>> From the Cube Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> Hi everyone, this is Dave Vellante and welcome back to the Cube's coverage of the MIT CDOIQ. God, we've been covering this show since probably 2013, really trying to understand the intersection of data and organizations and data quality and how that's evolved over time. And with me to discuss these issues is Krishna Cheriath, who's the Vice President and Chief Data Officer, Bristol-Myers Squibb. Krishna, great to see you, thanks so much for coming on. >> Thank you so much Dave for the invite, I'm looking forward to it. >> Yeah first of all, how are things in your part of the world? You're in New Jersey, I'm also on the East coast, how you guys making out? >> Yeah, I think these are unprecedented times all around the globe and whether it is from a company perspective or a personal standpoint, it is how do you manage your life, how do you manage your work in these unprecedented COVID-19 times has been a very interesting challenge. And to me, what is most amazing has been, I've seen humanity rise up and so to our company has sort of snap to be able to manage our work so that the important medicines that have to be delivered to our patients are delivered on time. So really proud about how we have done as a company and of course, personally, it has been an interesting journey with my kids from college, remote learning, wife working from home. So I'm very lucky and blessed to be safe and healthy at this time. So hopefully the people listening to this conversation are finding that they are able to manage through their lives as well. >> Obviously Bristol-Myers Squibb, very, very strong business. You guys just recently announced your quarter. There's a biologics facility near me in Devon's, Massachusetts, I drive by it all the time, it's a beautiful facility actually. But extremely broad portfolio, obviously some COVID impact, but you're managing through that very, very well, if I understand it correctly, you're taking a collaborative approach to a COVID vaccine, you're now bringing people physically back to work, you've been very planful about that. My question is from your standpoint, what role did you play in that whole COVID response and what role did data play? >> Yeah, I think it's a two part as you rightly pointed out, the Bristol-Myers Squibb, we have been an active partner on the the overall scientific ecosystem supporting many different targets that is, from many different companies I think. Across biopharmaceuticals, there's been a healthy convergence of scientific innovation to see how can we solve this together. And Bristol-Myers Squibb have been an active participant as our CEO, as well as our Chief Medical Officer and Head of Research have articulated publicly. Within the company itself, from a data and technology standpoint, data and digital is core to the response from a company standpoint to the COVID-19, how do we ensure that our work continues when the entire global workforce pivots to a kind of a remote setting. So that really calls on the digital infrastructure to rise to the challenge, to enable a complete global workforce. And I mean workforce, it is not just employees of the company but the all of the third-party partners and others that we work with, the whole ecosystem needs to work. And I think our digital infrastructure has proven to be extremely resilient than that. From a data perspective, I think it is twofold. One is how does the core book of business of data continue to drive forward to make sure that our companies key priorities are being advanced. Secondarily, we've been partnering with a research and development organization as well as medical organization to look at what kind of real world data insights can really help in answering the many questions around COVID-19. So I think it is twofold. Main summary; one is, how do we ensure that the data and digital infrastructure of the company continues to operate in a way that allows us to progress the company's mission even during a time when globally, we have been switched to a remote working force, except for some essential staff from lab and manufacturing standpoint. And secondarily is how do we look at the real-world evidence as well as the scientific data to be a good partner with other companies to look at progressing the societal innovations needed for this. >> I think it's a really prudent approach because let's face it, sometimes one shot all vaccine can be like playing roulette. So you guys are both managing your risk and just as I say, financially, a very, very successful company in a sound approach. I want to ask you about your organization. We've interviewed many, many Chief Data Officers over the years, and there seems to be some fuzziness as to the organizational structure. It's very clear with you, you report in to the CIO, you came out of a technical bag, you have a technical degree but you also of course have a business degree. So you're dangerous from that standpoint. You got both sides which is critical, I would think in your role, but let's start with the organizational reporting structure. How did that come about and what are the benefits of reporting into the CIO? >> I think the Genesis for that as Bristol-Myers Squibb and when I say Bristol-Myers Squibb, the new Bristol-Myers Squibb is a combination of Heritage Bristol-Myers Squibb and Heritage Celgene after the Celgene acquisition last November. So in the Heritage Bristol-Myers Squibb acquisition, we came to a conclusion that in order for BMS to be able to fully capitalize on our scientific innovation potential as well as to drive data-driven decisions across the company, having a robust data agenda is key. Now the question is, how do you progress that? Historically, we had approached a very decentralized mechanism that made a different data constituencies. We didn't have a formal role of a Chief Data Officer up until 2018 or so. So coming from that realization that we need to have an effective data agenda to drive forward the necessary data-driven innovations from an analytic standpoint. And equally importantly, from optimizing our execution, we came to conclusion that we need an enterprise-level data organization, we need to have a first among equals if you will, to be mandated by the CEO, his leadership team, to be the kind of an orchestrator of a data agenda for the company, because data agenda cannot be done individually by a singular CDO. It has to be done in partnership with many stakeholders, business, technology, analytics, et cetera. So from that came this notion that we need an enterprise-wide data organization. So we started there. So for awhile, I would joke around that I had all of the accountabilities of the CDO without the lofty title. So this journey started around 2016, where we create an enterprise-wide data organization. And we made a very conscious choice of separating the data organization from analytics. And the reason we did that is when we look at the bowl of Bristol-Myers Squibb, analytics for example, is core and part of our scientific discovery process, research, our clinical development, all of them have deep data science and analytic embedded in it. But we also have other analytics whether it is part of our sales and marketing, whether it is part of our finance and our enabling functions they catch all across global procurement et cetera. So the world of analytics is very broad. BMS did a separation between the world of analytics and from the world of data. Analytics at BMS is in two modes. There is a central analytics organization called Business Insights and Analytics that drive most of the enterprise-level analytics. But then we have embedded analytics in our business areas, which is research and development, manufacturing and supply chain, et cetera, to drive what needs to be closer to the business idea. And the reason for separating that out and having a separate data organization is that none of these analytic aspirations or the business aspirations from data will be met if the world of data is, you don't have the right level of data available, the velocity of data is not appropriate for the use cases, the quality of data is not great or the control of the data. So that we are using the data for the right intent, meeting the compliance and regulatory expectations around the data is met. So that's why we separated out that data world from the analytics world, which is a little bit of a unique construct for us compared to what we see generally in the world of CDOs. And from that standpoint, then the decision was taken to make that report for global CIO. At Bristol-Myers Squibb, they have a very strong CIO organization and IT organization. When I say strong, it is from this lens standpoint. A, it is centralized, we have centralized the budget as well as we have centralized the execution across the enterprise. And the CDO reporting to the CIO with that data-specific agenda, has a lot of value in being able to connect the world of data with the world of technology. So at BMS, their Chief Data Officer organization is a combination of traditional CDO-type accountabilities like data risk management, data governance, data stewardship, but also all of the related technologies around master data management, data lake, data and analytic engineering and a nascent AI data and technology lab. So that construct allows us to be a true enterprise horizontal, supporting analytics, whether it is done in a central analytics organization or embedded analytics teams in the business area, but also equally importantly, focus on the world of data from operational execution standpoint, how do we optimize data to drive operational effectiveness? So that's the construct that we have where CDO reports to the CIO, data organization separated from analytics to really focus around the availability but also the quality and control of data. And the last nuance that is that at BMS, the Chief Data Officer organization is also accountable to be the Data Protection Office. So we orchestrate and facilitate all privacy-related actions across because that allows us to make sure that all personal data that is collected, managed and consumed, meets all of the various privacy standards across the world, as well as our own commitments as a company from across from compliance principles standpoint. >> So that makes a lot of sense to me and thank you for that description. You're not getting in the way of R&D and the scientists, they know data science, they don't need really your help. I mean, they need to innovate at their own pace, but the balance of the business really does need your innovation, and that's really where it seems like you're focused. You mentioned master data management, data lakes, data engineering, et cetera. So your responsibility is for that enterprise data lifecycle to support the business side of things, and I wonder if you could talk a little bit about that and how that's evolved. I mean a lot has changed from the old days of data warehouse and cumbersome ETL and you mentioned, as you say data lakes, many of those have been challenging, expensive, slow, but now we're entering this era of cloud, real-time, a lot of machine intelligence, and I wonder if you could talk about the changes there and how you're looking at and thinking about the data lifecycle and accelerating the time to insights. >> Yeah, I think the way we think about it, we as an organization in our strategy and tactics, think of this as a data supply chain. The supply chain of data to drive business value whether it is through insights and analytics or through operation execution. When you think about it from that standpoint, then we need to get many elements of that into an effective stage. This could be the technologies that is part of that data supply chain, you reference some of them, the master data management platforms, data lake platforms, the analytics and reporting capabilities and business intelligence capabilities that plug into a data backbone, which is that I would say the technology, swim lane that needs to get right. Along with that, what we also need to get right for that effective data supply chain is that data layer. That is, how do you make sure that there is the right data navigation capability, probably you make sure that we have the right ontology mapping and the understanding around the data. How do we have data navigation? It is something that we have invested very heavily in. So imagine a new employee joining BMS, any organization our size has a pretty wide technology ecosystem and data ecosystem. How do you navigate that, how do we find the data? Data discovery has been a key focus for us. So for an effective data supply chain, then we knew that and we have instituted our roadmap to make sure that we have a robust technology orchestration of it, but equally important is an effective data operations orchestration. Both needs to go hand in hand for us to be able to make sure that that supply chain is effective from a business use case and analytic use standpoint. So that has led us on a journey from a cloud perspective, since you refer that in your question, is we have invested very heavily to move from very disparate set of data ecosystems to a more converse cloud-based data backbone. That has been a big focus at the BMS since 2016, whether it is from a research and development standpoint or from commercialization, it is our word for the sales and marketing or manufacturing and supply chain and HR, et cetera. How do we create a converged data backbone that allows us to use that data as a resource to drive many different consumption patterns? Because when you imagine an enterprise of our size, we have many different consumers of the data. So those consumers have different consumption needs. You have deep data science population who just needs access to the data and they have data science platforms but they are at once programmers as well, to the other end of the spectrum where executives need pre-packaged KPIs. So the effective orchestration of the data ecosystem at BMS through a data supply chain and the data backbone, there's a couple of things for us. One, it drives productivity of our data consumers, the scientific researchers, analytic community or other operational staff. And second, in a world where we need to make sure that the data consumption appalls ethical standards as well as privacy and other regulatory expectations, we are able to build it into our system and process the necessary controls to make sure that the consumption and the use of data meets our highest trust advancements standards. >> That makes a lot of sense. I mean, converging your data like that, people always talk about stove pipes. I know it's kind of a bromide but it's true, and allows you to sort of inject consistent policies. What about automation? How has that affected your data pipeline recently and on your journey with things like data classification and the like? >> I think in pursuing a broad data automation journey, one of the things that we did was to operate at two different speed points. In a historically, the data organizations have been bundled with long-running data infrastructure programs. By the time you complete them, their business context have moved on and the organization leaders are also exhausted from having to wait from these massive programs to reach its full potential. So what we did very intentionally from our data automation journey is to organize ourselves in two speed dimensions. First, a concept called Rapid Data Lab. The idea is that recognizing the reality that the data is not well automated and orchestrated today, we need a SWAT team of data engineers, data SMEs to partner with consumers of data to make sure that we can make effective data supply chain decisions here and now, and enable the business to answer questions of today. Simultaneously in a longer time horizon, we need to do the necessary work of moving the data automation to a better footprint. So enterprise data lake investments, where we built services based on, we had chosen AWS as the cloud backbone for data. So how do we use the AWS services? How do we wrap around it with the necessary capabilities so that we have a consistent reference and technical architecture to drive the many different function journeys? So we organized ourselves into speed dimensions; the Rapid Data Lab teams focus around partnering with the consumers of data to help them with data automation needs here and now, and then a secondary team focused around the convergence of data into a better cloud-based data backbone. So that allowed us to one, make an impact here and now and deliver value from data to the dismiss here and now. Secondly, we also learned a lot from actually partnering with consumers of data on what needs to get adjusted over a period of time in our automation journey. >> It makes sense, I mean again, that whole notion of converged data, putting data at the core of your business, you brought up AWS, I wonder if I could ask you a question. You don't have to comment on specific vendors, but there's a conversation we have in our community. You have AWS huge platform, tons of partners, a lot of innovation going on and you see innovation in areas like the cloud data warehouse or data science tooling, et cetera, all components of that data pipeline. As well, you have AWS with its own tooling around there. So a question we often have in the community is will technologists and technology buyers go for kind of best of breed and cobble together different services or would they prefer to have sort of the convenience of a bundled service from an AWS or a Microsoft or Google, or maybe they even go best of breeds for all cloud. Can you comment on that, what's your thinking? >> I think, especially for organizations, our size and breadth, having a converged to convenient, all of the above from a single provider does not seem practical and feasible, because a couple of reasons. One, the heterogeneity of the data, the heterogeneity of consumption of the data and we are yet to find a single stack provider who can meet all of the different needs. So I am more in the best of breed camp with a few caveats, a hybrid best of breed, if you will. It is important to have a converged the data backbone for the enterprise. And so whether you invest in a singular cloud or private cloud or a combination, you need to have a clear intention strategy around where are you going to host the data and how is the data is going to be organized. But you could have a lot more flexibility in the consumption of data. So once you have the data converged into, in our case, we converged on AWS-based backbone. We allow many different consumptions of the data, because I think the analytic and insights layer, data science community within R&D is different from a data science community in the supply chain context, we have business intelligence needs, we have a catered needs and then there are other data needs that needs to be funneled into software as service platforms like the sales forces of the world, to be able to drive operational execution as well. So when you look at it from that context, having a hybrid model of best of breed, whether you have a lot more convergence from a data backbone standpoint, but then allow for best of breed from an analytic and consumption of data is more where my heart and my brain is. >> I know a lot of companies would be excited to hear that answer, but I love it because it fosters competition and innovation. I wish I could talk for you forever, but you made me think of another question which is around self-serve. On your journey, are you at the point where you can deliver self-serve to the lines of business? Is that something that you're trying to get to? >> Yeah, I think it does. The self-serve is an absolutely important point because I think the traditional boundaries of what you consider the classical IT versus a classical business is great. I think there is an important gray area in the middle where you have a deep citizen data scientist in the business community who really needs to be able to have access to the data and I have advanced data science and programming skills. So self-serve is important but in that, companies need to be very intentional and very conscious of making sure that you're allowing that self-serve in a safe containment sock. Because at the end of the day, whether it is a cyber risk or data risk or technology risk, it's all real. So we need to have a balanced approach between promoting whether you call it data democratization or whether you call it self-serve, but you need to balance that with making sure that you're meeting the right risk mitigation strategy standpoint. So that's how then our focus is to say, how do we promote self-serve for the communities that they need self-serve, where they have deeper levels of access? How do we set up the right safe zones for those which may be the appropriate mitigation from a cyber risk or data risk or technology risk. >> Security pieces, again, you keep bringing up topics that I could talk to you forever on, but I heard on TV the other night, I heard somebody talking about how COVID has affected, because of remote access, affected security. And it's like hey, give everybody access. That was sort of the initial knee-jerk response, but the example they gave as well, if your parents go out of town and the kid has a party, you may have some people show up that you don't want to show up. And so, same issue with remote working, work from home. Clearly you guys have had to pivot to support that, but where does the security organization fit? Does that report separate alongside the CIO? Does it report into the CIO? Are they sort of peers of yours, how does that all work? >> Yeah, I think at Bristol-Myers Squibb, we have a Chief Information Security Officer who is a peer of mine, who also reports to the global CIO. The CDO and the CSO are effective partners and are two sides of the coin and trying to advance a total risk mitigation strategy, whether it is from a cyber risk standpoint, which is the focus of the Chief Information Security Officer and whether it is the general data consumption risk. And that is the focus from a Chief Data Officer in the capacities that I have. And together, those are two sides of a coin that the CIO needs to be accountable for. So I think that's how we have orchestrated it, because I think it is important in these worlds where you want to be able to drive data-driven innovation but you want to be able to do that in a way that doesn't open the company to unwanted risk exposures as well. And that is always a delicate balancing act, because if you index too much on risk and then high levels of security and control, then you could lose productivity. But if you index too much on productivity, collaboration and open access and data, it opens up the company for risks. So it is a delicate balance within the two. >> Increasingly, we're seeing that reporting structure evolve and coalesce, I think it makes a lot of sense. I felt like at some point you had too many seats at the executive leadership table, too many kind of competing agendas. And now your structure, the CIO is obviously a very important position. I'm sure has a seat at the leadership table, but also has the responsibility for managing that sort of data as an asset versus a liability which my view, has always been sort of the role of the Head of Information. I want to ask you, I want to hit the Escape key a little bit and ask you about data as a resource. You hear a lot of people talk about data is the new oil. We often say data is more valuable than oil because you can use it, it doesn't follow the laws of scarcity. You could use data in infinite number of places. You can only put oil in your car or your house. How do you think about data as a resource today and going forward? >> Yeah, I think the data as the new oil paradigm in my opinion, was an unhealthy, and it prompts different types of conversations around that. I think for certain companies, data is indeed an asset. If you're a company that is focused on information products and data products and that is core of your business, then of course there's monetization of data and then data as an asset, just like any other assets on the company's balance sheet. But for many enterprises to further their mission, I think considering data as a resource, I think is a better focus. So as a vital resource for the company, you need to make sure that there is an appropriate caring and feeding for it, there is an appropriate management of the resource and an appropriate evolution of the resource. So that's how I would like to consider it, it is a personal end of one perspective, that data as a resource that can power the mission of the company, the new products and services, I think that's a good, healthy way to look at it. At the center of it though, a lot of strategies, whether people talk about a digital strategy, whether the people talk about data strategy, what is important is a company to have a pool north star around what is the core mission of the company and what is the core strategy of the company. For Bristol-Myers Squibb, we are about transforming patients' lives through science. And we think about digital and data as key value levers and drivers of that strategy. So digital for the sake of digital or data strategy for the sake of data strategy is meaningless in my opinion. We are focused on making sure that how do we make sure that data and digital is an accelerant and has a value lever for the company's mission and company strategy. So that's why thinking about data as a resource, as a key resource for our scientific researchers or a key resource for our manufacturing team or a key resource for our sales and marketing, allows us to think about the actions and the strategies and tactics we need to deploy to make that effective. >> Yeah, that makes a lot of sense, you're constantly using that North star as your guideline and how data contributes to that mission. Krishna Cheriath, thanks so much for coming on the Cube and supporting the MIT Chief Data Officer community, it was a really pleasure having you. >> Thank you so much for Dave, hopefully you and the audience is safe and healthy during these times. >> Thank you for that and thank you for watching everybody. This is Vellante for the Cube's coverage of the MIT CDOIQ Conference 2020 gone virtual. Keep it right there, we'll right back right after this short break. (lively upbeat music)

(techno music) >> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, we're here with theCUBE at the MIT Chief Data Officer Information Quality Conference. I'm Dave Vellante with my co-host Paul Gillin. Susan Wilson is here, she's the vice president of data governance and she's the leader at Informatica. Blake Anders is the corporate vice president of data governance at New York Life. Folks, welcome to theCUBE, thanks for coming on. >> Thank you. >> Thank you. >> So, Susan, interesting title; VP, data governance leader, Informatica. So, what are you leading at Informatica? >> We're helping our customers realize their business outcomes and objectives. Prior to joining Informatica about 7 years ago, I was actually a customer myself, and so often times I'm working with our customers to understand where they are, where they going, and how to best help them; because we recognize data governance is more than just a tool, it's a capability that represents people, the processes, the culture, as well as the technology. >> Yeah so you've walked the walk, and you can empathize with what your customers are going through. And Blake, your role, as the corporate VP, but more specifically the data governance lead. >> Right, so I lead the data governance capabilities and execution group at New York Life. We're focused on providing skills and tools that enable government's activities across the enterprise at the company. >> How long has that function been in place? >> We've been in place for about two and half years now. >> So, I don't know if you guys heard Mark Ramsey this morning, the key-note, but basically he said, okay, we started with enterprise data warehouse, we went to master data management, then we kind of did this top-down enterprise data model; that all failed. So we said, all right, let's pump the governance. Here you go guys, you fix our corporate data problem. Now, right tool for the right job but, and so, we were sort of joking, did data governance fail? No, you always have to have data governance. It's like brushing your teeth. But so, like I said, I don't know if you heard that, but what are your thoughts on that sort of evolution that he described? As sort of, failures of things like EDW to live up to expectations and then, okay guys over to you. Is that a common theme? >> It is a common theme, and what we're finding with many of our customers is that they had tried many of the, if you will, the methodologies around data governance, right? Around policies and structures. And we describe this as the Data 1.0 journey, which was more application-centric reporting to Data 2.0 to data warehousing. And a lot of the failed attempts, if you will, at centralizing, if you will, all of your data, to now Data 3.0, where we look at the explosion of data, the volumes of data, the number of data consumers, the expectations of the chief data officer to solve business outcomes; crushing under the scale of, I can't fit all of this into a centralized data at repository, I need something that will help me scale and to become more agile. And so, that message does resonate with us, but we're not saying data warehouses don't exist. They absolutely do for trusted data sources, but the ability to be agile and to address many of your organizations needs and to be able to service multiple consumers is top-of-mind for many of our customers. >> And the mind set from 1.0 to 2.0 to 3.0 has changed. From, you know, data as a liability, to now data as this massive asset. It's sort of-- >> Value, yeah. >> Yeah, and the pendulum is swung. It's almost like a see-saw. Where, and I'm not sure it's ever going to flip back, but it is to a certain extent; people are starting to realize, wow, we have to be careful about what we do with our data. But still, it's go, go, go. But, what's the experience at New York Life? I mean, you know. A company that's been around for a long time, conservative, wants to make sure risk averse, obviously. >> Right. >> But at the same time, you want to keep moving as the market moves. >> Right, and we look at data governance as really an enabler and a value-add activity. We're not a governance practice for the sake of governance. We're not there to create a lot of policies and restrictions. We're there to add value and to enable innovation in our business and really drive that execution, that efficiency. >> So how do you do that? Square that circle for me, because a lot of people think, when people think security and governance and compliance they think, oh, that stifles innovation. How do you make governance an engine of innovation? >> You provide transparency around your data. So, it's transparency around, what does the data mean? What data assets do we have? Where can I find that? Where are my most trusted sources of data? What does the quality of that data look like? So all those things together really enable your data consumers to take that information and create new value for the company. So it's really about enabling your value creators throughout the organization. >> So data is an ingredient. I can tell you where it is, I can give you some kind of rating as to the quality of that data and it's usefulness. And then you can take it and do what you need to do with it in your specific line of business. >> That's right. >> Now you said you've been at this two and half years, so what stages have you gone through since you first began the data governance initiative. >> Sure, so our first year, year and half was really focused on building the foundations, establishing the playbook for data governance and building our processes and understanding how data governance needed to be implemented to fit New York Life in the culture of the company. The last twelve months or so has really been focused on operationalizing governance. So we've got the foundations in place, now it's about implementing tools to further augment those capabilities and help assist our data stewards and give them a better skill set and a better tool set to do their jobs. >> Are you, sort of, crowdsourcing the process? I mean, you have a defined set of people who are responsible for governance, or is everyone taking a role? >> So, it is a two-pronged approach, we do have dedicated data stewards. There's approximately 15 across various lines of business throughout the company. But, we are building towards a data democratization aspect. So, we want people to be self-sufficient in finding the data that they need and understanding the data. And then, when they have questions, relying on our stewards as a network of subject matter experts who also have some authorizations to make changes and adapt the data as needed. >> Susan, one of the challenges that we see is that the chief data officers often times are not involved in some of these skunkworks AI projects. They're sort of either hidden, maybe not even hidden, but they're in the line of business, they're moving. You know, there's a mentality of move fast and break things. The challenge with AI is, if you start operationalizing AI and you're breaking things without data quality, without data governance, you can really affect lives. We've seen it. In one of these unintended consequences. I mean, Facebook is the obvious example and there are many, many others. But, are you seeing that? How are you seeing organizations dealing with that problem? >> As Blake was mentioning often times what it is about, you've got to start with transparency, and you got to start with collaborating across your lines of businesses, including the data scientists, and including in terms of what they are doing. And actually provide that level of transparency, provide a level of collaboration. And a lot of that is through the use of our technology enablers to basically go out and find where the data is and what people are using and to be able to provide a mechanism for them to collaborate in terms of, hey, how do I get access to that? I didn't realize you were the SME for that particular component. And then also, did you realize that there is a policy associated to the data that you're managing and it can't be shared externally or with certain consumer data sets. So, the objective really is around how to create a platform to ensure that any one in your organization, whether I'm in the line of business, that I don't have a technical background, or someone who does have a technical background, they can come and access and understand that information and connect with their peers. >> So you're helping them to discover the data. What do you do at that stage? >> What we do at that stage is, creating insights for anyone in the organization to understand it from an impact analysis perspective. So, for example, if I'm going to make changes, to as well as discovery. Where exactly is my information? And so we have-- >> Right. How do you help your customers discover that data? >> Through machine learning and artificial intelligence capabilities of our, specifically, our data catalog, that allows us to do that. So we use such things like similarity based matching which help us to identify. It doesn't have to be named, in miscellaneous text one, it could be named in that particular column name. But, in our ability to scan and discover we can identify in that column what is potentially social security number. It might have resided over years of having this data, but you may not realize that it's still stored there. Our ability to identify that and report that out to the data stewards as well as the data analysts, as well as to the privacy individuals is critical. So, with that being said, then they can actually identify the appropriate policies that need to be adhered to, alongside with it in terms of quality, in terms of, is there something that we need to archive. So that's where we're helping our customers in that aspect. >> So you can infer from the data, the meta data, and then, with a fair degree of accuracy, categorize it and automate that. >> Exactly. We've got a customer that actually ran this and they said that, you know, we took three people, three months to actually physically tag where all this information existed across something like 7,000 critical data elements. And, basically, after the set up and the scanning procedures, within seconds we were able to get within 90% precision. Because, again, we've dealt a lot with meta data. It's core to our artificial intelligence and machine learning. And it's core to how we built out our platforms to share that meta data, to do something with that meta data. It's not just about sharing the glossary and the definition information. We also want to automate and reduce the manual burden. Because we recognize with that scale, manual documentation, manual cataloging and tagging just, >> It doesn't work. >> It doesn't work. It doesn't scale. >> Humans are bad at it. >> They're horrible at it. >> So I presume you have a chief data officer at New York Life, is that correct? >> We have a chief data and analytics officer, yes. >> Okay, and you work within that group? >> Yes, that is correct. >> Do you report it to that? >> Yes, so-- >> And that individual, yeah, describe the organization. >> So that sits in our lines of business. Originally, our data governance office sat in technology. And then, our early 2018 we actually re-orged into the business under the chief data and analytics officer when that role was formed. So we sit under that group along with a data solutions and governance team that includes several of our data stewards and also some others, some data engineer-type roles. And then, our center for data science and analytics as well that contains a lot of our data science teams in that type of work. >> So in thinking about some of these, I was describing to Susan, as these skunkworks projects, is the data team, the chief data officer's team involved in those projects or is it sort of a, go run water through the pipes, get an MVP and then you guys come in. How does that all work? >> We're working to try to centralize that function as much as we can, because we do believe there's value in the left hand knowing what the right hand is doing in those types of things. So we're trying to build those communications channels and build that network of data consumers across the organization. >> It's hard right? >> It is. >> Because the line of business wants to move fast, and you're saying, hey, we can help. And they think you're going to slow them down, but in fact, you got to make the case and show the success because you're actually not going to slow them down to terms of the ultimate outcome. I think that's the case that you're trying to make, right? >> And that's one of the things that we try to really focus on and I think that's one of the advantages to us being embedded in the business under the CDAO role, is that we can then say our objectives are your objectives. We are here to add value and to align with what you're working on. We're not trying to slow you down or hinder you, we're really trying to bring more to the table and augment what you're already trying to achieve. >> Sometimes getting that organization right means everything, as we've seen. >> Absolutely. >> That's right. >> How are you applying governance discipline to unstructured data? >> That's actually something that's a little bit further down our road map, but one of the things that we have started doing is looking at our taxonomy's for structured data and aligning those with the taxonomy's that we're using to classify unstructured data. So, that's something we're in the early stages with, so that when we get to that process of looking at more of our unstructured content, we can, we already have a good feel for there's alignment between the way that we think about and organize those concepts. >> Have you identified automation tools that can help to bring structure to that unstructured data? >> Yes, we have. And there are several tools out there that we're continuing to investigate and look at. But, that's one of the key things that we're trying to achieve through this process is bringing structure to unstructured content. >> So, the conference. First year at the conference. >> Yes. >> Kind of key take aways, things that interesting to you, learnings? >> Oh, yes, well the number of CDO's that are here and what's top of mind for them. I mean, it ranges from, how do I stand up my operating model? We just had a session just about 30 minutes ago. A lot of questions around, how do I set up my organization structure? How do I stand up my operating model so that I could be flexible? To, right, the data scientists, to the folks that are more traditional in structured and trusted data. So, still these things are top-of-mind and because they're recognizing the market is also changing too. And the growing amount of expectations, not only solving business outcomes, but also regulatory compliance, privacy is also top-of-mind for a lot of customers. In terms of, how would I get started? And what's the appropriate structure and mechanism for doing so? So we're getting a lot of those types of questions as well. So, the good thing is many of us have had years of experience in this phase and the convergence of us being able to support our customers, not only in our principles around how we implement the framework, but also the technology is really coming together very nicely. >> Anything you'd add, Blake? >> I think it's really impressive to see the level of engagement with thought leaders and decision makers in the data space. You know, as Susan mentioned, we just got out of our session and really, by the end of it, it turned into more of an open discussion. There was just this kind of back and forth between the participants. And so it's really engaging to see that level of passion from such a distinguished group of individuals who are all kind of here to share thoughts and ideas. >> Well anytime you come to a conference, it's sort of any open forum like this, you learn a lot. When you're at MIT, it's like super-charged. With the big brains. >> Exactly, you feel it when you come on the campus. >> You feel smarter when you walk out of here. >> Exactly, I know. >> Well, guys, thanks so much for coming to theCUBE. It was great to have you. >> Thank you for having us. We appreciate it, thank you. >> You're welcome. All right, keep it right there everybody. Paul and I will be back with our next guest. You're watching theCUBE from MIT in Cambridge. We'll be right back. (techno music)

(techno music) >> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to MIT CDOIQ. The CDO Information Quality Conference. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. I'm here with my co-host, Paul Gillin. This is our day two of our two day coverage. Jean Ross is here. She's the principle research scientist at MIT CISR, Jean good to see you again. >> Nice to be here! >> Welcome back. Okay, what do all these acronyms stand for, I forget. MIT CISR. >> CISR which we pronounce scissor, is the Center for Information Systems Research. It's a research center that's been at MIT since 1974, studying how big companies use technology effectively. >> So and, what's your role as a research scientist? >> As a research scientist, I work with both researchers and with company leaders to understand what's going on out there, and try to present some simple succinct ideas about how companies can generate greater value from information technology. >> Well, I guess not much has changed in information technology since 1974. (laughing) So let's fast forward to the big, hot trend, digital transformation, digital business. What's the difference between a business and a digital business? >> Right now, you're hoping there's no difference for you and your business. >> (chuckling) Yeah, for sure. >> The main thing about a digital business is it's being inspired by technology. So in the past, we would establish a strategy, and then we would check out technology and say, okay, how can technology make us more effective with that strategy? Today, and this has been driven a lot by start-ups, we have to stop and say, well wait a minute, what is technology making possible? Because if we're not thinking about it, there sure are a lot of students at MIT who are, and we're going to miss the boat. We're going to get Ubered if you will, somebody's going to think of a value proposition that we should be offering and aren't, and we'll be left in the dust. So, our digital businesses are those that are recognizing the opportunities that digital technologies make possible. >> Now, and what about data? In terms of the role of digital business, it seems like that's an underpinning of a digital business. Is it not? >> Yeah, the single biggest capability that digital technologies provide, is ubiquitous data that's readily accessible anytime. So when we think about being inspired by technology, we could reframe that as inspired by the availability of ubiquitous data that's readily accessible. >> Your premise about the difference between digitization and digital business is interesting. It's more than just a sematic debate. Do companies now, when companies talk about digital transformation these days, in fact, are most of them of thinking of digitization rather than really transformative business change? >> Yeah, this is so interesting to me. In 2006, we wrote a book that said, you need to become more agile, and you need to rely on information technology to get you there. And these are basic things like SAP and salesforce.com and things like that. Just making sure that your core processes are disciplined and reliable and predictable. We said this in 2006. What we didn't know is that we were explaining digitization, which is very effective use of technology in your underlying process. Today, when somebody says to me, we're going digital, I'm thinking about the new value propositions, the implications of the data, right? And they're often actually saying they're finally doing what we thought they should do in 2006. The problem is, in 2006, we said get going on this, it's a long journey. This could take you six, 10 years to accomplish. And then we gave examples of companies that took six to 10 years. LEGO, and USAA and really great companies. And now, companies are going, "Ah, you know, we really ought to do that". They don't have six to 10 years. They get this done now, or they're in trouble, and it's still a really big deal. >> So how realistic is it? I mean, you've got big established companies that have got all these information silos, as we've been hearing for the last two days, just pulling their information together, knowing what they've got is a huge challenge for them. Meanwhile, you're competing with born on the web, digitally native start-ups that don't have any of that legacy, is it really feasible for these companies to reinvent themselves in the way you're talking about? Or should they just be buying the companies that have already done it? >> Well good luck with buying, because what happens is that when a company starts up, they can do anything, but they can't do it to scale. So most of these start-ups are going to have to sell themselves because they don't know anything about scale. And the problem is, the companies that want to buy them up know about the scale of big global companies but they don't know how to do this seamlessly because they didn't do the basic digitization. They relied on basically, a lot of heroes in their company to pull of the scale. So now they have to rely more on technology than they did in the past, but they still have a leg up if you will, on the start-up that doesn't want to worry about the discipline of scaling up a good idea. They'd rather just go off and have another good idea, right? They're perpetual entrepreneurs if you will. So if we look at the start-ups, they're not really your concern. Your concern is the very well run company, that's been around, knows how to be inspired by technology and now says, "Oh I see what you're capable of doing, "or should be capable of doing. "I think I'll move into your space". So this, the Amazon's, and the USAA's and the LEGO's who say "We're good at what we do, "and we could be doing more". We're watching Schneider Electric, Phillips's, Ferovial. These are big ole companies who get digital, and they are going to start moving into a lot of people's territory. >> So let's take the example of those incumbents that you've used as examples of companies that are leaning into digital, and presumably doing a good job of it, they've got a lot of legacy debt, as you know people call it technical debt. The question I have is how they're using machine intelligence. So if you think about Facebook, Amazon, Microsoft, Google, they own horizontal technologies around machine intelligence. The incumbents that you mentioned, do not. Now do they close the gap? They're not going to build their own A.I. They're going to buy it, and then apply it. It's how they apply it that's going to be the difference. So do you agree with that premise, and where are they getting it, do they have the skill sets to do it, how are they closing that gap? >> They're definitely partnering. When you say they're not going to build any of it, that's actually not quite true. They're going to build a lot around the edges. They'll rely on partners like Microsoft and Google to provide some of the core, >> Yes, right. >> But they are bringing in their own experts to take it to the, basically to the customer level. How do I take, let me just take Schneider Electric for an example. They have gone from being an electrical equipment manufacturer, to a purveyor of energy management solutions. It's quite a different value proposition. To do that, they need a lot of intelligence. Some of it is data analytics of old, and some of it is just better representation on dashboards and things like that. But there is a layer of intelligence that is new, and it is absolutely essential to them by relying on partners and their own expertise in what they do for customers, and then co-creating a fair amount with customers, they can do things that other companies cannot. >> And they're developing a software presumably, a SAS revenue stream as part of that, right? >> Yeah, absolutely. >> How about the innovators dilemma though, the problem that these companies often have grown up, they're very big, they're very profitable, they see disruption coming, but they are unable to make the change, their shareholders won't let them make the change, they know what they have to do, but they're simply not able to do it, and then they become paralyzed. Is there a -- I mean, looking at some of the companies you just mentioned, how did they get over that mindset? >> This is real leadership from CEO's, who basically explain to their boards and to their investors, this is our future, we are... we're either going this direction or we're going down. And they sell it. It's brilliant salesmanship, and it's why when we go out to study great companies, we don't have that many to choose from. I mean, they are hard to find, right? So you are at such a competitive advantage right now. If you understand, if your own internal processes are cleaned up and you know how to rely on the E.R.P's and the C.R.M's, to get that done, and on the other hand, you're using the intelligence to provide value propositions, that new technologies and data make possible, that is an incredibly powerful combination, but you have to invest. You have to convince your boards and your investors that it's a good idea, you have to change your talent internally, and the biggest surprise is, you have to convince your customers that they want something from you that they never wanted before. So you got a lot of work to do to pull this off. >> Right now, in today's economy, the economy is sort of lifting all boats. But as we saw when the .com implosion happened in 2001, often these breakdown gives birth to great, new companies. Do you see that the next recession, which is inevitably coming, will be sort of the turning point for some of these companies that can't change? >> It's a really good question. I do expect that there are going to be companies that don't make it. And I think that they will fail at different rates based on their, not just the economy, but their industry, and what competitors do, and things like that. But I do think we're going to see some companies fail. We're going to see many other companies understand that they are too complex. They are simply too complex. They cannot do things end to end and seamlessly and present a great customer experience, because they're doing everything. So we're going to see some pretty dramatic changes, we're going to see failure, it's a fair assumption that when we see the economy crash, it's also going to contribute, but that's, it's not the whole story. >> But when the .com blew up, you had the internet guys that actually had a business model to make money, and the guys that didn't, the guys that didn't went away, and then you also had the incumbents that embrace the internet, so when we came out of that .com downturn, you had the survivors, who was Google and eBay, and obviously Amazon, and then you had incumbent companies who had online retailing, and e-tailing and e-commerce etc, who thrived. I would suspect you're going to see something similar, but I wonder what you guys think. The street today is rewarding growth. And we got another near record high today after the rate cut yesterday. And so, but companies that aren't making money are getting rewarded, 'cause they're growing. Well when the recession comes, those guys are going to get crushed. >> Right. >> Yeah. >> And you're going to have these other companies emerge, and you'll see the winners, are going to be those ones who have truly digitized, not just talking the talk, or transformed really, to use your definition. That's what I would expect. I don't know, what do you think about that? >> I totally agree. And, I mean, we look at industries like retail, and they have been fundamentally transformed. There's still lots of opportunities for innovation, and we're going to see some winners that have kind of struggled early but not given up, and they're kind of finding their footing. But we're losing some. We're losing a lot, right? I think the surprise is that we thought digital was going to replace what we did. We'd stop going to stores, we'd stop reading books, we wouldn't have newspapers anymore. And it hasn't done that. Its only added, it hasn't taken anything away. >> It could-- >> I don't think the newspaper industry has been unscathed by digital. >> No, nor has retail. >> Nor has retail, right. >> No, no no, not unscathed, but here's the big challenge. Is if I could substitute, If I could move from newspaper to online, I'm fine. You don't get to do that. You add online to what you've got, right? And I think this right now is the big challenge. Is that nothing's gone away, at least yet. So we have to sustain the business we are, so that it can feed the business we want to be. And we have to make that transition into new capabilities. I would argue that established companies need to become very binary, that there are people that do nothing but sustain and make better and better and better, who they are. While others, are creating the new reality. You see this in auto companies by the way. They're creating not just the autonomous automobiles, but the mobility services, the whole new value propositions, that will become a bigger and bigger part of their revenue stream, but right now are tiny. >> So, here's the scary thing to me. And again, I'd love to hear your thoughts on this. And I've been an outspoken critic of Liz Warren's attack on big tech. >> Absolutely. >> I just think if they're breaking the law, and they're really acting like monopolies, the D.O.J and F.T.C should do something, but to me, you don't just break up big tech because they're good capitalists. Having said that, one of the things that scares me is, when you see Apple getting into payment systems, Amazon getting into grocery and logistics. Digital allows you to do something that's never happened before which is, you can traverse industries. >> Yep. >> Yeah, absolutely >> You used to have this stack of industries, and if you were in that industry, you're stuck in healthcare, you're stuck in financial services or whatever it was. And today, digital allows you to traverse those. >> It absolutely does. And so in theory, Amazon and Apple and Facebook and Google, they can attack virtually any industry and they kind of are. >> Yeah they kind are. I would certainly not break up anything. I would really look hard though at acquisitions, because I think that's where some of this is coming from. They can stop the overwhelming growth, but I do think you're right. That you get these opportunities from digital that are just so much easier because they're basically sharing information and technology, not building buildings and equipment and all that kind of thing. But I think there all limits to all this. I do not fear these companies. I think there, we need some law, we need some regulations, they're fine. They are adding a lot of value and the great companies, I mean, you look at the Schneider's and the Phillips, yeah they fear what some of them can do, but they're looking forward to what they provide underneath. >> Doesn't Cloud change the equation here? I mean, when you think of something like Amazon getting into the payments business, or Google in the payments business, you know it used to be that the creating of global payments processing network, just going global was a huge barrier to entry. Now, you don't have nearly that same level of impediment right? I mean the cloud eliminates much of the traditional barrier. >> Yeah, but I'll tell you what limits it, is complexity. Every company we've studied gets a little over anxious and becomes too complex, and they cannot run themselves effectively anymore. It happens to everyone. I mean, remember when we were terrified about what Microsoft was going to become? But then it got competition because it's trying to do so many things, and somebody else is offering, Sales Force and others, something simpler. And this will happen to every company that gets overly ambitious. Something simpler will come along, and everybody will go "Oh thank goodness". Something simpler. >> Well with Microsoft, I would argue two things. One is the D.O.J put some handcuffs on them , and two, with Steve Ballmer, I wouldn't get his nose out of Windows, and then finally stuck on a (mumbles) (laughter) >> Well it's they had a platform shift. >> Well this is exactly it. They will make those kind of calls . >> Sure, and I think that talks to their legacy, that they won't end up like Digital Equipment Corp or Wang and D.G, who just ignored the future and held onto the past. But I think, a colleague of ours, David Moschella wrote a book, it's called "Seeing Digital". And his premise was we're moving from a world of remote cloud services, to one where you have to, to use your word, ubiquitous digital services that you can access upon which you can build your business and new business models. I mean, the simplest example is Waves, you mentioned Uber. They're using Cloud, they're using OAuth.in with Google, Facebook or LinkedIn and they've got a security layer, there's an A.I layer, there's all your BlockChain, mobile, cognitive, it's all these sets of services that are now ubiquitous on which you're building, so you're leveraging, he calls it the matrix, to the extent that these companies that you're studying, these incumbents can leverage that matrix, they should be fine. >> Yes. >> The part of the problem is, they say "No, we're going to invent everything ourselves, we're going to build it all ourselves". To use Andy Jassy's term, it's non-differentiated heavy lifting, slows them down, but there's no reason why they can't tap that matrix, >> Absolutely >> And take advantage of it. Where I do get scared is, the Facebooks, Apples, Googles, Amazons, they're matrix companies, their data is at their core, and they get this. It's not like they're putting data around the core, data is the core. So your thoughts on that? I mean, it looks like your slide about disruption, it's coming. >> Yeah, yeah, yeah, yeah. >> No industry is safe. >> Yeah, well I'll go back to the complexity argument. We studied complexity at length, and complexity is a killer. And as we get too ambitious, and we're constantly looking for growth, we start doing things that create more and more tensions in our various lines of business, causes to create silos, that then we have to coordinate. I just think every single company that, no cloud is going to save us from this. It, complexity will kill us. And we have to keep reminding ourselves to limit that complexity, and we've just not seen the example of the company that got that right. Sooner or later, they just kind of chop them, you know, create problems for themselves. >> Well isn't that inherent though in growth? >> Absolutely! >> It's just like, big companies slow down. >> That's right. >> They can't make decisions as quickly. >> That's right. >> I haven't seen a big company yet that moves nimbly. >> Exactly, and that's the complexity thing-- >> Well wait a minute, what about AWS? They're a 40 billion dollar company. >> Oh yeah, yeah, yeah >> They're like the agile gorilla. >> Yeah, yeah, yeah. >> I mean, I think they're breaking the rule, and my argument would be, because they have data at their core, and they've got that, its a bromide, but that common data model, that they can apply now to virtually any business. You know, we're been expecting, a lot of people have been expecting that growth to attenuate. I mean it hasn't yet, we'll see. But they're like a 40 billion dollar firm-- >> No that's a good example yeah. >> So we'll see. And Microsoft, is the other one. Microsoft is demonstrating double digit growth. For such a large company, it's astounding. I wonder, if the law of large numbers is being challenged, so. >> Yeah, well it's interesting. I do think that what now constitutes "so big" that you're really going to struggle with the complexity. I think that has definitely been elevated a lot. But I still think there will be a point at which human beings can't handle-- >> They're getting away. >> Whatever level of complexity we reach, yeah. >> Well sure, right because even though this great new, it's your point. Cloud technology, you know, there's going to be something better that comes along. Even, I think Jassy might have said, If we had to do it all over again, we would have built the whole thing on lambda functions >> Yeah. >> Oh, yeah. >> Not on, you know so there you go. >> So maybe someone else does that-- >> Yeah, there you go. >> So now they've got their hybrid. >> Yeah, yeah. >> Yeah, absolutely. >> You know maybe it'll take another ten years, but well Jean, thanks so much for coming to theCUBE, >> it was great to have you. >> My pleasure! >> Appreciate you coming back. >> Really fun to talk. >> All right, keep right there everybody, Paul Gillin and Dave Villante, we'll be right back from MIT CDOIQ, you're watching theCUBE. (chuckles) (techno music)

>> From Cambridge, Massachusetts, it's theCUBE covering MIT Chief Data Officer and Information Quality Symposium 2019 brought to you by SiliconANGLE Media. (upbeat techno music) >> Hi everybody, welcome back to Cambridge, Massachusetts. You're watching theCUBE, the leader in tech coverage. We go out to the events. We extract the signal from the noise, and we're here at the MIT CDOIQ Conference, Chief Data Officer Information Quality Conference. It is the 13th year here at the Tang building. We've outgrown this building and have to move next year. It's fire marshal full. Gokula Mishra is here. He is the Senior Director of Global Data and Analytics and Supply Chain-- >> Formerly. Former, former Senior Director. >> Former! I'm sorry. It's former Senior Director of Global Data Analytics and Supply Chain at McDonald's. Oh, I didn't know that. I apologize my friend. Well, welcome back to theCUBE. We met when you were at Oracle doing data. So you've left that, you're on to your next big thing. >> Yes, thinking through it. >> Fantastic, now let's start with your career. You've had, so you just recently left McDonald's. I met you when you were at Oracle, so you cut over to the dark side for a while, and then before that, I mean, you've been a practitioner all your life, so take us through sort of your background. >> Yeah, I mean my beginning was really with a company called Tata Burroughs. Those days we did not have a lot of work getting done in India. We used to send people to U.S. so I was one of the pioneers of the whole industry, coming here and working on very interesting projects. But I was lucky to be working on mostly data analytics related work, joined a great company called CS Associates. I did my Master's at Northwestern. In fact, my thesis was intelligent databases. So, building AI into the databases and from there on I have been with Booz Allen, Oracle, HP, TransUnion, I also run my own company, and Sierra Atlantic, which is part of Hitachi, and McDonald's. >> Awesome, so let's talk about use of data. It's evolved dramatically as we know. One of the themes in this conference over the years has been sort of, I said yesterday, the Chief Data Officer role emerged from the ashes of sort of governance, kind of back office information quality compliance, and then ascended with the tailwind of the Big Data meme, and it's kind of come full circle. People are realizing actually to get value out of data, you have to have information quality. So those two worlds have collided together, and you've also seen the ascendancy of the Chief Digital Officer who has really taken a front and center role in some of the more strategic and revenue generating initiatives, and in some ways the Chief Data Officer has been a supporting role to that, providing the quality, providing the compliance, the governance, and the data modeling and analytics, and a component of it. First of all, is that a fair assessment? How do you see the way in which the use of data has evolved over the last 10 years? >> So to me, primarily, the use of data was, in my mind, mostly around financial reporting. So, anything that companies needed to run their company, any metrics they needed, any data they needed. So, if you look at all the reporting that used to happen it's primarily around metrics that are financials, whether it's around finances around operations, finances around marketing effort, finances around reporting if it's a public company reporting to the market. That's where the focus was, and so therefore a lot of the data that was not needed for financial reporting was what we call nowadays dark data. This is data we collect but don't do anything with it. Then, as the capability of the computing, and the storage, and new technologies, and new techniques evolve, and are able to handle more variety and more volume of data, then people quickly realize how much potential they have in the other data outside of the financial reporting data that they can utilize too. So, some of the pioneers leverage that and actually improved a lot in their efficiency of operations, came out with innovation. You know, GE comes to mind as one of the companies that actually leverage data early on, and number of other companies. Obviously, you look at today data has been, it's defining some of the multi-billion dollar company and all they have is data. >> Well, Facebook, Google, Amazon, Microsoft. >> Exactly. >> Apple, I mean Apple obviously makes stuff, but those other companies, they're data companies. I mean largely, and those five companies have the highest market value on the U.S. stock exchange. They've surpassed all the other big leaders, even Berkshire Hathaway. >> So now, what is happening is because the market changes, the forces that are changing the behavior of our consumers and customers, which I talked about which is everyone now is digitally engaging with each other. What that does is all the experiences now are being captured digitally, all the services are being captured digitally, all the products are creating a lot of digital exhaust of data and so now companies have to pay attention to engage with their customers and partners digitally. Therefore, they have to make sure that they're leveraging data and analytics in doing so. The other thing that has changed is the time to decision to the time to act on the data inside that you get is shrinking, and shrinking, and shrinking, so a lot more decision-making is now going real time. Therefore, you have a situation now, you have the capability, you have the technology, you have the data now, you have to make sure that you convert that in what I call programmatic kind of data decision-making. Obviously, there are people involved in more strategic decision-making. So, that's more manual, but at the operational level, it's going more programmatic decision-making. >> Okay, I want to talk, By the way, I've seen a stat, I don't know if you can confirm this, that 80% of the data that's out there today is dark data or it's data that's behind a firewall or not searchable, not open to Google's crawlers. So, there's a lot of value there-- >> So, I would say that percent is declining over time as companies have realized the value of data. So, more and more companies are removing the silos, bringing those dark data out. I think the key to that is companies being able to value their data, and as soon as they are able to value their data, they are able to leverage a lot of the data. I still believe there's a large percent still not used or accessed in companies. >> Well, and of course you talked a lot about data monetization. Doug Laney, who's an expert in that topic, we had Doug on a couple years ago when he, just after, he wrote Infonomics. He was on yesterday. He's got a very detailed prescription as to, he makes strong cases as to why data should be valued like an asset. I don't think anybody really disagrees with that, but then he gave kind of a how-to-do-it, which will, somewhat, make your eyes bleed, but it was really well thought out, as you know. But you talked a lot about data monetization, you talked about a number of ways in which data can contribute to monetization. Revenue, cost reduction, efficiency, risk, and innovation. Revenue and cost is obvious. I mean, that's where the starting point is. Efficiency is interesting. I look at efficiency as kind of a doing more with less but it's sort of a cost reduction, but explain why it's not in the cost bucket, it's different. >> So, it is first starts with doing what we do today cheaper, better, faster, and doing more comes after that because if you don't understand, and data is the way to understand how your current processes work, you will not take the first step. So, to take the first step is to understand how can I do this process faster, and then you focus on cheaper, and then you focus on better. Of course, faster is because of some of the market forces and customer behavior that's driving you to do that process faster. >> Okay, and then the other one was risk reduction. I think that makes a lot of sense here. Actually, let me go back. So, one of the key pieces of it, of efficiency is time to value. So, if you can compress the time, or accelerate the time and you get the value that means more cash in house faster, whether it's cost reduction or-- >> And the other aspect you look at is, can you automate more of the processes, and in that way it can be faster. >> And that hits the income statement as well because you're reducing headcount cost of your, maybe not reducing headcount cost, but you're getting more out of different, out ahead you're reallocating them to more strategic initiatives. Everybody says that but the reality is you hire less people because you just automated. And then, risk reduction, so the degree to which you can lower your expected loss. That's just instead thinking in insurance terms, that's tangible value so certainly to large corporations, but even midsize and small corporations. Innovation, I thought was a good one, but maybe you could use an example of, give us an example of how in your career you've seen data contribute to innovation. >> So, I'll give an example of oil and gas industry. If you look at speed of innovation in the oil and gas industry, they were all paper-based. I don't know how much you know about drilling. A lot of the assets that goes into figuring out where to drill, how to drill, and actually drilling and then taking the oil or gas out, and of course selling it to make money. All of those processes were paper based. So, if you can imagine trying to optimize a paper-based innovation, it's very hard. Not only that, it's very, very by itself because it's on paper, it's in someone's drawer or file. So, it's siloed by design and so one thing that the industry has gone through, they recognize that they have to optimize the processes to be better, to innovate, to find, for example, shale gas was a result output of digitizing the processes because otherwise you can't drill faster, cheaper, better to leverage the shale gas drilling that they did. So, the industry went through actually digitizing a lot of the paper assets. So, they went from not having data to knowingly creating the data that they can use to optimize the process and then in the process they're innovating new ways to drill the oil well cheaper, better, faster. >> In the early days of oil exploration in the U.S. go back to the Osage Indian tribe in northern Oklahoma, and they brilliantly, when they got shuttled around, they pushed him out of Kansas and they negotiated with the U.S. government that they maintain the mineral rights and so they became very, very wealthy. In fact, at one point they were the wealthiest per capita individuals in the entire world, and they used to hold auctions for various drilling rights. So, it was all gut feel, all the oil barons would train in, and they would have an auction, and it was, again, it was gut feel as to which areas were the best, and then of course they evolved, you remember it used to be you drill a little hole, no oil, drill a hole, no oil, drill a hole. >> You know how much that cost? >> Yeah, the expense is enormous right? >> It can vary from 10 to 20 million dollars. >> Just a giant expense. So, now today fast-forward to this century, and you're seeing much more sophisticated-- >> Yeah, I can give you another example in pharmaceutical. They develop new drugs, it's a long process. So, one of the initial process is to figure out what molecules this would be exploring in the next step, and you could have thousand different combination of molecules that could treat a particular condition, and now they with digitization and data analytics, they're able to do this in a virtual world, kind of creating a virtual lab where they can test out thousands of molecules. And then, once they can bring it down to a fewer, then the physical aspect of that starts. Think about innovation really shrinking their processes. >> All right, well I want to say this about clouds. You made the statement in your keynote that how many people out there think cloud is cheaper, or maybe you even said cheap, but cheaper I inferred cheaper than an on-prem, and so it was a loaded question so nobody put their hand up they're afraid, but I put my hand up because we don't have any IT. We used to have IT. It was a nightmare. So, for us it's better but in your experience, I think I'm inferring correctly that you had meant cheaper than on-prem, and certainly we talked to many practitioners who have large systems that when they lift and shift to the cloud, they don't change their operating model, they don't really change anything, they get a bill at the end of the month, and they go "What did this really do for us?" And I think that's what you mean-- >> So what I mean, let me make it clear, is that there are certain use cases that cloud is and, as you saw, that people did raise their hand saying "Yeah, I have use cases where cloud is cheaper." I think you need to look at the whole thing. Cost is one aspect. The flexibility and agility of being able to do things is another aspect. For example, if you have a situation where your stakeholder want to do something for three weeks, and they need five times the computing power, and the data that they are buying from outside to do that experiment. Now, imagine doing that in a physical war. It's going to take a long time just to procure and get the physical boxes, and then you'll be able to do it. In cloud, you can enable that, you can get GPUs depending on what problem we are trying to solve. That's another benefit. You can get the fit for purpose computing environment to that and so there are a lot of flexibility, agility all of that. It's a new way of managing it so people need to pay attention to the cost because it will add to the cost. The other thing I will point out is that if you go to the public cloud, because they make it cheaper, because they have hundreds and thousands of this canned CPU. This much computing power, this much memory, this much disk, this much connectivity, and they build thousands of them, and that's why it's cheaper. Well, if your need is something that's very unique and they don't have it, that's when it becomes a problem. Either you need more of those and the cost will be higher. So, now we are getting to the IOT war. The volume of data is growing so much, and the type of processing that you need to do is becoming more real-time, and you can't just move all this bulk of data, and then bring it back, and move the data back and forth. You need a special type of computing, which is at the, what Amazon calls it, adds computing. And the industry is kind of trying to design it. So, that is an example of hybrid computing evolving out of a cloud or out of the necessity that you need special purpose computing environment to deal with new situations, and all of it can't be in the cloud. >> I mean, I would argue, well I guess Microsoft with Azure Stack was kind of the first, although not really. Now, they're there but I would say Oracle, your former company, was the first one to say "Okay, we're going to put the exact same infrastructure on prem as we have in the public cloud." Oracle, I would say, was the first to truly do that-- >> They were doing hybrid computing. >> You now see Amazon with outposts has done the same, Google kind of has similar approach as Azure, and so it's clear that hybrid is here to stay, at least for some period of time. I think the cloud guys probably believe that ultimately it's all going to go to the cloud. We'll see it's going to be a long, long time before that happens. Okay! I'll give you last thoughts on this conference. You've been here before? Or is this your first one? >> This is my first one. >> Okay, so your takeaways, your thoughts, things you might-- >> I am very impressed. I'm a practitioner and finding so many practitioners coming from so many different backgrounds and industries. It's very, very enlightening to listen to their journey, their story, their learnings in terms of what works and what doesn't work. It is really invaluable. >> Yeah, I tell you this, it's always a highlight of our season and Gokula, thank you very much for coming on theCUBE. It was great to see you. >> Thank you. >> You're welcome. All right, keep it right there everybody. We'll be back with our next guest, Dave Vellante. Paul Gillin is in the house. You're watching theCUBE from MIT. Be right back! (upbeat techno music)

>> From Cambridge Massachusetts, it's The Cube covering MIT Chief Data Officer, and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (electronic music) >> Welcome back to MIT in Cambridge, Massachusets everybody. You're watching The Cube, the leader in live tech coverage. My name is Dave Vellante I'm here with Paul Gillin. Day two of the of the MIT Chief Data Officer Information Quality Conference. One of the things we like to do, at these shows, we love to profile Boston area start-ups that are focused on data, and in particular we love to focus on start-ups that are founded by women. Julie Johnson is here, She's the Co-founder and CEO of Armored Things. Julie, great to see you again. Thanks for coming on. >> Great to see you. >> So why did you start Armored Things? >> You know, Armored Things was created around a mission to keep people safe. Early in the time where were looking at starting this company, incidents like Las Vegas happened, Parkland happened, and we realized that the world of security and operations was really stuck in the past right? It's a manual solutions generally driven by a human instinct, anecdotal evidence, and tools like Walkie-Talkies and video cameras. We knew there had to be a better way right? In the world of Data that we live in today, I would ask if either of you got in your car this morning without turning on Google Maps to see where you were going, and the best route with traffic. We want to help universities, ball parks, corporate campuses do that for people. How do we keep our people safe? By understanding how they live. >> Yeah, and stay away from Lambert Street in Cambridge by the way. >> (laughing) >> Okay so, you know in people, when they think about security they think about cyber, they think about virtual security, et cetera et cetera, but there's also the physical security aspect. Can you talk about the balance of those two? >> Yeah, and I think both are very important. We actually tend to mimic some of the revolutions that have happened on the cyber security side over the last 10 years with what we're trying to do in the world of physical security. So, folks watching this who are familiar with cyber security might understand concepts like anomaly detection, SIEM and SOAR for orchestrated response. We very much believe that similar concepts can be applied to the physical world, but the unique thing about the physical world, is that it has defined boundaries, right? People behave in accordance with their environment. So, how do we take the lessons learned in cyber security over 10 to 15 years, and apply them to that physical world? I also believe that physical and cyber security are converging. So, are there things that we know in the physical world because of how we approach the problem? That can be a leading indicator of a threat in either the physical world or the digital world. What many people don't understand is that for some of these cyber security hacks, the first weak link is physical access to your network, to your data, to your systems. How do we actually help you get an eye on that, so you already have some context when you notice it in the digital realm. >> So, go back to the two examples you sited earlier, the two shooting examples. Could those have been prevented or mitigated in some way using the type of technology you're building? >> Yeah, I hate to say that you could ever prevent an incident like that. Everyone wants us to do better. Our goal is to get a better sense predicatively of the leading indicators that tell you you have a problem. So, because we're fundamentally looking at patterns of people and flow, I want to know when a normal random environment starts to disperse in a certain way, or if I have a bottle neck in my environment. Because if then I have that type of incident occur, I already know where my hotspots are, where my pockets of risk are. So, I can address it that much more efficiently from a response perspective. >> So if people are moving quickly away from a venue, it might be and indication that there's something wrong- >> It could be, Yeah. That demands attention. >> Yeah, when you go to a baseball game, or when you go to work I would imagine that you generally have a certain pattern of behavior. People know conceptually what those patterns are. But, we're the first effort to bring them data to prove what those patterns are so that they can actually use that data to consistently re-examine their operations, re-examine their security from a staffing perspective, from a management perspective, to make sure that they're using all the data that's at their disposal. >> Seems like there would be many other applications beyond security of this type of analysis. Are you committed to the security space, or do you have broader ambitions? >> Are we committed to the security space is a hundred percent. I would say the number one reason why people join our team, and the number one reason why people call us to be customers is for security. There's a better way to do things. We fundamentally believe that every ball park, every university, every corporate campus, needs a better way. I think what we've seen though is exactly what you're saying. As we built our software, for security in these venues, and started with an understanding of people and flow, there's a lot that falls out of that right? How do I open gates that are more effective based on patterns of entry and exit. How do I make sure that my staffing's appropriate for the number of people I have in my environment. There's lots of other contextual information that can ultimately drive a bottom line or top line revenue. So, you take a pro sports venue for example. If we know that on a 10 degree colder day people tend to eagres more early in the game, how do we adjust our food and beverage strategy to save money on hourly workers, so that we're not over staffing in a period of time that doesn't need those resources. >> She's talking about the physical and the logical security worlds coming together, and security of course has always been about data, but 10 years ago it was staring at logs increasing the machines are helping us do that, and software is helping us do that. So can you add some color to at least the trends in the market generally, and then maybe specifically what you're doing bringing machine intelligence to the data to make us more secure. >> Sure, and I hate to break it to you, but logs are still a pretty big part of what people are watching on a daily basis, as are video cameras. We've seen a lot of great technology evolve in the video management system realm. Very advanced technology great at object recognition and detecting certain behaviors with a video only solution, right? How do we help pinpoint certain behaviors on a specific frame or specific camera. The only problem with that is, if you have people watching those cameras, you're still relying on humans in the loop to catch a malicious behavior, to respond in the event that they're notified about something unusual. That still becomes a manual process. What we do, is we use data to watch not only cameras, but we are watching your cameras, your Wi-Fi, access control. Contextual data from public transit, or weather. How do we get this greater understanding of your environment that helps us watch everything so that we can surface the things that you want the humans in the loop to pay attention to, right? So, we're not trying to remove the human, we're trying to help them focus their time and make decisions that are backed by data in the most efficient way possible. >> How about the concerns about The Surveillance Society? In some countries, it's just taken for granted now that you're on camera all the time. In the US that's a little bit more controversial. Is what your doing, do you have to be sensitive to that in designing the tools you're building? >> Yeah, and I think to Dave's question, there are solutions like facial recognition which are very much working on identifying the individual. We have a philosophy as a company, that security doesn't necessarily start with the individual, it starts with the aggregate. How do we understand at an aggregate macro level, the patterns in an environment. Which means I don't have to identify Paul, or I don't have to identify Dave. I want to look for what's usual and unusual, and use that as the basis of my response. There's certain instances where you want to know who people are. Do I want to know who my security personnel are so I can dispatch them more efficiently? Absolutely. Let's opt those people in and allow them to share the information they need to share to be better resources for our environment. But, that's the exception not the norm. If we make the norm privacy first, I think we'll be really successful in this emerging GDPR data centric world. >> But I could see somebody down the road saying hey can you help us find this bad guy? And my kids at camp this week, This is his 7th year of camp, and this year was the first year my wife, she was able to sign up for a facial recognition thing. So, we used to have to scroll through hundreds and hundreds of pictures to see oh, there he is! And so Deb signs up for this thing, and then it pings you when your son has a picture taken. >> Yeah. And I was like, That's awesome. Oh. (laughing) >> That's great until you think about it. >> But there aren't really any clear privacy laws today. And so you guys are saying, look it, we're looking at the big picture. >> That's right. >> But that day is coming isn't it? >> There's certain environments that care more than others. If you think about universities, which is where we first started building our technology, they cared greatly about the privacy of their students. Health care is a great example. We want to make sure that we're protecting peoples personal data at a different level. Not only because that's the right thing to do, but also from a regulatory perspective. So, how do we give them the same security without compromising the privacy. >> Talk about Bottom line. You mentioned to us earlier that you just signed a contract with a sports franchise, you're actually going to help them, help save them money by deploying their resources more efficiently. How does your technology help the bottom line? >> Sure, you're average sporting venue, is getting great information at the point a ticket is scanned or a ticket is purchased, they have very little visibility beyond that into the customer journey during an event at their venue. So, if you think about again, patterns of people and flow from a security perspective, at our core we're helping them staff the right gates, or figure out where people need to be based on hot spots in their environment. But, what that also results in is an ability to drive other operational benefits. Do we have a zone that's very low utilization that we could use as maybe even a benefit to our avid fans. Send them to that area, get traffic in that area, and now give them a better concession experience because of it, right? Where they're going to end up spending more money because they're not waiting in line in the different zone. So, how do we give them a dashboard in real time, but also alerts or reports that they can use on an ongoing basis to change their decision making going forward. >> So, give us the company overview. Where are you guys at with funding, head count, all that good stuff. >> So, we raised a seed round with some great Boston and Silicon Valley investors a year ago. So, that was Glasswing is a Boston AI focused fund, has been a great partner for us, and Inovia which is Canada's largest VC fund recently opened a Silicon Valley office. We just started raising a series A about a week ago. I'm excited to say those conversation have been going really well so far. We have some potential strategic partners who we're excited about who know data better then anyone else that we think would help us accelerate our business. We also have a few folks who are very familiar with the large venue space. You know, the distributed campuses, the sporting and entertainment venues. So, we're out looking for the right partner to lead our series A round, and take our business to the next level, but where we are today with five really great branded customers, I think we'll have 20 by the end of next year, and we won't stop fighting 'till we're at every ball park, every football stadium, every convention center, school. >> The big question, at some point will you be able to eliminate security lines? (laughing) >> I don't think that's my core mission. (laughing) But, optimistically I'd love to help you. Right, I think there's some very talented people working on that challenge, so I'll defer that one to them. >> And rough head count today? >> We have 23 people. >> You're 23 people so- >> Yeah, I headquartered in Boston Post Office Square. >> Awesome, great location. So, and you say you've got five customers, so you're generating revenue? >> Yes >> Okay, good. Well, thank you for coming in The Cube >> Yeah, thank you. >> And best of luck with the series A- >> I appreciate it and going forward >> Yeah, great. >> All right, and thank you for watching. Paul Gillin and I will be back right after this short break. This is The Cube from MIT Chief Data Officer Information Quality Conference in Cambridge. We'll be right back. (electronic music)

>> From Cambridge, Massachusetts, it's The Cube, covering MIT Chief data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, everybody. We're here at Tang building at MIT for the MIT CDOIQ Conference. This is the 13th annual MIT CDOIQ. It started as a information quality conference and grew through the big data era, the Chief Data Officer emerged and now it's sort of a combination of those roles. That governance role, the Chief Data Officer role. Critical for organizations for quality and data initiatives, leading digital transformations ans the like. I'm Dave Vallante with my cohost Paul Gillin, you're watching The Cube, the leader in tech coverage. Mark Chrisco is here, the deputy, sorry, Principle Deputy Director for Enterprise Information at the Department of Defense. Good to see you again, thanks for coming on. >> Oh, thank you for having me. >> So, Principle Deputy Director Enterprise Information, what do you do? >> I do data. I do acquisition data. I'm the person in charge of lining the acquisition data for the programs for the Under Secretary and the components so a strong partnership with the army, navy, and air force to enable the department and the services to execute their programs better, more efficiently, and be efficient in the data management. >> What is acquisition data? >> So acquisition data generally can be considered best in the shorthand of cost schedule performance data. When a program is born, you have to manage, you have to be sure it's resourced, you're reporting up to congress, you need to be sure you have insight into the programs. And finally, sometimes you have to make decisions on those programs. So, cost schedule performance is a good shorthand for it. >> So kind of the key metrics and performance metrics around those initiatives. And how much of that is how you present that data? The visualization of it. Is that part of your role or is that, sort of, another part of the organization you partner with, or? >> Well, if you think about it, the visualization can take many forms beyond that. So a good part of the role is finding the authoritative trusted source of that data, making sure it's accurate so we don't spend time disagreeing on different data sets on cost schedule performance. The major programs are tremendously complex and large and involve and awful lot of data in the a buildup to a point where you can look at that. It's just not about visualizing, it's about having governed authoritative data that is, frankly, trustworthy that you can can go operate in. >> What are some of the challenges of getting good quality data? >> Well, I think part of the challenge was having a common lexicon across the department and the services. And as I said, the partnership with the services had been key in helping define and creating a semantic data model for the department that we can use. So we can have agreement on what it would mean when we were using it and collecting it. The services have thrown all in and, in their perspective, have extended that data model down through their components to their programs so they can better manage the programs because the programs are executed at a service level, not at an OSD level. >> Can you make that real? I mean, is there an example you can give us of what you mean by a common semantic model? >> So for cost schedule, let's take a very simple one, program identification. Having a key number for that, having a long name, a short name, and having just the general description of that, were in various states amongst the systems. We've had decades where, however the system was configured, configured it the way they wanted to. It was largely not governed and then trying to bring those data sets together were just impossible to do. So even with just program identification. Since the majority of the programs and numbers are executed at a service level, we worked really hard to get the common words and meanings across all the programs. >> So it's a governance exercise the? >> Yeah. It is certainly a governance exercise. I think about it as not so much as, in the IT world or the data world will call it governance, it's leadership. Let's settle on some common semantics here that we can all live with and go forward and do that. Because clearly there's needs for other pieces of data that we may or may not have but establishing a core set of common meanings across the department has proven very valuable. >> What are some of the key data challenges that the DOD faces? And how is your role helping address them? >> Well in our case, and I'm certain there's a myriad of data choices across the department. In our place it was clarity in and the governance of this. Many of the pieces of data were required by statute, law, police, or regulation. We came out of eras where data was the piece of a report and not really considered data. And we had to lead our ways to beyond the report to saying, "No, we're really "talking about key data management." So we've been at this for a few years and working with the services, that has been a challenge. I think we're at the part where we've established the common semantics for the department to go forward with that. And one of the challenges that I think is the access and dissemination of knowing what you can share and when you can share it. Because Michael Candolim said earlier that the data in mosaic, sometimes you really need to worry about it from our perspective. Is too much publicly available or should we protect on behalf of the government? >> That's a challenge. Is the are challenge in terms of, I'm sure there is but I wonder if you can describe it or maybe talk about how you might have solved it, maybe it's not a big deal, but you got to serve the mission of the organization. >> Absolutely. >> That's, like, number one. But at the same time, you've got stakeholders and they're powerful politicians and they have needs and there's transparency requirements, there are laws. They're not always aligned, those two directives, are they? >> No, thank goodness I don't have to deal with misalignments of those. We try to speak in the truth of here's the data and the decisions across the organization of our reports still go to congress, they go to congress on an annual basis through the selected acquisition report. And, you know, we are better understanding what we need to protect and how to advice congress on what should be protected and why. I would not say that's an easy proposition. The demands for those data come from the GAO, come from congress, come from the Inspector General and having to navigate that requires good access and dissemination controls and knowing why. We've sponsored some research though the RAND organization to help us look and understand why you have got to protect it and what policies, rules, and regulations are. And all those reports have been public so we could be sure that people would understand what it is. We're coming out of an era where data was not considered as it is today where reports were easily stamped with a little rubber stamp but data now moves at the velocities of milliseconds not as the velocity of reports. So we really took a comprehensive look at that. How do you manage data in a world where it is data and it is on infrastructures like data models. >> So, the future of war. Everybody talks about cyber as the future of war. There's a lot of data associated with that. How does that change what you guys do? Or does it? >> Well, I think from an acquisition perspective, you would think, you know. In that discussion that you just presented us, we're micro in that. We're equipping and acquiring through acquisitions. What we've done is we make sure that our data is shareable, you know? Open I, API structures. Having our data models. Letting the war fighters have our data so they could better understand where information is here. Letting other communities to better help that. By us doing our jobs where we sit, we can contribute to their missions and we've aways been every sharing in that. >> Is technology evolving to the point where, let's assume you could dial back 10 or 15 years and you had the nirvana of data quality. We know how fast technology is changing but is it changing as an enabler to really leverage that quality of data in ways that you might not have even envision 10 or 15 years ago? >> I think technology is. I think a lot of this is not in tools, it's now in technique and management practices. I think many of us find ourselves rethinking of how to do this now that you have data, now that you have tools that you can get them. How can you adopt better and faster? That requires a cultural change to organization. In some cases it requires more advanced skills, in other cases it requires you to think differently about the problems. I always like to consider that we, at some point, thought about it as a process-driven organization. Step one to step two to step three. Now process is ubiquitous because data becomes ubiquitous and you could refactor your processes and decisions much more efficiently and effectively. >> What are some of the information quality problems you have to wrestle with? >> Well, in our case, by setting a definite semantic meaning, we kicked the quality problems to those who provide the authoritative data. And if they had a quality problem, we said, "Here's your data. "We're going to now use it." So it spurs, it changes the model of them ensuring the quality of those who own the data. And by working with the services, they've worked down through their data issues and have used us a bit as the foil for cleaning up their data errors that they have from different inputs. And I like to think about it as flipping the model of saying, "It's not my job to drive quality, "it's my job to drive clarity, "it's their job to drive the quality into the system." >> Let's talk about this event. So, you guys are long-time contributors to the event. Mark, have you been here since the beginning? Or close to it? >> Um... About halfway through I think. >> When the focus was primarily on information quality? >> Yes. >> Was it CDOIQ at the time or was it IQ? >> It was the very beginnings of CDOIQ. It was right before it became CDOIQ. >> Early part of this decade? >> Yes. >> Okay. >> It was Information Quality Symposium originally, is that was attracted you to it? >> Well, yes, I was interested in it because I think there were two things that drew my interest. One, a colleague had told me about it and we were just starting the data journey at that point. And it was talking about information quality and it was out of a business school in the MIT slenton side of the house. And coming from a business perspective, it was not just the providence of IT, I wanted to learn form others because I sit on the business side of the equation. Not a pure IT-ist or technology. And I came here to learn. I've never stopped learning through my entire journey here. >> What have you learned this week? >> Well, there's an awful lot I learned. I think it's been... This space is evolving so rapidly with the law, policy, and regulation. Establishing the CDOs, establishing the roles, getting hear from the CDOs, getting to hear from visions, hear from Michael Conlan and hear from others in the federal agencies. Having them up here and being able to collaborate and talk to them. Also hearing from the technology people, the people that're bringing solutions to the table. And then, I always say this is a bit like group therapy here because many of us have similar problems, we have different start and end points and learning from each other has proven to be very valuable. From the hallway conversations to hearing somebody and seeing how they thought about the products, seeing how commercial industry has implemented data management. And you have a lot of similarity of focus of people dealing with trying to bring data to bring value to the organizations and understanding their transformations, it's proven invaluable. >> Well, what did the appointment of the DOD's first CDO last year, what statement did that make to the organization? >> That data's important. Data are important. And having a CDO in that and, when Micheal came on board, we shared some lessons learned and we were thinking about how to do that, you know? As I said, I function in a, arguably a silo of the institution is the acquisition data. But we were copying CDO homework so it helped in my mind that we can go across to somebody else that would understand and could understand what we're trying to do and help us. And I think it becomes, the CDO community has always been very sharing and collaborative and I hold that true with Micheal today. >> It's kind of the ethos of this event. I mean, obviously you guys have been heavily involved. We've always been thrilled to cover this. I think we started in 2013 and we've seen it grow, it's kind of fire marshal full now. We got to get to a new facility, I understand. >> Fire marshal full. >> Next year. So that's congratulations to all the success. >> Yeah, I think it's important and we've now seen, you know, you hear it, you can read it in every newspaper, every channel out there, that data are important. And what's more important than the factor of governance and the factor of bringing safety and security to the nation? >> I do feel like a lot in, certainly in commercial world, I don't know if it applies in the government, but a lot of these AI projects are moving really fast. Especially in Silicon Valley, there's this move fast and break things mentality. And I think that's part of why you're seeing some of these big tech companies struggle right now because they're moving fast and they're breaking things without the governance injected and many CDOs are not heavily involved in some of these skunk works projects and it's almost like they're bolting on governance which has never been a great formula for success in areas like governance and compliance and security. You know, the philosophy of designing it in has tangible benefits. I wonder if you could comment on that? >> Yeah, I can talk about it as we think about it in our space and it may be limited. AI is a bit high on the hype curve as you might imagine right now, and the question would be is can it solve a problem that you have? Well, you just can't buy a piece of software or a methodology and have it solve a problem if you don't know what problem you're trying to solve and you wouldn't understand the answer when it gave it to you. And I think we have to raise our data intellectualism across the organization to better work with these products because they certainly represent utility but it's not like you give it with no fences on either side or you open up your aperture to find basic solution on this. How you move forward with it is your workforce has got to be in tune with that, you have to understand some of the data, at least the basics, and particularly with products when you get the machine learning AI deep learning, the models are going to be moving so fast that you have to intellectually understand them because you'll never be able to go all the way back and stubby pencil back to an answer. And if you don't have the skills and the math and the understanding of how these things are put together, it may not bring the value that they can bring to us. >> Mark, thanks very much for coming on The Cube. >> Thank you very much. >> Great to see you again and appreciate all the work you guys both do for the community. All right. And thank you for watching. We'll be right back with our next guest right after this short break. You're watching The Cube from MIT CDOIQ.

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Hi, everybody, welcome back to Cambridge, Massachusetts. This is theCUBE, the leader in tech coverage. I'm Dave Vellante with my cohost, Paul Gillin, and we're here covering the MIT Chief Data Officer Information Quality Conference, #MITCDOIQ. Lisa Ehrlinger is here, she's the Senior Researcher at the Johannes Kepler University in Linz, Austria, and the Software Competence Center in Hagenberg. Lisa, thanks for coming in theCUBE, great to see you. >> Thanks for having me, it's great to be here. >> You're welcome. So Friday you're going to lay out the results of the study, and it's a study of Data Quality Tools. Kind of the long tail of tools, some of those ones that may not have made the Gartner Magic Quadrant and maybe other studies, but talk about the study and why it was initiated. >> Okay, so the main motivation for this study was actually a very practical one, because we have many company projects with companies from different domains, like steel industry, financial sector, and also focus on automotive industry at our department at Johannes Kepler University in Linz. We have experience with these companies for more than 20 years, actually, in this department, and what reoccurred was the fact that we spent the majority of time in such big data projects on data quality measurement and improvement tasks. So at some point we thought, okay, what possibilities are there to automate these tasks and what tools are out there on the market to automate these data quality tasks. So this was actually the motivation why we thought, okay, we'll look at those tools. Also, companies ask us, "Do you have any suggestions? "Which tool performs best in this-and-this domain?" And I think this study answers some questions that have not been answered so far in this particular detail, in these details. For example, Gartner Magic Quadrant of Data Quality Tools, it's pretty interesting but it's very high-level and focusing on some global windows, but it does not look on the specific measurement functionalities. >> Yeah, you have to have some certain number of whatever, customers or revenue to get into the Magic Quadrant. So there's a long tail that they don't cover. But talk a little bit more about the methodology, was it sort of you got hands-on or was it more just kind of investigating what the capabilities of the tools were, talking to customers? How did you come to the conclusions? >> We actually approached this from a very scientific side. We conducted a systematic search, which tools are out there on the market, not only industrial tools, but also open-sourced tools were included. And I think this gives a really nice digest of the market from different perspectives, because we also include some tools that have not been investigated by Gartner, for example, like more BTQ, Data Quality, or Apache Griffin, which has really nice monitoring capabilities, but lacks some other features from these comprehensive tools, of course. >> So was the goal of the methodology largely to capture a feature function analysis of being able to compare that in terms of binary, did it have it or not, how robust is it? And try to develop a common taxonomy across all these tools, is that what you did? >> So we came up with a very detailed requirements catalog, which is divided into three fields, like the focuses on data profiling to get a first insight into data quality. The second is data quality management in terms of dimensions, metrics, and rules. And the third part is dedicated to data quality monitoring over time, and for all those three categories, we came up with different case studies on a database, on a test database. And so we conducted, we looked, okay, does this tool, yes, support this feature, no, or partially? And when partially, to which extent? So I think, especially on the partial assessment, we got a lot into detail in our survey, which is available on Archive online already. So the preliminary results are already online. >> How do you find it? Where is it available? >> On Archive. >> Archive? >> Yes. >> What's the URL, sorry. Archive.com, or .org, or-- >> Archive.org, yeah. >> Archive.org. >> But actually there is a ID I have not with me currently, but I can send you afterwards, yeah. >> Yeah, maybe you can post that with the show notes. >> We can post it afterwards. >> I was amazed, you tested 667 tools. Now, I would've expected that there would be 30 or 40. Where are all of these, what do all of these long tail tools do? Are they specialized by industry or by function? >> Oh, sorry, I think we got some confusion here, because we identified 667 tools out there on the market, but we narrowed this down. Because, as you said, it's quite impossible to observe all those tools. >> But the question still stands, what is the difference, what are these very small, niche tools? What do they do? >> So most of them are domain-specific, and I think this really highlights also these very basic early definition about data quality, of like data qualities defined as fitness for use, and we can pretty much see it here that we excluded the majority of these tools just because they assess some specific kind of data, and we just really wanted to find tools that are generally applicable for different kinds of data, for structured data, unstructured data, and so on. And most of these tools, okay, someone came up with, we want to assess the quality of our, I don't know, like geological data or something like that, yeah. >> To what extent did you consider other sort of non-technical factors? Did you do that at all? I mean, was there pricing or complexity of downloading or, you know, is there a free version available? Did you ignore those and just focus on the feature function, or did those play a role? >> So basically the focus was on the feature function, but of course we had to contact the customer support. Especially with the commercial tools, we had to ask them to provide us with some trial licenses, and there we perceived different feedback from those companies, and I think the best comprehensive study here is definitely Gartner Magic Quadrant for Data Quality Tools, because they give a broad assessment here, but what we also highlight in our study are companies that have a very open support and they are very willing to support you. For example, Informatica Data Quality, we perceived a really close interaction with them in terms of support, trial licenses, and also like specific functionality. Also Experian, our contact from Experian from France was really helpful here. And other companies, like IBM, they focus on big vendors, and here, it was not able to assess these tools, for example, yeah. >> Okay, but the other differences of the Magic Quadrant is you guys actually used the tools, played with them, experienced firsthand the customer experience. >> Exactly, yeah. >> Did you talk to customers as well, or, because you were the customer, you had that experience. >> Yes, I were the customer, but I was also happy to attend some data quality event in Vienna, and there I met some other customers who had experience with single tools. Not of course this wide range we observed, but it was interesting to get feedback on single tools and verify our results, and it matched pretty good. >> How large was the team that ran the study? >> Five people. >> Five people, and how long did it take you from start to finish? >> Actually, we performed it for one year, roughly. The assessment. And I think it's a pretty long time, especially when you see how quick the market responds, especially in the open source field. But nevertheless, you need to make some cut, and I think it's a very recent study now, and there is also the idea to publish it now, the preliminary results, and we are happy with that. >> Were there any surprises in the results? >> I think the main results, or one of the surprises was that we think that there is definitely more potential for automation, but not only for automation. I really enjoyed the keynote this morning that we need more automation, but at the same time, we think that there is also the demand for more declaration. We observed some tools that say, yeah, we apply machine learning, and then you look into their documentation and find no information, which algorithm, which parameters, which thresholds. So I think this is definitely, especially if you want to assess the data quality, you really need to know what algorithm and how it's attuned and give the user, which in most case will be a technical person with technical background, like some chief data officer. And he or she really needs to have the possibility to tune these algorithms to get reliable results and to know what's going on and why, which records are selected, for example. >> So now what? You're presenting the results, right? You're obviously here at this conference and other conferences, and so it's been what, a year, right? >> Yes. >> And so what's the next wave? What's next for you? >> The next wave, we're currently working on a project which is called some Knowledge Graph for Data Quality Assessment, which should tackle two problems in ones. The first is to come up with a semantic representation of your data landscape in your company, but not only the data landscape itself in terms of gathering meta data, but also to automatically improve or annotate this data schema with data profiles. And I think what we've seen in the tools, we have a lot of capabilities for data profiling, but this is usually left to the user ad hoc, and here, we store it centrally and allow the user to continuously verify newly incoming data if this adheres to this standard data profile. And I think this is definitely one step into the way into more automation, and also I think it's the most... The best thing here with this approach would be to overcome this very arduous way of coming up with all the single rules within a team, but present the data profile to a group of data, within your data quality project to those peoples involved in the projects, and then they can verify the project and only update it and refine it, but they have some automated basis that is presented to them. >> Oh, great, same team or new team? >> Same team, yeah. >> Oh, great. >> We're continuing with it. >> Well, Lisa, thanks so much for coming to theCUBE and sharing the results of your study. Good luck with your talk on Friday. >> Thank you very much, thank you. >> All right, and thank you for watching. Keep it right there, everybody. We'll be back with our next guest right after this short break. From MIT CDOIQ, you're watching theCUBE. (upbeat music)

>> From Cambridge, Massachusetts, it's The Cube! Covering MIT Chief Data Officer and Information Qualities Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome to Cambridge, Massachusetts everybody. You're watching The Cube, the leader in live tech coverage. My name is Dave Vellante and I'm here with my cohost Paul Gillin. And we're covering the 13th annual MIT CDOIQ conference. The Cube first started here in 2013 when the whole industry Paul, this segment of the industry was kind of moving out of the ashes of the compliance world and the data quality world and kind of that back office role, and it had this tailwind of the so called big data movement behind it. And the Chief Data Officer was emerging very strongly within as we've talked about many times in theCube, within highly regulated industries like financial services and government and healthcare and now we're seeing data professionals from all industries join this symposium at MIT as I say 13th year, and we're now seeing a lot of discussion about not only the role of the Chief Data Officer, but some of what we heard this morning from Mark Ramsey some of the failures along the way of all these north star data initiatives, and kind of what to do about it. So this conference brings together several hundred practitioners and we're going to be here for two days just unpacking all the discussions the major trends that touch on data. The data revolution, whether it's digital transformation, privacy, security, blockchain and the like. Now Paul, you've been involved in this conference for a number of years, and you've seen it evolve. You've seen that chief data officer role both emerge from the back office into a c-level executive role, and now spanning a very wide scope of responsibilities. Your thoughts? >> It's been like being part of a soap opera for the last eight years that I've been part of this conference because as you said Dave, we've gone through all of these transitions. In the early days this conference actually started as an information qualities symposium. It has evolved to become about chief data officer and really about the data as an asset to the organization. And I thought that the presentation we saw this morning, Mark Ramsey's talk, we're going to have him on later, very interesting about what they did at GlaxoSmithKline to get their arms around all of the data within that organization. Now a project like that would've unthinkable five years ago, but we've seen all of these new technologies come on board, essentially they've created a massive search engine for all of their data. We're seeing organizations beginning to get their arms around this massive problem. And along the way I say it's a soap opera because along the way we've seen failure after failure, we heard from Mark this morning that data governance is a failure too. That was news to me! All of these promising initiatives that have started and fallen flat or failed to live up to their potential, the chief data officer role has emerged out of that to finally try to get beyond these failures and really get their arms around that organizational data and it's a huge project, and it's something that we're beginning to see some organization succeed at. >> So let's talk a little bit about the role. So the chief data officer in many ways has taken a lot of the heat off the chief information officer, right? It used to be CIO stood for career is over. Well, when you throw all the data problems at an individual c-level executive, that really is a huge challenge. And so, with the cloud it's created opportunities for CIOs to actually unburden themselves of some of the crapplications and actually focus on some of the mission critical stuff that they've always been really strong at and focus their budgets there. But the chief data officer has had somewhat of an unclear scope. Different organizations have different roles and responsibilities. And there's overlap with the chief digital officer. There's a lot of emphasis on monetization whether that's increasing revenue or cutting costs. And as we heard today from the keynote speaker Mark Ramsey, a lot of the data initiatives have failed. So what's your take on that role and its viability and its longterm staying power? >> I think it's coming together. I think last year we saw the first evidence of that. I talked to a number of CDOs last year as well as some of the analysts who were at this conference, and there was pretty good clarity beginning to emerge about what they chief data officer role stood for. I think a lot of what has driven this is this digital transformation, the hot buzz word of 2019. The foundation of digital transformation is a data oriented culture. It's structuring the entire organization around data, and when you get to that point when an organization is ready to do that, then the role of the CDO I think becomes crystal clear. It's not so much just an extract transform load discipline. It's not just technology, it's not just governance. It really is getting that data, pulling that data together and putting it at the center of the organization. That's the value that the CDO can provide, I think organizations are coming around to that. >> Yeah and so we've seen over the last 10 years the decrease, the rapid decrease in cost, the cost of storage. Microprocessor performance we've talked about endlessly. And now you've got the machine intelligence piece layering in. In the early days Hadoop was the hot tech, and interesting now nobody talks even about Hadoop. Rarely. >> Yet it was discussed this morning. >> It was mentioned today. It is a fundamental component of infrastructures. >> Yeah. >> But what it did is it dramatically lowered the cost of storing data, and allowing people to leave data in place. The old adage of ship a five megabytes of code to a petabyte of data versus the reverse. Although we did hear today from Mark Ramsey that they copied all the data into a centralized location so I got some questions on that. But the point I want to make is that was really early days. We're now entered an era and it's underscored by if you look at the top five companies in terms of market cap in the US stock market, obviously Microsoft is now over a trillion. Microsoft, Apple, Amazon, Google and Facebook. Top five. They're data companies, their assets are all data driven. They've surpassed the banks, the energy companies, of course any manufacturing automobile companies, et cetera, et cetera. So they're data companies, and they're wrestling with big issues around security. You can't help but open the paper and see issues on security. Yesterday was the big Capital One. The Equifax issue was resolved in terms of the settlement this week, et cetera, et cetera. Facebook struggling mightily with whether or not how to deal fake news, how to deal with deep fakes. Recently it shut down likes for many Instagram accounts in some countries because they're trying to protect young people who are addicted to this. Well, they need to shut down likes for business accounts. So what kids are doing is they're moving over to the business Instagram accounts. Well when that happens, it exposes their emails automatically so they've all kinds of privacy landmines and people don't know how to deal with them. So this data explosion, while there's a lot of energy and excitement around it, brings together a lot of really sticky issues. And that falls right in the lap of the chief data officer, doesn't it? >> We're in uncharted territory and all of the examples you used are problems that we couldn't have foreseen, those companies couldn't have foreseen. A problem may be created but then the person who suffers from that problem changes their behavior and it creates new problems as you point out with kids shifting where they're going to communicate with each other. So these are all uncharted waters and I think it's got to be scary if you're a company that does have large amounts of consumer data in particular, consumer packaged goods companies for example, you're looking at what's happening to these big companies and these data breaches and you know that you're sitting on a lot of customer data yourself, and that's scary. So we may see some backlash to this from companies that were all bought in to the idea of the 360 degree customer view and having these robust data sources about each one of your customers. Turns out now that that's kind of a dangerous place to be. But to your point, these are data companies, the companies that business people look up to now, that they emulate, are companies that have data at their core. And that's not going to change, and that's certainly got to be good for the role of the CDO. >> I've often said that the enterprise data warehouse failed to live up to its expectations and its promises. And Sarbanes-Oxley basically saved EDW because reporting became a critical component post Enron. Mark Ramsey talked today about EDW failing, master data management failing as kind of a mapping and masking exercise. The enterprise data model which was a top down push for a sort of distraction layer, that failed. You had all these failures and so we turned to governance. That failed. And so you've had this series of issues. >> Let me just point out, what do all those have in common? They're all top down. >> Right. >> All top down initiatives. And what Glaxo did is turn that model on its head and left the data where it was. Went and discovered it and figured it out without actually messing with the data. That may be the difference that changes the game. >> Yeah and it's prescription was basically taking a tactical approach to that problem, start small, get quick hits. And then I think they selected a workload that was appropriate for solving this problem which was clinical trials. And I have some questions for him. And of the big things that struck me is the edge. So as you see a new emerging data coming out of the edge, how are organizations going to deal with that? Because I think a lot of what he was talking about was a lot of legacy on-prem systems and data. Think about JEDI, a story we've been following on SiliconANGLE the joint enterprise defense infrastructure. This is all about the DOD basically becoming cloud enabled. So getting data out into the field during wartime fast. We're talking about satellite data, you're talking about telemetry, analytics, AI data. A lot of distributed data at the edge bringing new challenges to how organizations are going to deal with data problems. It's a whole new realm of complexity. >> And you talk about security issues. When you have a lot of data at the edge and you're sending data to the edge, you're bringing it back in from the edge, every device in the middle is from the smart thermostat. at the edge all the way up to the cloud is a potential failure point, a potential vulnerability point. These are uncharted waters, right? We haven't had to do this on a large scale. Organizations like the DOD are going to be the ones that are going to be the leaders in figuring this out because they are so aggressive. They have such an aggressive infrastructure and place. >> The other question I had, striking question listening to Mark Ramsey this morning. Again Mark Ramsey was former data God at GSK, GlaxoSmithKline now a consultant. We're going to hear from a number of folks like him and chief data officers. But he basically kind of poopooed, he used the example of build it and they will come. You know the Kevin Costner movie Field of Dreams. Don't go after the field of dreams. So my question is, and I wonder if you can weigh in on this is, everywhere we go we hear about digital transformation. They have these big digital transformation projects, they generally are top down. Every CEO wants to get digital right. Is that the wrong approach? I want to ask Mark Ramsey that. Are they doing field of dreams type stuff? Is it going to be yet another failure of traditional legacy systems to try to compete with cloud native and born in data era companies? >> Well he mentioned this morning that the research is already showing that digital transformation most initiatives are failing. Largely because of cultural reasons not technical reasons, and I think Ramsey underscored that point this morning. It's interesting that he led off by mentioning business process reengineering which you remember was a big fad in the 1990s, companies threw billions of dollars at trying to reinvent themselves and most of them failed. Is digital transformation headed down the same path? I think so. And not because the technology isn't there, it's because creating a culture where you can break down these silos and you can get everyone oriented around a single view of the organizations data. The bigger the organization the less likely that is to happen. So what does that mean for the CDO? Well, chief information officer at one point we said the CIO stood for career is over. I wonder if there'll be a corresponding analogy for the CDOs at some of these big organizations when it becomes obvious that pulling all that data together is just not feasible. It sounds like they've done something remarkable at GSK, maybe we'll learn from that example. But not all organizations have the executive support, which was critical to what they did, or just the organizational will to organize themselves around that central data storm. >> And I also said before I think the CDO is taking a lot of heat off the CIO and again my inference was the GSK use case and workload was actually quite narrow in clinical trials and was well suited to success. So my takeaway in this, if I were CDO what I would be doing is trying to figure out okay how does data contribute to the monetization of my organization? Maybe not directly selling the data, but what data do I have that's valuable and how can I monetize that in terms of either saving money, supply chain, logistics, et cetera, et cetera, or making money? Some kind of new revenue opportunity. And I would super glue myself for the line of business executive and go after a small hit. You're talking about digital transformations being top down and largely failing. Shadow digital transformations is maybe the answer to that. Aligning with a line of business, focusing on a very narrow use case, and building successes up that way using data as the ingredient to drive value. >> And big ideas. I recently wrote about Experian which launched a service last called Boost that enables the consumers to actually impact their own credit scores by giving Experian access to their bank accounts to see that they are at better credit risk than maybe portrayed in the credit store. And something like 600,000 people signed up in the first six months of this service. That's an example I think of using inspiration, creating new ideas about how data can be applied And in the process by the way, Experian gains data that they can use in other context to better understand their consumer customers. >> So digital meets data. Data is not the new oil, data is more valuable than oil because you can use it multiple times. The same data can be put in your car or in your house. >> Wish we could do that with the oil. >> You can't do that with oil. So what does that mean? That means it creates more data, more complexity, more security risks, more privacy risks, more compliance complexity, but yet at the same time more opportunities. So we'll be breaking that down all day, Paul and myself. Two days of coverage here at MIT, hashtag MITCDOIQ. You're watching The Cube, we'll be right back right after this short break. (upbeat music)