I from Las Vegas Nevada it's the cube covering IBM world of Watson 2016 brought to you by IBM now here are your hosts John furrier and Dave vellante hey welcome back everyone we're here live in Las Vegas for IBM's world of Watson at the mandalay bay here this is the cube SiliconANGLE media's flagship program we go out to the events and extract the signal-to-noise I'm John Ford SiliconANGLE i'm here with dave vellante my co-host chief researcher red Wikibon calm and our next guest is inderpal bhandari who's the chief global chief data officer for IBM welcome to the cube welcome back thank you thank you meet you you have in common with Dave at the last event 10 years Papa John was just honest we just talked about the ten year anniversary of I OD information on demand and Dave's joke why thought was telling we'll set up the says that ten years ago different data conversation how do you get rid of it is I don't want the compliance and liability now it shifted to a much more organic innovative exciting yeah I need a value add what's the shift what's the big change in 10 years what besides the obvious of the Watson vision how did what it move so fast or too slow what's your take on this ya know so David used to be viewed as exhaust right the tribe is something to get rid of like you pointed out and now it's much more to an asset and in fact you know people are even talking about about quantifying it as an asset so that you can reflect it on the balance sheet and stuff like that so it certainly moved a long long way and I think part of it has to do with the fact that we are inundated with data and data does contain valuable information and to the extent that you're able to glean it and act on it efficiently and quickly and accurately it leads to a competitive advantage what's the landscape for architects out there because a lot of things that we hear is that ok i buy the day they I got a digital transformation ok but now I got to get put the data to work so I need to have it all categorized what's the setup is there a general architecture philosophy that you could share with companies that are trying to set themselves up for some baseline foundational sets of building blocks I mean I think they buy the Watson dream that's a little Headroom I just want to start in kindergarten or in little league or whatever metaphor we want to use any to baseline what's today what's the building blocks approach the building blocks approach I mean from a if you're talking about a pure technical architectural that kind of approach that's one thing if you're really going after a methodology that's going to allow you to create value from data I would back you up further I would say that you want to start with the business itself and gaining an understanding of how the business is going to go about monetizing itself not its data but you know what is the businesses monetization strategy how does the business plan to make money over the next few years not how it makes money today but over the next few years how it plans to make money that's the right starting point once you've understood that then it's basically reflecting on how data is best used in service of that and then that leads you down to the architecture the technologies the people you need the skills makes the process Tanner intuitive the way it used to be the ivory tower or we would convene and dictate policy and schemas on databases and say this is how you do it you're saying the opposite business you is going to go in and own the road map if you will the business it's a business roadmap and then figure it out yeah go back then go back well that's that's really the better way to address it than my way so the framework that we talked about in in Boston and now and just you're like the professor I'm the student so and I've been out speaking to other cheap date officers about it it's spot on this framework so let me briefly summarize it and we can I heard you not rebuilding it to me babe I'm saying this is Allah Falls framework I've stolen it but with no shame no kidding and so again we're doing a live TV it's you know he can source your head I will give him credit so but you have said they're there are two parallel and three sequential activities that have to take place for data opposite of chief data officer the two parallel our partnership with the line of business and get the skill sets right the three sequential are the thing you just mentioned how you going to monetize data access to data data sources and Trust trust the data okay so great framework and I'd say I've tested it some CEOs have said to me well I geeza that's actually better than the framework I had so they've sort of evolved as I said you're welcome and oh okay but now so let's drill into that a little bit maybe starting with the monetization piece in the early days Jonna when people are talking about Big Data it was the the mistake people made was I got to sell the data monetize the data itself not necessarily it's what you're saying yes yes I think that's the common pitfall with that when you start thinking about monetization and you're the chief data officer your brain naturally goes to well how do I monetize the data that's the wrong question the question really is how is the business planning to monetize itself what is the monetization strategy for the overall business and once you understand that then you kind of back into what data is needed to support it and that's really kind of the sets the staff the strategy in place and then the next two steps off well then how do you govern that data so it's fit for the purpose of that business lead that you just identified and finally what data is so critical that you want to centralize it and make sure that it's completely trusted so you back into those three those three steps so thinking about data sources you know people always say well should you start with internal should you start with external and the answer presumably is it depends it depends on the business so how do you how do you actually go through that decision tree what's that process like yeah I mean if you know you start with the monetization strategy of the company so for example I'll use IBM a banana and the case of IBM took me the first few months to understand that our monetization strategy was around cognitive business specifically making enterprises into cognitive businesses and so then the strategy that we have internally for IBM's data is to enable cognition within within IBM the enterprise and move forward with that and then that becomes a showcase for our customers because it is after all such a good example of a complex enterprise and so backing you know backing in from that strategy it becomes clear what are some of the critical data elements that you need to master that you need to trust that you need to centralize and you need to govern very very rigorously so that's basically how I approached it did I answer your question daivam do you get so so you touched on the on the second part I want to drill into the the third sequential activities which which is sources so i did so you did we just talk about this well the sources i mean if you had something add to that yes in terms of the i think you mentioned the internal versus external so one thing else i'll mention especially if you kind of take that 10-year outlook that we were talking about 10 years ago serials had very internal outlook in terms of the data was all internal business data today it's much more external as well there's a lot more exogenous data that we have to handle and validity and that's because we're making use of a lot more unstructured data so things like news feeds press releases articles that have just been written all our fair game to amplify the view that you have about some entity so for example if we're dealing with a new supplier you know previously we might gather some information by talking with them now we'd also be able to look at essentially everything that's out there about them and factor that in so it is a there's an element of the exogenous data that's brought to bear and then that obviously becomes part of the realm of the CDO as well to make sure that that data is available and you unusable by the business is John Kelly said something go ahead sorry well Jeff Jonas would say that's the observation space right that you want to have the news feeds it's extra metadata that could change the alchemy if you will of whatever the mix of the data is that kind of well yeah I would say you might even go further than just metadata i would say that in some some sense it's part of your intrinsic data set because you know it gives you additional information about the entities that you're collecting data on and that measuring the John Kelly in the keynote this morning he made two statements he said one is in three to five years every health care practitioners going to going to want to consult Watson and then he also said same thing for MA because watch is going to know every public piece of data about every single company right so it's would seem that within the three to five year time frame that the shift is going to be increasingly toward external data sources not necessarily the value in the lever points but in terms of the volume certainly of data is that fair I think it's a it's a fair statement I mean I think if you think of it in the healthcare context if you know a patient comes in and there's a doctor or a practitioner that's examining the patient right there they're generating some data based on their interaction but then if you think about the exogenous data that's relevant and pertinent to that case that could involve you know thousands of journals and articles and so you know your example of essentially saying that the external data could be far greater than the internal data out say we're already there okay and then the third sequential piece is trust are you gonna be able to trust the trust we talk a lot about we were down to Big Data NYC the same week you guys made your big announcement the data works everybody talks about data Lakes we joke gets the data swamp and can't really trust the data yeah we further away from a single version of the truth than we ever were so how are you dealing with that problem internally at IBM and what's the focus is it more on reporting is it more on supporting lines of business in product yeah the focus internal within IBM is in terms of driving cognition at the way I would describe it is at points where today we have significant human judgment being exercised to make decisions and that's you know thousands of points in our enterprise or complicated enterprise like IBM's and each of those decision points is actually an opportunity to inject cognitive technology and play and then bring to bear and augmented intelligence to those decisions that you know a factors in the exogenous data so leaving a much better informed decision but also them a much more accurate decision okay the two parallel activities let's start with the first one line of business you know relationships sounds like bromide why is it not just sort of a trite throwaway statement what where's the detail behind that so the detail behind that if you go back to the very first and the most important step and this whole thing with regard to the monetization strategy of the company understanding that if you don't have those deep relationships with the lines of business there's no way that you'll be able to understand the monetization strategy of the business so that's why that's a concurrent activity that has to start on day one otherwise you won't even get past the you know that that very first first base in terms of understanding what the monetization strategies are for the business and that can only really come by working directly with the business units meeting with their leadership understanding their business so you have to do that due diligence and that's where that partnership becomes critical then as you move on as you progress to that sequence you need them again so for instance once you understood the strategy and now you understood what data you need to follow that strategy and to govern it you need their help in governing the business because in many cases the businesses may be the ones collecting the data or at least controlling the source systems for that data so that partnership then just gets deeper and deeper and deeper as you move forward in that program I love the conscience of monetizing earlier and this some tweets going around you know what's holding it back cost of building it obviously and manageability but I want to bring that back and bring a developer perspective here because a lot of emphasis is on developing apps where the data is now part of the development process I wrote a blog post in 2008 saying that dated some new development kit radical at the time but reality it came out to be true and that they're looking at data as library of value to tap into so if stuffs annandale they could be sitting there for years but I could pull something out and be very relevant in context in real time and change the game on some insight and the insight economy is bob was saying so what is your strategy for IBM 21 on board more developer goodness and to how do you talk to customers were really trying to figure out a developer strategy so they can build apps and not to go back and rewrite it make it certainly mobile first etc but what's how does a date of first appt get built and I should developers be programming with you I'll give you a way to think about it right i mean and going back again to that ten-year paradigm shift right so ten years ago if somebody wanted to write an application and put it on the internet and it was based on data the hardest part was getting hold of the data because it was just very very difficult for them to get all of it to access the data and then those who did manage to get all of the data they were very successful in being able to utilize it so now with the the paradigm shift that's happened now is the approaches that you make the data available to developers and so they don't have to go through that work both in terms of accessing collecting finding that data then cleaning it it's also significant and so time consuming that it could put put back there their whole process of eventually getting to the app so to the extent that you have large stores of data that are ready to go and you can then make that available to a body of developers it just unleashes it's like having a library of code available is it all the hard work and I think that's a good way to look at it I mean that's think that's a very good way to look at it because you've also got technologies like the deep learning technologies where you can essentially train them with data so you don't need to write the code they get trained to later so I see a DevOps of data means like an agile meets I'm again you're right a lot of the cleaning and this is where you no more noise we all know that problem or data creates more noise better cleaning tools so however you can automate that yes seems to be the secret differentiator it's an accelerator it's amazing accelerator for development if you have good sets of data that are available for them to used so I want to round out my my little framework here your frame working with my my learnings for the fifth one being skills yes so this is complicated because it involves organization skills changes as pepper going through the lava here we try to get her on the cube Dave home to think the pamper okay babe yeah so should I take over pepper you want to go see pepper I want to see pepper on the cube hey sorry exact dress but so a lot of issues there there's reporting structures so what do you mean when you talk about sort of the skill sets and rescaling so and I'll describe to you a little bit about the organization that I have at IBM as an example some of that carries over and some of that doesn't the reason I say that is again I mean the skills piece there are some generic skill sets that you need for to be achieved data officer to be a successful chief data officer in an enterprise there is one pillar that I have in my organization is around data science data engineering DevOps deep learning and these are the folks who are adept at those technologies and approaches and methodologies and they can take those and apply them to the enterprise so in a sense these are the more technical people then another pillar that's again pretty generic and you have to have it is the information and data governance pillow so that anything that's flowing any data that's flowing through the data platform that I spoke off in the first pillar that those that that data is governed and fit for purpose so they have to worry about that as soon as any data is you even think of introducing that into the platform these folks have to be on that and they're essentially governing it making sure that people have the right access security the quality is good its improving there's a path to improving it and so forth I think those are some fairly generic you know skill sets that we have to get in the case of the first pillar what's difficult is that there aren't that many people with those skills and so it's hard to find that talent and so the sooner you get on it so that would that's the biggest barrier in the case of the second pillar what's the most difficult piece there is you need people who can walk the balance between monetization and governance too much governance and you essentially slow everything down and nothing moved a cuff and you're handcuffed and then you know if it's too much monetization you might run aground because you you ignored some major regulation so walking that loss of market value yeah that's what you have to really get ahead of your skis as they say and have a faceplant you'll try too hard to live boost mobile web startups like Twitter that's big cock rock concert with Twitter Facebook if you try to monetize too early yes you lose the flywheel effect of value absolutely so walking that balance is critical so that's that that's really finding the skill set to be able to do that that's that's what what's at play in that second or the third one is if you are applying it to an enterprise you have to integrate these you know this platform into the workflow off the enterprise itself otherwise you're not going to create any impact because that's where the impact gets created right that's basically where the data is that the tip of the spear to so to speak so you it's going to create value and in a large enterprise which has legacy systems which are silos which is acquiring companies and so on and so forth that's enough itself a significant job and that skill set is that's a handicapped because if you have that kind of siloed mentality you don't get the benefits of the data sharing right so what's that what's said how much how much effort would it take I'm just kind of painting that picture kind of like out there like well a lot of massively hard ya know that that's you know a lot of you know a lot of people think that data mining is all about my data you know this is my data I'm not going to give it to you the one of the functions of the chief data office is to change that mindset yeah and to stop making use of the data in a broader context than just a departmental siloed type of approach and now some data can legitimately be used only departmentally but the moment you need two or more department start using that data I mean it's essentially corporate data so are those roles a shared service everybody see that works it maybe varies but is it a shared service that reports into the chief data officer or is it embedded into the business those those skill sets that you talked about I think those skill sets are definitely part of the chief data officer you know organization now it's interesting you mentioned that about embedding them and the business units now in a in a large enterprise a complicated enterprise like IBM the different business units and that potentially have different business objectives and so forth you know you you do need a chief data officer role for each of these business units and that's something that I've been advocating that's my fault pillar and we are setting that up and then within the context of IBM so that they serve the business unit but they essentially reporting to me so that they can make use of the overall corporate structure you do their performance review the performance review is done by the business unit it is ok but the functional direction is given by me ok so I get back to still go either way oh yes that's a balance loon yeah absolutely under a lot of time for sure i'll get back to this data mining because you bring up a good point we can maybe continue on our next time we talk but data monies were all the cutting edge kind of best practices are were arsed work what we're relations are still there technically if you're here but that the dynamic of data mining is is that you're assuming no new data so with if you have a lot of data coming in most of the best data mining techniques are like a corpus you attack it and learned but if the pile of data is getting bigger faster that you could date a mine it what good is against or initial circular hole I'm going to again you know just take you back 10 years from now and now right and the differences between the two so it's very interesting points that you bring up I'll give you an example from 10 years ago this data mining example not ten years ago actually my first go-around at IBM so it's like 94 yeah one of the things I've done was we had a program a computer program that every team in the National Basketball Association started using and this was a classic data mining program it would look at the data and find insights and present them and one of the insights that it came up with and this was for a critical playoff game it told the coach you got to play your backup point guard and your backup forward now think about that which same coach would actually go with that so it's very hard for them to believe that they don't know if it's right or wrong in my own insurance and the way we got around that was we essentially pointed back to the snippets of video where those circumstances occurred and now the coach could see what is going on make a you know an informed decision flash forward to now the systems we have now can actually look at all that context all at once what's happening in the video what's happening in the audio also the data can piece together the context so data mining is very different today than what it was them now it's all about weaving the context and the story together and serving it up yeah what happened what's happening and what's going to happen kinda is the theaters of yes there are in sight writing what happened it's easy just yeah look at the data and spit out some insight what's happening now is a bit harder in memory I think that's the difference between cognition as it away versus data mining as you know we understood a few years ago great cartridge we can go for another hour but do we ever get enough love to follow up on some of the deep learning maybe come down to armonk next time we're in this certainly on the sports data we have a whole program on sports data so we love the sports with the ESPN of tech and bringing you all the action right here yes I did Doug before Moneyball you know my mistake was letting right yeah yeah right the next algorithm but that's okay you know we put a little foot mark on the cube notes for that thank you very much thank you appreciate okay live in Mandalay Bay we're right back with more live coverage I'm Sean for a table on thing great back today I am helping people

from Las Vegas accepting the signal from the noise it's the kue coverage interconnect 2016 brought to you by IBM now your host John hurry and Dave vellante okay welcome back around we are here live in Las Vegas for IBM interconnect 2016 special presentation of the cube our flagship program would go out to the events and extract the signal from the noise I'm John forreal echoes gave a lot they are next guest pricing Kohler who's the chief information technology officer and I'm saying this for the first time on the cube the weather company and IBM business welcome back to the cube thank you very much glad to be back last time you weren't an IBM business we were just the weather company were just the weather company so congratulations on your success want to say we really big fans of it but what Papa Chiana the team have done is visionary bold and very relevant so congratulations hey how's it feel it is grateful din we are really excited the opportunity with the IBM platform and you know the reach and the capabilities I mean it it really helps accelerate what we were trying to get done as the weather company you know as our own standalone business um and you know as you try to prepare and protect the entire planet all of its people and all of its businesses prepare and protect them for tomorrow which is really what the weather is company is all about finding that intersection of consumer behavior helping prepare and protect you as a in your personal life and your family but also you as a business owner how do we prepare and protect you to do better tomorrow because of the weather and the insights that we can provide fit straight into the work the Bob picciano in team have been doing with the insights you know economy with Watson and analytics with insights as a service all of that just kind of plugs together in it it really is a natural fit it's interesting to see IBM's move we were asked to guess on from IBM earlier and Jamie Thomas said it's all open source we want to get in early so this is an early bet for IBM certainly a bold move with the weather company but it's interesting the scuttlebutt as we talk to our sources inside the company close to the company have telling us that the weather companies is infiltrating and affecting the DNA IBM in a good way and you guys have always been a large scale data company and that is what all businesses are striving to digitize everything yes and so take us through that I mean one I think it's fair to say that you guys are kind of infecting I play in a positive way the mindset of being large-scale data yeah well why is that so compelling and how did you guys get here obviously whether the big data problem share some commentary around where it all came from well i think you know it's in my DNA first of all and it's in our company's DNA it's are no teams DNA you know I'm a change agent you would not want to hire me to maintain something good if you want to hire me to you know to break something and rebuild it better that's I'm your guy so you know I think when you look at the movement from you know the kind of the movement over time of IBM and you know the constant evolution that IBM goes through time is ripe when you take the cloud capabilities and you take data and you take analytics and the whole concept and capabilities of Watson Watson gets smarter as it learns more Watson can only be as smart as the data you feed it and so for Watson to continue to learn and continue to solve new problems and continue to expand its capability set we do have to feed it more data and and so you know looking at whether whether it was the original big data problem ever since the first mainframe the first you know application ever written on a mainframe was a weather forecast and ever since then everybody's been trying to figure out how to make the forecast more accurate and a lot of that comes from more data the more data you have the more accurate your forecast is going to be so we've been trying to solve this big data problem Walt and Dave talks about it was saw earlier in the opening about digital assets and in this digital transformation companies have to create more digital assets that's just dating yeah in this new model so when you look at the data aspect you say whether also is a use case where people are familiar with we were talking before we went on camera that people can understand the geekiness of whether it's different they're familiar with it but also highlights a real-life use case and the IOT Internet of Things wearables we heard you have sports guys on here tracking sensors this brings up that digital digitizing is going to be everything not just IT right it makes it real right if I think about my parents right we've been talking about IOT hey dad you're gonna have a connected refrigerator why does he care what do I need a connected refrigerator for but as you start to bring these insights to life and you make them real and you say you know what if I actually understand the humidity levels in your house and I can get that off the sensor on the air intake of your refrigerator I can now correlate that the humidity level outside of your house and I might be able to actually tweak your HVAC and I can make that run efficiently and I can now you know cut thirty percent of your cooling costs and all of these you know examples they're integrated they become real yeah and and I think weather is great because everybody checks their weather app the weather channel app or the weather underground app every day they're always looking at it and you know we get it right seventy-eight percent of the time we'd get it wrong sometimes we're constantly working to maintain our number-one position and data accuracy on weather forecasting and you know the more data we have the more accurate we can make it and so we've got any safer to you think just think about the use cases of people's lives slippery rose you know events correct I mean it's all tied in no goes back to another you know if I understand what's going on with the anti-lock braking system of a car and I already have a communication vehicle into everybody in that car which is our appt in their pocket I can alert them if the car is up ahead are having here are their abs activated and if all of the cars up ahead are having their abs activated I could alert them two miles back and say hey get ready slow down it's real it's not forecasted it's real data I'm giving you a real alert you should really take action and you know as we move from you know weather-alerts that we're looking out forward in time many hours as we're now doing rain alerts where we tell you it's going to start raining in the next seven minutes ten minutes people love those because it's right now and I can make a decision right now lightning strikes are always fascinating oh god because I gotta see crisis so last fall at IBM insight we interviewed David Kinney death your CEO and then right after I think was the week after I was watching some you know I was in Boston watching some sports program and there's bill belichick complaining about the in accuracy of whether i'll try that whether some reporter asked him about you know you factor in the weather i don't even pay attention i look at the weather forecast they're always wrong as a wait a minute I just I just interviewed David Kennedy he was bragging on the weather is the accuracy and how much it's improved so helping you mentioned seventy-eight percent of the time it's it's gotten better over time it has it still got rooms we're not perfect so so talk about that progression it is the data but how much better are you over time where is that better is it just short term or is it longer term at so color to that it's a great question and it's a fair point I think one of the biggest changes we've made in the last three years that the weather company is we've taken our forecast from what was roughly 2 million locations where we would do a forecast two million locations around the globe and today we we create a forecast for 2.2 billion locations around the globe because the weather is different at Fenway then Boston Logan it's just different than the the start time of rain the start time of a thunderstorm you know that's gonna be different now maybe five minutes but it's different the temperature the wind it's different and so as we've increased the accuracy and granularity of ours are our locations we've also done that from a time perspective as well so we used to produce a forecast every four to six hours depending upon how fast the models ran and did they run and complete successfully we now update our forecast every 15 minutes and so we we've increased the the you know all aspects of that and when you when you now think about getting your weather forecast you can no longer just type in BOS for your airport code and say i want to know what the weather is at boston logan if you're you know if you're in cambridge the boston logan forecast is not accurate for you you know five years ago every that was fine for everybody right right and so we have to retrain people to think about and make sure that when they're looking for a forecast and they're using our apps they can get a very specific forecast for where they are whatever point on the globe they are and and don't have you know Boston you know Logan as your you know favorite for your city if you're sitting in Cambridge or your you know you know it in Andover further outside where I am now where you gonna be my guess I gotta get so different you leverage the gps capabilities get that pinpoint location it will improve what the forecast is telling so I feel like this is one of those omni headed acquisition monsters for lack of a better term because when the acquisition was first announced is huh wow really interesting remember my line Dell's by an emc IBM is buying the weather company oh how intriguing it's a contrast it's all about the data the Dane is a service and then somebody whispered in my ear well you know there's like 800 Rockstar data scientists that come along with that act like wow it's all about the data scientists and then on IBM's earnings call i hear the weather company will provide the basis for our IOT platform like okay there's another one so we're take uh uh well i think IBM made a very smart move i'm slightly biased on that opinion but I think I be made a very smart move at very forward-looking move and one built on a cloud foundation not kind of a legacy foundation and when you think about IOT data sets we ingest 100 terabytes of data a day i ingest 62 different types of data at the weather company i ingest this data and then i distributed it massive volumes so what we had fundamentally built was the world's you know largest cloud-based iot data platform and you know IBM has many capabilities of their own and as we bring these things together and create a true next-gen cloud-based IOT data engine the ability for IBM to become smarter for Watson to become smarter than all of IBM's customers and clients to to become smarter with better applications better alerts better triggers and that alerts if you think about alerting my capability to alert hundreds of millions of people weather-alerts whether that's a lightning alert a rain alert a tornado warning whatever it is that's not really any different than me being able to alert a store clerk a night stock clerk at the local you know warehouse club that they need a stock you know aisle three differently put a different in cap on because we now have a new insight we have a new insight for what demand is going to be tomorrow and how do we shift what's going on that alert going down to a handheld device on the guy driving the four club yeah it's no different skoda tato yeah the capability to ingest transform store do analytics lon provide alerting on and then distribute data at massive scale that's what we do we talk about is what happened when Home Depot gets a big truck comes in a bunch of fans and say we know where this know the weather company did for you yeah we don't understand you'll understand you'll fake it later they file a big on the top of it so I OT as well as markets where people don't can't understand that some people don't know it means being like what's IOT Internet of Things I don't get it explain to them some little use cases that you guys are involved in today and some of these new areas that you're highlighting with with learning somehow see real life examples for for businesses and users there is a smarter planet kind of you know safe society kind of angle to it but it's also there's a nuts-and-bolts kind of practical if business value saving money saving lives changing you know maintenance what are some of the things share the IOT so there's there's only two things there so one is what is IOT and IOT really is is sensor data at the end of the day computers sensors electronic equipment has a sensor in it usually that sensor is there to do its job it's there to make a decision for what if it's a thermostat it has a sensor in it what's the temperature you know and so there are sensors in everything today things have become digitized and so those sensors are there as next as those next evolutions have come online those those sensors got connected to the Internet why because it was easier than to manage and monitor you know you know here we are at the mandalay bay how many thermostat sensors do you think this hotel casino complex has thousands and so you can't walk around and look at each one to understand well how's the temperature doing they all needed to be shipped back to a central room so that the in a building manager could actually do his job more efficiently those things then got connected so you could look at it on a smartphone those things they continued to get connected to make those jobs easier that first version of all of those things it was siloed that data SAT within just this hotel but now as we move forward we have the ability to take that data and merge it with other data sets there's actually a personal a Weather Underground personal weather station on the roof of the Mandalay Bay and it's actually collecting weather data every three seconds sending it back to us we have a very accurate understanding of the state of the Earth's atmosphere right atop this building having those throws is very good for the weather data but now how does the weather data impact a business that cares about the weather that has there we understand what the Sun load is on the top of this building and so we can go ahead and pre-heat your pre cool rooms get ahead of what's changing out sign that will have an impact here inside we have sensors on aircraft today that are collecting telemetry from aircraft turbulence data that helps us understand exactly what's going on with that airplane and as that's fed in real-time back down to the earth we process that and then send it back to the plane behind it and let that plane behind it know that it needs to alter it course change its flight plan automatically and update the pilots that they need to change course to a smoother altitude so gone are the days of the pilot having to radio down and fall around his body it's bumpy to get these through there anywhere machines can can can do this in real time collected and synthesize it from hundreds of aircraft that have been flying in that same route now we can actually take that and produce a better you know in flight plan for those for those machines we do that with with advertising so you know when you think about advertising you be easy the easy example is hey we know that you're going to sell more of X product when y weather condition happens that's easy but what if I also help you know when not to run an ad how do I help save you money you know if I know that there's no way for me to actually impact demand of your product up or down because we know over the course of time looking at your skew data and weather data that no matter what what we do weathers gonna have this impact on your product save your money don't run an ad tomorrow because it doesn't matter what you do you're not going to actually move your product more that's great and it's much business intelligence it's all the above its contextual data help people get insights in subjective and prescriptive analytics all rolled into one in a tool that alerts the actual person may explain to people out they were predictive versus prescriptive means a lot people get those confused what's your how would you prescriptive is you know where we want data that just tell us what to do based upon historic looking trends so i can take ten years of weather data and I can marry that up with ten years of some other data set and I can come up with you know a trend based upon the past and with that then I could prescribe what you should do in the future hey looks like general trend bring an umbrella tomorrow it's good it might rain but if I get into predictive analytics now I can start to understand by looking at forward-looking data things that haven't happened yet or new data sets that I'm merging in in real time oh wait a minute we thought that every time it rained more people went to this gas station to fill up but wait a minute today there's an accident on the road and people no matter what we do they're not going to go to that gas station because they're not even going to drive by it so being able to predict based upon feet of our real-time data but also forward-looking data the predictive analytics is really around the insights that we want to guess I got to ask you one question about the IBM situation and I want you to kind of reflect get him get you know all right philosophical for a second what's the learning that you've had over the past few weeks months post-acquisition inside IBM is there a learning that you to kind of hit you that you didn't expect there's something you'd expect what sure what was your big takeaway from this experience personally and you had some great success in the business now integrated into IBM what's the learning that cuz that's comes out of this for you I am really proud of the team at the weather company you know I I think what we have been able to accomplish as a small company you know comparative to my four hundred and sixty-eight thousand colleagues at IBM yeah what we've been able to accomplish what we've been able to do is really you know it's impressive and I've been proud of my team I'm proud of our company I'm proud of what we were able to get done as a company and you know the reflection really is as you bring that into IBM how do you make sure that you can you can now scale that to benefit such a large organization and and so while we were great at doing it for ourselves and we built an amazing business with amazing growth you know attracted lots of people that looked at buying us and obviously IBM executing on that I think that's amazing and I'm proud of that but I think my biggest reflection is that doesn't necessarily equate to success at IBM and we now have to retool and retrans form ourselves again to be able to take what we know how to do really well which is build great capabilities build big data platforms build analytics engines and inside engines and then armed a sea of developers to use our API we can't just take what we've done and go mate rest on your laurels you gotta go reinvent so I think my biggest you know real learning and take away from the kind of integration process is well we have a lot to learn and we have a lot of change we need to do so that we can actually now adapt and and continue to be us but do it in a way that works as an IBM ER and and that's that's there's there's going to be an art to this and we've got a ways to learn so I'm going in while eyes wide open around what I have to learn but I also am very reflective on on how proud I am as a leader of the team that you know has created you know such an amazing capability acquisition is done you savor it you come in you get blue washed and I hope I had a Saturday afternoon where I say okay got all like what is this gonna think so and then okay so you you wake up in the morning and you sort of described at a high level you know what you're doing but top three things that you're focused on the next you know 12 12 months so so you know the biggest thing that I'm focused on number one is making sure that we protect the weather company culture and how we know how to do and build great things and so I've got to lead us through obviously becoming integrated with IBM but not losing who we are and IBM is very supportive of that you know Bob picciano his team have been awesome and you know John Kelly and team have been awesome everybody that we have worked with has been so supportive of Bryson please make sure you find the right way through this we don't want to break you and I think that's natural for any acquisition for any yeah but you guys aren't dogmatic you were very candid saying we're gonna transform ourselves and adapt absolutely and so and so so we've got that on wrestling on my mind how do we go find immediate wins there's there's a a million different ways for us to win there's thousands of IBM sales teams that are out in front of clients it's just today with new problems how do we quickly adapt what we've been good at doing and help solve new problems very quickly so that's on my mind and then you know wrapping that in a way that becomes self service we can't I don't want to scale my team through people to solve all these problems I want to find a way to make sure that all these capabilities new data sets new insights new capabilities that we bring the life I want to do that in a self-service way I want to make sure that our technology the way we interact with developers the developer community that we bring in to kind of work on our behalf to make this happen I don't want to solve all these problems I want to enable others to solve the problems and so we're very focused on the self service aspect which i think is very new prices thank you so much taking the time out of your busy schedule to see with us in the queue good to see you again or any congratulations IOT everything's a sensor that we're a sense are here in the cube and we sense that it's time to go to SiliconANGLE DV and check out all the videos we have a purpose our sensor is to get the data to share that out with you thanks for the commentary and insight appreciate it whether company great success weather effects of song could affect stock prices all kinds of things in the real world so we had a lot of a lot of big data thank you very much look you here live in Las Vegas right back more coverage at this short break