>> From Cambridge Massachusetts, it's the CUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by silicon angle media. >> Welcome back to Cambridge Massachusetts everybody you're watching the cube. The leader in live tech coverage. The cubes two day coverage of MIT's CDOIQ. The chief data officer information quality event. Thirteenth year we started here in 2013. I'm Dave Vallante with my co-host Paul Gillin. Veda Bawo. Bowo. Bawo. Sorry Veda Bawo is here. Did I get that right? >> That's close enough. >> The director of data governance at Raymond James and Althea Davis the former chief data officer of ING bank challengers and growth markets. Ladies welcome to the cube thanks so much for coming on. >> Thank you. >> Thank you. >> Hi Vita, talk about your role at Raymond James. Relatively new role for you? >> It is a relatively new role. So I recently left fifth third bank as their managing director of data governance and I've moved on to Raymond James in sunny Florida. And I am now the director of data governance for Raymond James. So it's a global financial services company they do asset wealth management, investment banking, retail banking. So I'm excited, I'm very excited about it. >> So we've been talking all day and actually several years about how the chief data officer role kind of emerged from the back office of the data governance. >> Mmm >> And the information quality and now its come you know front and center. And actually we've seen a full circle because now it's all about data quality again. So Althea as the former CDO right is that a fair assessment that it sort of came out of the ashes of the back room. >> Yeah, I mean its definitely a fair assessment. That's where we got started. That's how we got our budgets that's how we got our teams. However, now we have to serve many masters. We have to deal with all of the privacy, we have to deal with the multiple compliancies. We have to deal with the data operations and we have to deal with all of the new, sexy emerging technologies. So to do AI and data science you need a lot of data. You need data rich. You need it to be knowledge management, you need it to be information management. And it needs to be intelligent. So we need to actually raise the bar on what we do and at the same time get the credibility from our sea sweet peers. >> Well I think we no longer have the. We don't have the luxury of being just a cost center anymore . >> No. >> Right, we have to generate revenue. So it's about data monetization. It's about partnering with our businesses to make sure that we're helping to drive strategy and deliver results for the broader organization. >> So you got to hit the bottom line. >> Yeah. >> Either raise revenue or cut costs >> Yeah absolutely >> You know directly that can be tangibly monetized. >> Exactly keep them out of jail. Right. Save money >> That too. >> Save money, make money. (inaudible laughter) keep them out of jail. >> Like both CDO's you do not study for this career path because it didn't exist a few years ago. So talk about your backgrounds and how you came to come into this role Veda. >> Yeah absolutely so you know you talked about you know data kind of starting in the bowels of the back office. So I am that person right. So I am an accountant by training. So I am the person who is non legally entity controllership by book journal entries I've closed the books. I've done regulatory reporting so I know what it feels like to have to deal with dirty data every single month end, every single quarter end right. And I know the pain of having to cleanse it and having to deal with our business partners and having experienced that gave me the passion to want to do better. Right so I want to influence my partners upstream to do better as well as to take away some of the pain points that my teams experiencing over and over again it really was groundhog day. So that really made me feel passionate about going into the data discipline. Right and so you know the benefit is great it's not an easy journey but yeah out of accounting finance and that kind of back office operational support was boring right. A data evangelist and some passionate were about it. >> Which made sense because you have to have quality. >> Absolutely. >> Consistency. You have to have so called single version of the truth. >> Absolutely because you look regularly there's light for the financial reports to be accurate. All the time. (laughter) >> Exactly >> How about you? >> I came at it from a totally different angle. I was a marketeer so I was a business manager, a marketeer I was working with the big retail brands you know the Nikes and the Levi's strauss's of the world. So I came to it from a value chain perspective from marketing you know from rolling out retail chains across Europe. And I went from there as a line management position and all the pains of the different types of data we needed and then did quite a bit of consulting with some of the big consultancies accenture. And then rolled more into the data migration so dealing with those huge change projects and having teams from all of the world. And knowing the pains what all of the guys didn't want to work on. I got it all on my plate. But it put me in position to be a really solid chief data officer. >> Somebody it was called like data chicks or something like that (laughter) and I snuck in I was like the lone >> Data chicks >> I was like the lone data dude >> You can be a data chick. It's okay no judgement here. >> And so one of the things that one of the CDO's said there. She was a woman obviously. And she said you know I think that and the stat was there was a higher proportion of women as CDO's than there were across tech which is like I don't know fifty seventeen percent. And she's positive that the reason was because it's like a thankless job that nobody wants and so I just wonder as woman CDO your thoughts on that is that true. >> Well first of all we're the newest to the table right so you're the new kid on the block it doesn't matter if you're man or woman you're the new kid on the block so you know the CFO's got the four thousand year history behind him or her. The CIO or CTO they've got the fifty, sixty year up on us. So we're new. So you have to calve out your space and I do think that a lot of women by nature like to take on things big. To do things that other people don't want to do. So I can see how women kind of fell into that. But, at the same time you know data it's an asset and it is the newest asset. And it's definitely misunderstood. So I do think that you know women you know we kind of fell into it but it was actually something that happened good for women because there's a big future in data. >> Well let's just be realistic right. Woman have unique skillset. I may be a little bias but we have a unique skillset. We're able to solve problems creatively. Right there's no one size fits all solution for data. There's no accounting pronouncement that tells me how to handle and manage my data. Right I have to kind of figure it out as I go along and pivot when something doesn't work. I think that's something that is very natural to women. >> Yeah. >> I think that contributes to us kind of taking on these roles. >> Can I just do a little survey here (laughter) We hear that the chief data officer of function is defined differently at different organizations. Now you both are in financial services. You both have a chief data function. Are you doing the same thing? (laughter) >> Absolutely not! (laughter) >> You know this is data by design. I mean I'm getting lucky I've had teams that go the whole gammon right so. From the compliancy side through to the data operations through to all of the like I said the exotics, sexy you know emerging technologies stuff with the data scientists. So I've had the whole thing. I've also had my last position at ING bank I had to you know lead a team of chief data officers across three different continents Australia, Asia and also Eastern and Western Europe. So it's totally different than you know maybe another company that they've only got to chief data officer working on data quality and data governance. >> So again another challenge of being the new kid on the block right. Defining roles and responsibilities. There's no one globally, universally accepted definition of what a chief data officer should do. >> Right >> Right is data science in or out are analytics in or out. Right. >> Security sometimes. >> Security right sometimes privacy is it or out. Do you have operational responsibilities or are you truly just a second line governance function right? There's a mixed bag out there in the industry. I don't know that we have one answer that we know for sure is true. But I do know for sure is that data is not an IT function. >> Well okay. That's really important. >> It's not an IT asset. >> Yeah. >> I want to say that it's not an IT asset. It is an information asset or a data asset which is a different asset than an IT asset or a financial asset or a human asset. >> But and that's the other big change is that fifteen. Ten to fifteen years ago data was assumed to be a liability right. >> Mmm. >> Federal rules set up a civil procedure we got to get rid of the data or you know we're going to get sued. Number one and number two is that data because it's digital you know people say data is the new oil. I always say it's not. It's more important than oil. >> It's like blood. >> Oil you can only use in one use case. Data you can reuse over and over again. >> Reuse, reuse perpetual. It goes on and on and on. And every time you reuse it the value increases. So I would agree with you it is not the new oil. It is much bigger than that and it needs to I mean I know from some of my colleagues in the profession. We talk about borrowing from other more mature disciplines to make data management, information management and knowledge management much more robust and be much more professional. We also need to be more professional about it as the data leaders. >> So when you're a little panel today. One of the things that you guys addressed is what keeps the CDO up at night. >> Yes >> I presume it's data. (laughter) >> No, no, no. >> It's our payers that don't get it. (laughter) >> That's what keeps us up at night. >> Its the sponsors that keep us up at night. (laughter) So what was that discussion like? >> So yeah I mean it was a lively discussion. Um, great attendance at the panel so we appreciate everyone who came out and supported. >> Full house. >> Definitely a full house. Great reviews so far. >> Yep. >> Okay, so the thing that definitely keeps folks up at night and I'm going to start with my standard one which is quality. Right you can have all of the fancy tools, right you can have a million data scientists but if the quality is not good or sufficient. Then you're no where. So quality is fundamentally the thing that the CDO has to always pay attention to. And there's no magic you know pill or magic right potion that's going to make the quality right. It's something that the entire organization has a rally around. And it's not a one thing done right it has to be a sustainable approach to making sure the quality is good enough so that you can actually reap the benefits or derive the value right from your data. >> Absolutely and I would say you know following on from the quality and I consider that trustworthiness of the data. I would say as a chief data officer you're coming to the table. You're coming to the executive table you need to bring it all so you need to be impactful. You need to be absolutely relevant to your peers. You also need to be able to make their teams in a position to act. So it needs to be actionable. And if you don't have all of that combination with the trustworthiness you're dead in the water. So it is a hard act and that's why there is a high attrition for chief data officers. You know it's a hard job. But I think it's very much worthwhile because this particular asset this new asset we haven't been able to even scratch the surface of what it could mean for us a society and for commercial organizations or government organizations. >> To your point it's not a technology problem when Mark Ramsay who was surveying the audience this morning. He said you know why have we had so many failures and the first hand that went up said. It's because of relations with the database. >> And I wanted to say it's not a technology problem. >> It's a hearts, minds and haves >> Absolutely. Absolutely. You couldn't make an impact to your data landscape without changing your technology. >> You said at the outset how important it is for you to show a bottom line impact. >> Right >> What's one project you've worked on or that you've led in your tenure that did that. >> If we're talking about for example I can't say specifics but if we're looking at one of institutions I worked at in an insurance firm and we looked at the customer journey. So we worked with some of the different departments that traditionally did not get access to data for them to be able to be effective at their jobs. But they wanted to do in marketing was create actually new products to make you know increase the wallet from the existing customers other things they wanted to do was for example, when there were problems with the customers instead of customer you know leaving you know the journey they were able to bring them back in by getting access to the data. So we either gave them insight like you know looking back to make sure that things didn't happen wrong the next time or we helped them giving them information so they could develop new products so this is all about going to market. So that's absolutely bottom line. It's not just all cost efficiency and products to begin . >> Yeah pipeline. (laughter) >> And that's really valid but you know. >> Absolutely so I'll give you one example where the data organization partnered with our data scientists. To try to figure out the best location for various branches. For that particular institution. And it was taking right trillions of data points right about current footprint as well as other information about geographic information that was out there publicly available. Taking that and using the analytics to figure out okay where should we have our branches, our ATM's etc... and then conslidating the footprint or expanding where appropriate. So that is bottom line impact for sure. >> I remember in the early part of the two thousands I remember reading a Harvard business review article about gut feel trumps data every time. But that's an example where no way. >> Nope. >> You could never do better with the gut than that example that you just gave. >> Absolutely. >> Veda. I want to ask you a question. I don't know if you've heard Mark Ramsays talk this morning but he sort of. He sort of declared that data governance was over. >> Mmm. >> And as the director of data governance >> Never! >> I wondered if you would disagree with that. >> Never! >> Look. >> Were you surprised? >> It's just like saying that I should stop brushing my teeth. Right I always will have to maintain a certain level of data hygiene. And I don't think that employees and executives and organizations have reached a level of maturity where I can trust them to maintain that level of hygiene independently. And therefore I need a governance function. I need to check to make sure you brush your teeth in the morning and in the evening. Right and I need you to go for your annual exam to make sure you don't have any cavities that weren't detected. Right so I think that there's still a role for governance to play. It will evolve over time for sure. Right as you know the landscape changes but I think there's still a role right for like governance. >> And that wasn't my takeaway part. I think he said that basically enterprise data warehouse fail massive data management fail. The single data model failed so we punted to governance and that's not going to solve the enterprise data problem. >> I think it's a one leg in the stool. It's one leg in the stool. ` >> Yeah I think I would really sum it up as a monolithic data storage approach failed. Like that. And then our attention went to data governance but that's not going to solve it either. Look, data management is about twelve different data capabilties it's a discipline so we give the title data governance but it means multiple things. And I think that if we're more educated and we have more confidence on what we're doing on those different areas. Plus information and knowledge management then we're way ahead of the game. I mean knowledge graphs and semantics. That puts companies you know at the top of that you know corporate inequality gap that we're looking at right now. Where you know companies are you know five and thousand times more valuable then their competition and the gap is just going to get bigger considering if some of those companies at the bottom of the gap are you know just keep on doing the same thing. >> I agree I was just trying to get you worked up. (laughter) >> Well you did. >> It's going to be a different kind of show. >> But that point you're making. Microsoft, Apple, Amazon and Google, Facebook. Top five companies in terms of market cap. And they're all data companies. They surpass all the financial services, all the energy companies, all the manufacturers. >> And Alibaba same thing. >> Oh yeah. >> They're doing the same thing. >> They're coming right up there. With four or five hundred billion. >> They're all doing the knowledge approach. They're doing all of this stuff and that's a much more comprehensive approach to looking at it as a full spectrum and if we keep on in the financial industry or any industry keep on just kind of looking at little bits and pieces. It's not going to work. It's a lot of talk but there's no action. >> We are losing right. I know that Fintechs are right fringing upon are territory. Right if Amazon can provide a credit card or lend you money or extend you credit. They're now functioning as a traditional bank would. If we're not paying attention to them as real competitors. We've lost the battle. >> That's a really important point you're making because it's all digital now. >> Absolutely. >> You used to be you'd never see companies traverse industries and now you see it Apple pay and Amazon and healthcare. >> Yeah. >> And government organizations teaming up with corporations and individuals. Everything is free flowing so that means the knowledge and the data and the information also needs to flow freely but it needs to be managed. >> Now you're into a whole realm of privacy and security. >> And regulations right. Regulations for the non right traditional banks. So we're doing banking transactions. >> Do you think traditional banks will lose control over the payment systems? >> If they don't move with the time they will. If they don't. I mean it's not something that's going to happen tomorrow but you know there is a category of bank called Challenger banks so there's a reason. You know even within their own niche there's a group of banks. >> I mean not even just payments right. Think about cash transactions like if I do money transfer am I going to my traditional bank to do it or am I going to cashapp. >> I think it's interesting particularly in the retail banking business where you know one banking app looks pretty much like other and people don't go to branches anymore and so that brand affinity that used to exist is harder and harder to maintain and I wonder what role does data play in reestablishing that connection. >> Well for me right I get really excited and sometimes annoyed when I can open up my app for my bank and I can see the pie chart of my spending. They're using my data to inform me about my behaviors sometimes a good story, sometimes a bad story. But they're using it to inform me. That's making me more loyal to that particular institution right so I can also link all of my financial accounts in that one institutions app and I can see a full list of all of my credit cards, all of my loans, all of my investments in one stop shopping. That's making me go to their app more often versus the other options that are out there. So I think we can use the data in order to endear the customer source but we have to be smart about it. >> That's the accountant in you. I just refuse to not look. (laughter) >> You can afford to not look. I can't. >> Thank you. >> Thanks for riling us up. >> Alright thank you for watching everybody we'll be right back with our next guest right after this short break. You're watching the cube from MIT in Boston, Cambridge. Right back. (atmospheric music)