in Cambridge Massachusetts it's the cube covering the MIT chief data officer and information quality symposium now here are your hosts Stu miniman and George Gilbert you're watching the cube SiliconANGLE media's flagship program we go out to lots of technology shows and symposiums like this one here help extract the signal from the noise I'm Stu miniman droid joined by George Gilbert from the Wikibon research team and really thrilled to have on the program the keynote speaker from this MIT event Tom Davenport whose pressure at babson author of some books including a new one that just came out and thank you so much for joining us my pleasure great to be here all right so uh you know so many things your morning keynote that I know George and I want to dig into I guess I'll start with you talk about the you know for eras of you called it data today used to be formation from the information sorry but you said you started with when it was three eras of analytics and now you've came to information so I'm just curious we you know we get caught up sometimes on semantics but is there a reason why you switch from you know analytics to information now well I'm not sure it's a permanent switch I just did it for this occasion but you know I I think that it's important for even people who aren't who don't have as their job doing something with analytics to realize that analytics or how we turn data into information so kind of on a whim I change it from four errors of analytics 24 hours of information to kind of broaden it out in a sense and make people realize that the whole world is changing it's not just about analytics ya know I it resonated with me because you know in the tech industry so much we get caught up on the latest tool George will be talking about how Hadoop is moving to spark and you know right if we step back and look from a longitudinal view you know data is something's been around for a long time but as as you said from Peter Drucker's quote when we endow that with relevance and purpose you know that that's when we get information so yeah and that's why I got interested in analytics a year ago or so it was because we weren't thinking enough about how we endowed data with relevance and purpose turning it into knowledge and knowledge management was one of those ways and I did that for a long time but the people who were doing stuff with analytics weren't really thinking about any of the human mechanisms for adding value to to data so that moved me in analytics direction okay so so Tommy you've been at this event before you know you you've taught in written and you know written books about this about this whole space so willing I'm old no no its you got a great perspective okay so bring us what's exciting you these days what are some of our big challenges and big opportunities that we're facing as kind of kind of humanity and in an industry yeah well I think for me the most exciting thing is they're all these areas where there's just too much data and too much analysis for humans to to do it anymore you know when I first started working with analytics the idea was some human analysts would have a hypothesis about how to do that about what's going on in the data and you'd gather some data and test that hypothesis and so on it could take weeks if not months and now you know we need me to make decisions in milliseconds on way too much data for a human to absorb even in areas like health care we have 400 different types of cancer hundreds of genes that might be related to cancer hundreds of drugs to administer you know we have these decisions have to be made by technology now and so very interesting to think about what's the remaining human role how do we make sure those decisions are good how do we review them and understand them all sorts of fascinating new issues I think along those lines come you know in at a primitive level in the Big Data realm the tools are kind of still emerging and we want to keep track of every time someone's touched it or transformed it but when you talk about something as serious as cancer and let's say we're modeling how we decide to or how we get to a diagnosis do we need a similar mechanism so that it's not either/or either the doctor or you know some sort of machine machine learning model or cognitive model some waited for the model to say here's how I arrived at that conclusion and then for the doctor to say you know to the patient here's my thinking along those lines yeah I mean I think one can like or just like Watson it was being used for a lot of these I mean Watson's being used for a lot of these oncology oriented projects and the good thing about Watson in that context is it does kind of presume a human asking a question in the first place and then a human deciding whether to take the answer the answers in most cases still have confidence intervals you know confidence levels associated with them so and in health care it's great that we have this electronic medical record where the physicians decision of their clinicians decision about how to treat that patient is recorded in a lot of other areas of business we don't really have that kind of system of record to say you know what what decision did we make and why do we make it and so on so in a way I think health care despite being very backward in a lot of areas is kind of better off than then a lot of areas of business the other thing I often say about healthcare is if they're treating you badly and you die at least there will be a meeting about it in a healthcare institution in business you know we screw up a decision we push it under the rug nobody ever nobody ever considered it what about 30 years ago I think it was with Porter's second book you know and the concept of the value chain and sort of remaking the the understanding of strategy and you're talking about the you know the AP AP I economy and and the data flows within that can you help tie your concept you know the data flows the data value chain and the api's that connect them with the porters value chain across companies well it's an interesting idea I think you know companies are just starting to realize that we are in this API economy you don't have to do it all yourself the smart ones have without kind of modeling it in any systematic way like the porter value chain have said you know we we need to have other people linking to our information through api's google is fairly smart i think in saying will even allow that for free for a while and if it looks like there's money to be made in what start charging for access to those api so you know building the access and then thinking about the the revenue from it is one of the new principles of this approach but i haven't seen its i think would be a great idea for paper to say how do we translate the sort of value chain ideas a michael porter which were i don't know 30 years ago into something for the api oriented world that we live in today which you think would you think that might be appropriate for the sort of platform economics model of thinking that's emerging that's an interesting question i mean the platform people are quite interested in inner organizational connections i don't hear them as talking as much about you know the new rules of the api economy it's more about how to two sided and multi-sided platforms work and so on Michael Porter was a sort of industrial economist a lot of those platform people are economists so from that sense it's the same kind of overall thinking but lots of opportunity there to exploit I think so tell me what want to bring it back to kind of the chief data officer when one of the main themes of the symposium here I really like you talked about kind of there needs to be a balance of offense and defense because so much at least in the last couple of years we've been covering this you know governance and seems to be kind of a central piece of it but it's such an exciting subject it's exciting subject but you know you you put that purely in defense on and you know we get excited the companies that are you know building new products you know either you know saving or making more money with with data Kenny can you talk a little bit about kind of as you see how this chief data officer needs to be how that fits into your kind of four arrows yeah yeah well I don't know if I mentioned it in my talk but I went back and confirmed my suspicions that the sama Phi odd was the world's first chief data officer at Yahoo and I looked at what Osama did at Yahoo and it was very much data product and offense or unity established yahoo research labs you know not everything worked out well at Yahoo in retrospect but I think they were going in the direction of what interesting data products can can we create and so I think we saw a lot of kind of what I call to point o companies in the in the big data area in Silicon Valley sing it's not just about internal decisions from data it's what can we provide to customers in terms of data not just access but things that really provide value that means data plus analytics so you know linkedin they attribute about half of their membership to the people you may know data product and everybody else as a people you may know now well we these companies haven't been that systematic about how you build them and how do you know which one to actually take the market and so on but I think now more and more companies even big industrial companies are realizing that this is a distinct possibility and we oughta we ought to look externally with our data for opportunities as much as supporting internal and I guess for you talk to you know companies like Yahoo some of the big web companies the whole you know Big Data meme has been about allowing you know tools and processes to get to a broader you know piece of the economy you know the counterbalance that a little bit you know large public clouds and services you know how much can you know a broad spectrum of companies out there you know get the skill set and really take advantage of these tools versus you know or is it going to be something that I'm going to still going to need to go to some outside chores for some of this well you know I think it's all being democratized fairly rapidly and I read yesterday the first time the quote nobody ever got fired for choosing amazon web services that's a lot cheaper than the previous company in that role which was IBM where you had to build up all these internal capabilities so I think the human side is being democratized they're over a hundred company over 100 universities in the US alone that have analytics oriented degree programs so i think there's plenty of opportunity for existing companies to do this it's just a matter of awareness on the part of the management team I think that's what's lacking in most cases they're not watching your shows i guess and i along the lines of the you know going back 30 years we had a preference actually a precedent where the pc software sort of just exploded onto the scene and it was i want control over my information not just spreadsheets you know creating my documents but then at the same time aighty did not have those guardrails to you know help help people from falling off you know their bikes and getting injured what are the what tools and technologies do we have for both audiences today so that we don't repeat that mistake ya know it's a very interesting question and I think you know spreadsheets were great you know the ultimate democratization tool but depending on which study you believe 22 eighty percent of them had errors in them and there was some pretty bad decisions that were made sometimes with them so we now have the tools so that we could tell people you know that spreadsheet is not going to calculate the right value or you should not be using a pie chart for that visual display I think vendors need to start building in those guardrails as you put it to say here's how you use this product effectively in addition to just accomplishing your basic task but you wouldn't see those guardrails extending all the way back because of data that's being provisioned for the users well I think ultimately if we got to the point of having better control over our data to saying you should not be using that data element it's not you know the right one for representing you know customer address or something along those lines we're not there yet and the vast majority of companies I've seen a few that have kind of experimented with data watermarks or something to say yes this is the one that you're allowed to to use has been certified as the right one for that purpose but we need to do a lot more in that regard yeah all right so Tommy you've got a new book that came out earlier this year only humans need apply winners and losers in the age of smart machines so ask you the same question we asked eric donaldson and Auntie McAfee when they wrote the second Machine Age you know are we all out of job soon well I think big day and I have become a little more optimistic as we look in some depth at at the data I mean one there are a lot of jobs evolving working with these technologies and you know it's just somebody was telling me the other day that is that I was doing a radio interview from my book and the guy was hung who said you know I've made a big transition into podcasting he said but the vast majority of people in radio have not been able to make that transition so if you're willing to kind of go with the flow learn about new technologies how they work I think there are plenty of opportunities the other thing to think about is that these transitions tend to be rather slow I mean we had about in the United States in 1980 about half a million bank tellers since then we've had ATMs online banking etc give so many bank tellers we have in 2016 about half a million it's rather shocking i think i don't know exactly what they're all doing but we're pretty slow in making these transitions so i think those of us sitting here today or even watching her probably okay we'll see some job loss on the margins but anybody who's willing to keep up with new technologies and add value to the smart machines that come into the workplace i think is likely to be okay okay do you have any advice for people that either are looking at becoming you know chief data officers well yeah as I as you said balanced offense and defense defense is a very tricky area to inhabit as a CDO because you if you succeed and you prevent you know breaches and privacy problems and security issues and so on nobody gives you necessarily any credit for it or even knows that it's helps of your work that you were successful and if you fail it's obviously very visible and bad for your career too so I think you need to supplement defense with offense activities are analytics adding valued information digitization data products etc and then I think it's very important that you make nice with all the other data oriented c-level executives you know you may not want to report to the CIO or if you have a cheap analytics officer or chief information security officer chief digitization officer chief digital officer you gotta present a united front to your organization and figure out what's the division of labor who's going to do what in too many of these organizations some of these people aren't even talking to each other and it's crazy really and very confusing to the to the rest of the organization about who's doing what yeah do you see the CDO role but you know five years from now being a standalone you know peace in the organization and you know any guidance on where that should sit is structurally compared to say the CIO yeah I don't you know I I've said that ideally you'd have a CIO or somebody who all of these things reported to who could kind of represent all these different interests of the rest of the organization that doesn't mean that a CDO shouldn't engage with the rest of the business I think CIO should be very engaged with the rest of the business but i think this uncontrolled proliferation has not been a good thing it does mean that information and data are really important to organization so we need multiple people to address it but they need to be coordinated somehow in a smart CEO would say you guys get your act together and figure out sort of who does what tell me a structure I think multiple different things can work you can have it inside of IT outside of IT but you can at least be collaborating okay last question I've got is you talked about these errors and you know that they're not you know not one dies in the next one comes and you talked about you know we know how slow you know people especially are to change so what happened to the company that are still sitting in the 10 or 20 era as we see more 30 and 40 companies come yeah well it's not a good place to be in general and I think what we've seen is this in many industries the sophisticated companies with regard to IT are the ones that get more and more market share the the late adopters end up ultimately going out of business I mean you think about in retail who's still around Walmart was the most aggressive company in terms of Technology Walmart is the world's largest company in moving packages around the world FedEx was initially very aggressive with IT UPS said we better get busy and they did it to not too much left of anybody else sending packages around the world so I think in every industry ultimately the ones that embrace these ideas tend to be the ones who who prosper all right well Tom Davenport really appreciate this morning's keynote and sharing with our audience everything that's happening in the space will be back with lots more coverage here from the MIT CDO IQ symposium you're watching the q hi this is christopher