal data with all of that big, voluminous external data. >> So I'm interested in. So you get the organizational part down. Least you've settled on approach. What are some of the other big challenges that you face in terms of analytics and cognitive projects? Your organization? How are you dealing with those? >> Well, uh, >> to take a step back, use a We're, uh, financial services company that supports the military and their families. We now have 12 million members, and we're known for our service. And most of the time, those moments of truth, if you will, where our service really shines has been when someone talks to you, us on the phone when those member service reps are giving that incredible service that they're known for on the reason being is that the MSR is the aggregator of all that data. When you call in, it's all about you. There's two screens full of your information and the MSR is not interested in anything else but just serving you, our digital experiences more transactional in orientation. And it was It's more utilitarian, and we're trying to make it more personal, trying to make it more How do we know about you? And so one of the cues that were that were taking from the MSR community through cognitive learning is we like to say the only way to get into the call is to get into the call, and that is to truly get into the speech to text, Then do the text mining on that to see what are the other topics that are coming out that could surface that we're not actually capturing. And then how do we use those topics at a member level two then help inform the digital experience to make it more personal. How do I detect life events? Our MSR's are actually trained to listen for things like words like fiance, marriage moving, maybe even a baby crying in the background. How do we take that knowledge and turn that into something that machine learning can give us insights that can feedback into our digital transact actions. So >> this's what our group. >> It's a big task. So So how are >> you doing that? I mean, it's obviously we always talk about people processing technology. Yeah, break that down for us. I mean, how are you approaching that massive opportunity? >> Part of it is is, uh, you know, I look at it. It is like a set of those, you know, Russian nesting dolls. You know, every time you solve one problem, there's another problem inside of it. The first problem is getting access to the data. You know, where and where do you store? We're taking in two years of data per day of phone call data into a system where you put all that right and then you're where you put a week's worth a month's worth a quarter's worth of data like that. Then once you solve that problem, how do you read Act all that personal information So that that private information that you really don't need that data exhaust that would actually create a liability for you in our in our world so that you can really stay focused on what of the key themes that the member needs? And then the third thing is now had. Now that you've got access to the data, it's transcribed for you. It's been redacted from its P I I type work well, now you need the horse power and of analysts on, we're exploring partnerships with IBM, both locally and in in the States as well as internationally to look at data science as a service and try to understand How can we tap into this huge volume of data that we've got to explore those types of themes that are coming up The biggest challenges in typical transaction logging systems. You have to know what your logging You have to know what you're looking for before you know what to put the date, where to put the data. And so it's almost like you kind of have to already know that it's there to know how much you're acquiring for it and what we need to do more as we pivot more towards machine learning is that we need the data to tell us what's important to look at. And that's really the vat on the value of working with these folks. >> So obviously, date is increasingly on structure we heard this morning and whatever, 80 90% is structured. So here you're no whatever. You're putting it into whatever data fake swamp, ocean, everything center everywhere, and you're using sort of machine learning toe both find signal, but also protected yourself from risk. Right. So you've got a T said you gotta redact private information. So much of that information could be and not not no schema? Absolutely. Okay, So you're where are you in terms of solving that problem in the first inning or you deeper than that, >> we're probably would say beyond the first inning, but we so we've kind of figured out what that process is to get the data and all the piece parts working together. We've made some incredible insights already. Things that people, you know, I had no idea that was there. Um, but, uh, I'd say we still have a long way to go. Is particularly terms of scaling scaling the process, scaling the thie analytics, scaling the partnerships, figuring out how do we get the most throughput? I would say it's It's one of those things. We're measuring it on, maybe having a couple of good wins this year. A couple of really good projects that have come across. We want to kind of take that tube out 10 projects next year in this space. And that's how we're kind of measuring the velocity and the success >> data divas. I walked away and >> there was one of them Was breakfast this morning. Data divas. You hold this every year. >> D'oh! It's growing. Now we got data, >> dudes. So I was one of the few data dudes way walked in >> one of the women chief date officers. I got no problem with people calling me a P. >> I No. Yeah, I just sell. Sit down. Really? Bath s o. But also, >> what's the intent of that? What learning is that you take out of those? >> I think it's >> more. It's You know, you could honestly say this isn't just a data Debo problem. This is also, you know, anybody who feels like they're not being heard. Um, it's really easy to get drowned out in a lot of voices when it comes to data and analytics. Um, everybody has an opinion. I think. Remember, Ursula is always saying, Ah, all's fair in love, war and data. Um and it feels like, you know, sometimes you go, I'll come to the table and whoever has the loudest voice and whoever bangs their test the loudest, um, kind of wins the game. But I think in this case, you know, a lot of women are taking these roles. In fact, we saw, you know, a while back from Gardner that number about 25% of chief data officers are actually women because the role is evolving out of the business lines as opposed Thio more lines. And so I mean, it makes sense that, you know, were natural collaborators. I mean, like the biggest struggle and data governance isn't setting up frameworks. It's getting people to actually cooperate and bring data to the table and talk about their business processes that support that. And that's something that women do really well. But we've got to find our voice and our strength and our resolve. And we've got to support each other in trying to bring more diverse thinking to the table, you know? So it's it's all those kinds of issues and how do you balance family? I mean, >> we're seeing >> more and more. You know, I don't know if you know this, but there's actual statistics around millennials and that males are actually starting to take on more more role of being the the caregiver in the family. So I mean as we see that it's an interesting turnabout because now all the sudden, it's no longer, you know, women having that traditional role of, you know, I gotta always be home. Now we're actually starting to see a flip of that, which is which is, >> You know, I think it's kind of welcome. My husband's definitely >> I say he's a better parent than me. >> Friday. It's >> honest he'll watch this and he >> can thank me later that it was >> a great discussion this morning. Alan, I want to get your feedback on this event and also you participate in a couple of sessions yesterday. Maybe you could share with our audience Some of the key takeaways in the event of general and specific ones that you worked on yesterday. >> Well, I've been fortunate to come to the event for a couple of years now. And when we were just what 50 or so of us that were showing up? So, you know, I see that the evolution just in a couple of years time conversations have really changed. First meeting that we had people were saying, Where do you report in the organization? Um, how many people do you have? What do you do for your job? They were very different answers to any of that everywhere. From I'm an independent contributor that's a data evangelist to I run legions of data analysts and reporting shops, you know, and so forth and everything in between. And so what I see what it's offers in first year was really kind of a coalescing of what it really means to be a data officer in the company that actually happened pretty quickly in my mind, Um, when by seeing it through through the lens of my peers here, the other thing was when you when you think about the topics the topics are getting a lot more pointed. They're getting more pointed around the monetization of data communicating data through visualization, storytelling, key insights that you, you know, using different technologies. And we talked a lot yesterday about storytelling and storytelling is not through visual days in storytelling is not just about like who has the most, you know, colors on on a slide or or ah you know, animation of your bubble charts and things like that. But sometimes the best stories are told with the most simple charts because they resonate with your customers. And so what I think is it's almost like kind of getting a back to the basics when it comes to taking data and making it meaningful. We're only going to grow our organizations and data and data scientists and analysts. If we can communicate to the rest of the organization, our value and the key to creating that value is they can see themselves in our data. >> Yeah, the visit is we like to call it sometimes is critical to that to that storytelling. Sometimes I worry and we go onto these conferences and you go into a booth and look what we can do with machine learning, and we would just be looking at just this data. So what do I do? What >> I do with all this? Yeah. >> I don't know how it would make sense of it. So So is there a special storyteller role within your organization or you all storytellers? Do you cross train on that? Or >> it's funny you'd ask that one of the gentlemen of my team. He actually came to me about six months ago, and he says I'm really good at at the analysis part, but I really have a passion for things like Photoshopped things like, uh uh, uh the various, uh, video and video editing type software. He says I want to be your storyteller. I want to be creating a team of data and analytics storytellers for the rest of the organization. So we pitched the idea to our central hub and spoke leadership group. They loved it. They loved the idea. And he is now, um, oversubscribed. You would say in terms of demand for how do you tell the data? How do you tell the data story and how it's moving the business forward? And that takes the form kind of everything from infographics tell you also about how do you make it personal when, when? Now 7,000 m s. Ours have access to their own data. You know, really telling that at a at a very personal level, almost like a vignette of animus are who's now able to manage themselves using the data that they were not able able tto have before we're in the past, only managers had access to their performance results. This video, actually, you know, pulls on the heartstrings. But it it not only does that, but it really tells the story of how doing these types of things and creating these different data assets for the rest of your organization can actually have a very meaningful benefit to how they view work and how they view autonomy and how they view their own personal growth. >> That's critical, especially in a decentralized organization. Leased a quasi decentralized organization, getting everybody on the same page and understand You know what the vision is and what the direction is. It s so often if you don't have that storytelling capability, you have thousands of stories, and a lot of times there's dissonance. I mean, I'm not saying there's not in your in your organization, but have you seen the organization because of that storytelling capability become Mohr? Yeah, Joe. At least Mohr sort of effective and efficient, moving forward to the objectives. Well, >> you know, as a as a data person, I'm always biased thatyou know data, you know, can win an argument if presented the right way. It's the The challenge is when you're trying to overcome or go into a direction. And in this case, it was. We wanted to give more autonomy. Toothy MSR community. Well, the management of that call center were 94 year old company. And so the management of that of that call center has been doing things a certain way for many, many, many, many years. And the manager's having access to the data. The reps not That was how we did things, you know. And so when you make a change like that, there's a lot of hesitation of what is this going to do to us? How is this going to change? And what we're able to show with data and with through these visualizations is you really don't have anything to worry about? You're only gonna have upside, you know, in this conversation because at the end of the day, what's going to empower people this having access and power of >> their own destiny? Yeah, access is really the key isn't because we've all been in the meetings where somebody stands up and they've got some data point in there pounding the table, >> right? Oftentimes it's a man, all right. It >> is a powerful pl leader on jamming data down your throats, and you don't necessarily know the poor sap that he's, you know, beating up. Doesn't think Target doesn't have access to the data. This concept of citizen data scientists begins to a level that playing field doesn't want you seeing that >> it does. And I want to actually >> come back to what you're saying because there's a larger thought there, which is that we don't often address, and that's this change banishment concept. I mean, we we look at all these. I mean, everybody looks at all these technologies and all this information, and how much data can you possibly get your >> hands on? But at the end of >> the day, it's all about trying to create an outcome. A some joint outcome for the business and it could be threatening. It could be threatening to the C suite people who are actually deploying the use of these data driven tools because >> it may go >> against their gut. And, you >> know, oftentimes the poor messenger of that, >> When when you have to be the one that stands up and go against that, that senior vice presidents got it, the one who's pounding and saying No, but I know better >> That could be a >> tough position to be in without having some sort of change management philosophy going on with the introduction of data and analytics and with the introduction of tools, because there's a whole reframing that, Hey, my gut instinct that got me here all the way to the top doesn't necessarily mean that it's going to continue to scale in this new world with all of all of our competitors and all these, you know, massive changes going on in the market place right now. My guts not going to get me there anymore. So it's hard, it's hard, and I think a lot of executives don't really know to invest in that change management, if you know that goes with it that you need to change philosophies and mindsets and slowly introduced visualizations and things that get people slowly onboard, as opposed to just throwing it at him and saying here, believe it. >> Think I mean, it wasn't that >> long ago. Certainly this this millennium, where you know, publications like Harvard Business Review had, uh, cover stories on why gut feel, you know, beats, you know, analysis by paralysis. >> That seems to be changing. And >> the data purists would say the data doesn't lie. It was long as you could interpret it correctly. Let the data tell us what to do, as opposed to trying to push an agenda. But they're still politics. >> There's just things out >> there that you can't even perceive of that air coming your way. I mean, like, Blockbuster Netflix, Alibaba versus standard retailers. I mean, >> there's just things out >> there that without the use of things like machine learning and being comfortable with the use, the things like mission learning a lot of people think of that kind of stuff is >> Well, don't get your >> hoodoo voodoo into my business. You know, I don't know what that algorithm stuff does. It's >> going Yeah, I mean, e. I mean to say, What the hell is this? And now, yeah, it's coming and >> you need to get ready. >> There's an >> important role, though I think instinct, you know, you don't want to dismiss a 20 year leader in a particular operations because they've they've they've getting themselves where they're at because in large part, maybe they didn't have all the data. But they learned through a lot of those things, and I think it's when you marry those things up. And if you kenbrell in a kind of humble way to that kind of leader and win them over and show how it may be validating some of their, um uh yeah, that some of their points Or maybe how it explains it in a different way. Maybe it's not exactly what they want to see, but it's helping to inform their business, and you come into him as a partner, as opposed to gotcha, you know. Then then you know you can really change the business that way. And >> what is it? Was Linda Limbic brain is it just doesn't feel right. Is that the part of the brain that informs you that? And so It's hard to sometimes put, but you're right. Uh, there there is a component of this which is gut feel instinct and probably relates to to experience. So it's It's like, uh, when, when, uh, Deep blue beat Garry Kasparov. We talk about this all the time. It turns out that the best chess player in the world isn't a machine. It's a It's a human in the machine. >> That's right. That's exactly right. It's always the training that people training these things, that's where it gets its information. So at the end of the day, you're right. It's always still instinct to some >> level. I could We gotta go. All right. Last word on the event. You know what's next? >> Don't love my team. Data officer. Miss, you guys. It is good >> to be here. We appreciate it. All right, We'll leave it there. Thank you, guys. Thank you. All right, keep right. Everybody, this is Cuba. Live from IBM Chief Data Officer, Summit in Boston Right back. My name is Dave Volante.