>> from San Matteo. It's the Cube covering Scaler. Innovation Day. Brought to You by Scaler >> The Run Welcome to this special on the Ground Innovation Day. I'm John for a host of The Cube. We're here at scale. His headquarters in San Mateo, California Hardest Silicon Valley. But here the cofounder and CEO Steve, It's Irwin Ski and Jeff Low product marketing director. Thanks for having us. Thanks for having us. Thank you. But a great day so far talked Teo, the other co founders and team here. Great product opportunity. You guys been around for a couple of years, Got a lot of customers, Uh, just newly minted funded syriza and standard startup terms. That seems early, but you guys are far along, you guys, A unique architecture. What's so unique about the architecture? >> Well, thinks there's really three elements of the architecture's designed that I would highlight that differentiates us from our competitors. Three things that really set us apart. I think the biggest the 1st 1 is our use of a common our database. This is what allows us to provide a really superior search experience even though we're not using keyword indexing. Its purpose built for this problem domain and just provides us with great performance in scale. The second thing I would highlight would be the use of well, essentially were a cloud native solution. We have been architected in such a way that we can leverage the great advantage of cloud the scale, ability that cloud gives you the theological city. That cloud gives you andare. Architecture was built from the ground up to leverage that, uh and finally I would point out the way that we do our data. Um, the way that we don't silo data by data type, essentially any type of observe ability, data, whether it's logs or tracing or metrics. All that data comes into this great platform that we were in that provides a really great superior query performance over, >> and we talked earlier about Discover ability. I want to just quickly ask you about the keyword indexing and the cloud native. To me, that seems to be a two big pieces because a lot of the older all current standards people who are state of the art few years ago, 10 years ago, keyword index thing was a big part of it, and cloud native was still emerging except for those folks that were born the clouds. So >> this is a dynamic. How important is that? Oh, it's It's just critical. I mean, here, when we go to the white board, I love to talk about this in a little more detail in particular. So let's let's talk about keyword indexing, right? Because you're right. This is a lot of the technology that people leverage right now. It's what all of our competitors do in keyword indexing. Let's let's look at this from the point of view of a log ingestion pipeline. So in your first stage, you have your input, right? You've got your raw logs coming in. The first thing you do after that typically is parse. You're goingto parse out whatever fields you want from your logs. Now, all of our competitors, after they do that, they do in indexing step. Okay, this has a lot of expense to it. In fact, I'm going to dig into that after the log content is index. It's finally available for search. Where will be returned as a search result. Okay, this one little box, this little index box actually has a lot of costs associated with it. It contributes to the bloat of storage. It contributes to the cost of the overall product. In fact, that's why I love our competitors. Charge you based on how much you're indexing now, even how much you're ingesting. When you look at the cost for indexing, I think you can break it down into a few different categories. First of all, building the index. There's certain costs with just taking this data, building the index and storing it. Computational storage, memory, everything okay, But you build the index in order to get superior query performance, Right? So that kind of tells you that you're going to have another cost. You're going tohave an optimization cost. Where the index is that you're building are dependent on the queries that your users want to conduct, right, because you're trying to make sure you get as good of query performance as possible. So you have to take a look at the career. Is that your user performing the types of logs that you're coming in and you have to decide what indexing that you want to do? Okay. And that cost is shouldered by the burden of the customers. Um, okay, but nothing static in this world. So at some point your logs are going to change. The type of logs here in Justin is going to change. Maybe your query is goingto change. And so you have another category of costs, which is maintenance, right? You're going to have to react to changes in your infrastructure. It's used the type of logs you're ingesting, and basically, this is just creates a whole big loop where you have to keep an eye on your performance. You have to be constantly optimizing, maintaining and just going around in the circle. Right? And for us, we just thought that was ridiculous because all this costs is being born by the customer. And so when we designed the system, we just wanted to get rid of that. >> That's the classic shark fin. You see a fin on anything great whites going to eat you up or iceberg. You see that tip you don't see what's underneath? This seems to be the key problem, because the trend is more data. New data micro services gonna throw off new data type so that types is going up a CZ. Well, that's what that does that consistent with what you got just >> that's consistent. I mean, what we hear from our customers is they want flexibility, right? These are customers that are building service oriented, highly scalable applications on top of new infrastructure. They're reacting to changes everywhere, so they want to be able to not have to, you know, optimize their careers. They're not goingto want to maintain things. They just want to search product that works. That works over everything that they're ingesting. >> So, good plan. You eliminate that fly wheel of cost right for the index. But you guys, you were proprietary columnist, Or that's the key on >> your That's a Chiana and flexibility on data types. Yes, it does. And here, let me draw a little something to kind of highlight that because, you know, of course, it's a it begs the question. Okay, we're not doing keyword indexing. What do you do? What we do actually is leverage decades of research and distribute systems on commoner databases, and I'll use an example on or two >> People know that the data is, well, that's super fast, like a It's like a Ferrari. >> Yes, it's a fryer because you're able to do much more targeted essentially analysis on the data that you want to be searching over, right? And one way to look at this is, uh, no, Let's take a look at ah, Web access lock. Okay. And when we think about this and tables, we think that each line in the table represents, ah, particular entry from the access log. Right. And your columns represent what fields you've extracted. So for example, one the fields you might extract is thie HP status code. You know, Was it, um, a success or not? Right. Or you might have the your eye, or you might have the user agent of the incoming web request. Okay. Now, if you're not using a commoner database approach to execute a quarry where you're trying to count the number of non two hundreds that you've your Web server has responded with, you'd have to load in all the data for this >> table, right? >> And that's just its overkill in a commoner database. Essentially, what you do is you organize your data such that each column essentially has saved as a separate file. So if I'm doing a search where I just want to count the number of non two hundreds. I just have to read in these bites. And when your main bottleneck, it's sloshing bites in and out of Main Ram. This just gives you orders of magnitude better performance. And we've just built this optimize engine that does essentially this at its core and doesn't really well, really fast leveraging commoner database technology. >> So it lowers the overhead. You have to love the whole table in. That's going to take time. Clearing the table is going to take time. That seems to be the update. That's exactly right. Awesome, right? Okay. All right, Jeff. So you're the director of product marketing. So you got a genius pool of co founders here? Scaler. Been there, done that ball have successful track records as tech entrepreneurs, Not their first rodeo, making it all work. Getting it packaged for customers is the challenge that you guys have you been successful at it? What does it all mean? >> Yeah, it essentially means helping them explore and discover their data a lot more effectively than they happen before, you know, With applications and infrastructure becoming much more complex, much more distributed, our engineering customers are finding it increasingly difficult to find answers And so all of this technology that we've built is specifically designed to help him do that at much greater speed, Much greater ease, much more affordably and at scale. We always like to say we're fast, easy, affordable, at scale. >> You know, I noticed in getting to know you guys and interviewing people around around company. The tagline built by engineers for engineers is interesting. One. You guys are all super nerdy and geeky, so you get attacked and you take pride in the tech in the code. But also, your buyers are also engineers because they're dealing with cloud Native Wholenother Dev ops, level of scale where they love scale people in that market love infrastructures code. This is kind of the ethos of that market, but speed scale is what they live for, and that's their competitive advantage in most cases. How do you hit that point there? What's the alignment with the customers on scale and speed? >> Yeah, you know, with the couple of things that Stephen had mentioned, you know, the columnar database on DH, he mentioned cloud native. We like to refer to that as massively parallel or true multi tendency in the cloud those 11 two things give us really to key advantages when it comes to speed. So speed on in just that goes back to what Steven was talking about with the column. In our database, we're not having a weight to build the index so weakening unjust orders of magnitude faster than traditional solutions. So whereas a conventional solution might taking minutes even up to hours to ingest large sets of data, we can literally do it in seconds. It's the data's available immediately for used in research. One of our customers, in fact, that I'm thinking of down Australia actually uses our live tail because it actually works and as they push code out to production that can actually monitor what happens and see if the changes are impacting anything positively or negatively >> and speed two truths, a tagline the marking people came up with, which is cool. I love that kind of our fallouts. We have to get the content out there and get that let the people decide. But in your business, ingestion is critical. Getting the ingestion to value time frame nailed down is table stakes. People engineers want to test stuff. It doesn't work out of the box we ingest and they don't see value. They're not gonna kind of be within next levels. Kind of a psychology of the customer. >> Yeah, You know, when you're pushing code, you know, on an hourly basis, sometimes even minutes now, the last thing you want to do is wait for your data to analyse it, especially when a problem occurs. When a problem occurs and it's impacting a customer or impacting your overall business. You immediately go into firefighting mode, and you just can't wait to have that data become available so that speed to ingest becomes critical. You don't want to wait. The other aspect on the speed topic is B to search. So we talked about the types of searches that are calling. Our database affords us a couple that, within massively parallel and true multi tendency approach, basically means that you could do very, very ad hoc searches extremely quickly. You don't have to bill the keyword index. You don't have to have two, even build a query or learn how to build queries on DH, then run and then wait for it. And maybe in the meantime, wait to get a coffee or something like that. >> I mean, we grew up in Google search. Everyone who's exactly the Web knows what searches and discoveries kind the industry word in discovering navigation. But one of the things about searches about that made Google say Greg was relevance. You guys seem to have that same ethos around data discover, ability, speed and relevance. Talk about the relevance piece, because I think that, to me is what is everyone's trying to figure out as more data comes in? You mentioned some of the advantages Steven around, you know, complexity around data types. You know, Maur data types are coming on, so Relevance sees, is what everyone's chasing. >> So one of >> the things that I think we are very good at is helping people discover what is relevant. There are solutions out there. In fact, there's a lot of solutions out there that will focus on summarizing data, letting you easily monitor with a set of metrics, or even trace a single transaction from point A to point B through a set of services. Those are great for telling you that there is a problem or that problem exist. Maybe in this one service, this one server. But where we really shine is understanding why something has happened. Why a problem has occurred. And the ability to explore and discover through your data is what helps us get to that relevancy. >> Ameren meeting Larry and Sergey back into 1998. And you know, from day one it's fine. What you looking for him? And they did their thing. So I want to just quickly have you guys explain it. I think one thing that also has come up love to get your take on it, guys, is multi tendency urine in the clouds to get a lot of scale. We're out of resource talk about the debt. Why multi tendency is an important piece and what does that specifically mean? But the customer visa be potentially competitive solutions. And what do you guys bring for the tables? That seems to be an important discussion Point >> sure know. And it is one of the key piece of our architecture. I mean, when we talk about being designed for the cloud, this is a central part of that right? When you look at our competitors, for the most part, a lot of them have taken existing open the source off the shelf technologies and kind of taking that and shoved it into this, you know, square hole of, you know, let's run in the cloud, right? And so they're building. These SAS services were essentially they pretend like everyone's got access to a lot. Resource is but under the covers there, sitting there, spinning up thes open source solutions. Instances for each of the customers each of these instances are on ly provisioned with enough ramsi pew for that customer's needs, right? And so heaven forbid you try to issue more crews than you normally do or try to use Mohr you know, storage than you normally do, because your instance will just be capped out, right? Um, and also it's kind of inefficient in that when your users aren't issue inquiries, those CPU and RAM researchers are just sitting there idle instead, what we've done is we've built a system where we essentially have a big pool of resource is we have a big pool of CPU, a big pool of ram, a big pool of disc. Everyone comes in, get access to that, so it doesn't matter what customer you are. Your queries get full access to all these si pues that we have run around right? And that's that's the core of multi tendency is that we're able to not provision for just one look for each individual customer. But we have a big pool of resource is that everyone gets the >> land that's gonna hit the availability question on. And it's also have a side effect for all those app developers who want to build a I and stuff used data and build these micro services systems. >> They're going to get >> the benefit because you have that closed loop. Are you fly? Will, if you will. >> Yeah, yeah, the fight could just add the multi tendency really gives us a lot of economies of scale, both from, you know, the over provisioning and the ability to really effectively use resources. We also have the ability to pass those savings on to our customers. So there's that affordability piece that I think is extremely important. Find answers, this architectural force that >> Stephen I want to ask you because, you know, I know the devil's work pretty well. People are they're hard core, you know. They build their own stuff. They don't want us, have a vendor. Kuo. I can do this myself. There's always comes up there. But this use cases here. You guys seem to be doing well in that environment again. Engineering led solution, which I think gives you guys a great advantage. But what's the How do you handle the objection when you hear someone say, Well, I could do it. Just go do it myself. >> What I always like to point at is, yes, you can up to a decree, right? We often hear people that use open source technologies like elk. They can get that running and they can run it up to a certain scale like a you know, tens of gigabytes per day of logs. They're fine, right? But with those technologies, once it goes above a certain scale, it just becomes a lot more difficult to run. It's one those classic things you know, getting 50% of the way. There is easy getting 80% of the way. There is a lot harder. Getting 100% is almost impossible, right? And you, as whatever company that that that you're doing whatever product you're building, do you really want to spend your engineer? Resource is pushing through that curve, getting 80%. 100% of kind of good, a great solution. No, what we always pitches like Look, we've always solve these problems. These hard problems for this problem, too may come and leverage our technology. You don't have to spend your engineering capital on that. >> And then the people who are doing that scale that you guys provide, they want, they need those engineering resource is somewhere else. So I have to ask, you just basically followed question. Which is how does the customer know whether they have a non scaleable for scaleable solution? Because some of these SAS services air masquerading as scaleable solutions. >> No, they are. I mean, we we actually encourage our customers when they're in the pre sale stage to benchmark against us. We have ah customer right now that sending us terabytes of data per day as a trial just to show that we can meet the scale that they need. We encourage those same customers to go off and ask the other competitors to do that. And, you know, the proof is in the pudding. >> And how's the results look good? Yeah. So bring on the ingest Yes, that's that's That's the sales pitch. Yes, guys, thanks so much for sharing the inside. Even. Appreciate it, Jeff. Thanks for sharing. Appreciate it. I'm John for the Cube. Here for a special innovation Days scales >> headquarters in the heart of >> Silicon Valley's sent Matteo California. Thanks for watching.

hi 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 Fourier as Dave Volante hey welcome back everyone we are here live at the Mandalay Bay at the IBM world of Watson this is Silicon angles cube our flagship program we go out to the events and extract the signal from the noise I'm John Fourier with my co-host Dave allanté for the two days of wall-to-wall coverage our next guest is michelle fools so who's the chief marketing officer for IBM knew the company fairly new within the past year yes welcome to the queue last month I think you check all these new hires a lot of new blood coming inside me but this is a theme we heard from Staples to be agile to be fast you're new what's what's your impressions and what's your mandate for the branding the IBM strong brand but yes what's the future look well look I'm I'm thrilled to be here and I'm thrilled to be here because this is an extraordinary company that makes real difference in the world right and that I think you feel it here at the world of Watson in the sort of everyday ways that Watson and IBM touches consumers such as end-users makes their health better you know allows them to have greater experiences so so that's incredible to be part of my kind of company having said that and exactly to your point it's a time of acceleration and change for everyone in IBM is not immune to that and so my mandate here in my remit here and coming in and being a huge fan of what IBM has to say well how do we sharpen our messaging how do we always feel like a challenger brand you know how do we think about what Watson can do for people what the cloud can do what our services business can do and how is that distinctive and differentiated from everybody else out there and I think we have an incredible amount of assets to play with that's got to be through the line you know it's no longer the case that we can have a message on TV and that you know attracts the world the digital experiences are having every single day when they're clicking through on an ad when they're chatting with somebody when their car call center when they have a sales interaction is that differentiated message that brand resident all the way through second thing is marketing's become much more of a science you know and that to me is super exciting I've been a CEO most of my career and you know that the notion that marketing has to drive revenue that marketing has to drive retention and loyalty and expansion that we can come to the table with much more science in terms of what things are most effective in making sure that more clients love us more deeply for longer I'm gonna ask you the question because we had we've had many conversations with Kevin he was just here he was on last year Bob Lord the new chief digital officer we talked to your customers kind of the proof points in today's market is about transparency and if you're not a digital company how could you expect customers to to work with them so this has been a big theme for IBM you guys are hyper focused on being a digital company yes yes and how does it affect the brand a brand contract with the users what's your thoughts on that well first of all Bob Lord is awesome we've known each other for 10 years so it's so wonderful to be working with him again and Dave Kenny as well I think that the at the end of the day consumers have experiences and and you know think of every business you know out there as a consumer and they're having experiences all the time their expectations are being shaped by the fact that they go on Amazon and get prime delivery right their expectations are being shaped by they can go on Netflix and get you know personalized recommendations for them or Spotify and so our job of course and we have some of the greatest technical minds in the world it's to make sure that every experience lines up with the highest of their expectations and so much of that is digital and so my passion my background is entirely in the digital space I have a CEO of Travelocity and then CEO of gilt chief marketing a digital officer at Citigroup so the notion that you know the world's greatest digital experiences is something I'm very passionate about you mentioned Zelda so big TV ads and you think of the smarter planet which was so effective but it was a big TV campaign so you do what's the what's the sort of strategy that you're envisioning is in sort of digital breadcrumbs maybe you could talk about deadly yeah well think about Watson it's a perfect place to think about the Watson branding what does Watson really mean right Watson is and Ginni has said this so well of course it's cognitive and but at the end of the day it's about helping people make better decisions and so you can do some advertising with Watson and Bob Dylan and Watson and you know the young young girl with Serena and and you can get that messaging high but then you've got to bring it all the way through so that's why it's something like this is so powerful to see Woodside up their alley or all these companies talking about staples how they are using Watson embedded in their processes their tools to make their end-users experiences better and how nobody else could do this for them the way Watson's doing it that's taking a brand on high and advertising message on high and delivering value for businesses for patients for consumers all the way through that's what we have to do I got to ask you about that ad advertising trends I so we all see ad blocker in the news digital is a completely different new infrastructure expanded dynamic with social what not you can talk about Bob and I were talking last night about it too you Trevor you know banner ads are all out there impression base and then coded URLs to a landing page email marketing not gonna go away anytime soon but it's changing rapidly we have now new channels yeah what's your thoughts because this is now a new kind of ROI equation is there any thoughts on how you look at that and is it going to integrate into the top level campaigns how are you looking at the new digital that the cutting-edge digital stuff huge amounts of thoughts on this topic so I think you know if you think back 15 20 years ago there were always something called market mix modelling which helps advertisers and marketers to understand the effectiveness of their TV campaigns and frankly not too dissimilar from Nielsen you know there were so there was art and science at best in it and then all of a sudden the digital world evolved and you could get at a tactical level very very clear about attribution and whether you drove something and the challenge for us now is much more sophisticated models that are multi-touch attribution because the reality is an average consumer doesn't do one thing or have one interaction with a brand they're gonna see a TV show and watch a commercial while they're watching that commercial that business user or that end consumer is on their iPad or on their phone they're seeing a digital ad the next day at work they're being retargeted because they were aughts company they search for something they see a search campaign our job is to connect those dots and understand what really moves that consumer that business user to take an action and there are many sophisticated multi-touch attribution models where you model you know a standard set of behaviors and you test correlations against a bunch of different behaviors so you understand of what I did all the money I spent what really drove impact and by cohort I think that's the other credit there's no more the sense of sort of aggregated everything you really have to break it out yeah I didn't space my cohort to see what moves me and improve that experience right which has been you you get the example in the day of the Hilton retirees you already know that the retard the hotel was full so so obviously Watson plays a role in them Satyam plays a role in that so it's all about data it's all about you know that's where I think Watson can be extraordinarily helpful so if you think about the tool as a marketer has they're becoming more and more sophisticated and retargeting with something out of 10 years ago whenever was introduced that helped all of us a little bit and getting that message but it is only as good as the API is behind it and the the experience behind it when now when I was at gilt I was CEO of gilt we would put over a thousand products on sale every day that would be sold out by the next day sales down this 24-hour flash sale we had to get really really good at knowing how to how to retarget because last thing you want is to retarget something that sold out right or gone the next day and understand the user that was in and out and they're coming back and of course in that cohort that's where Watson to me is very exciting and you probably saw this in some of the demos of where Watson can help marketers you know where Watson can can really understand what are the drivers of behavior and what is likely to drive the highest purpose why were you so successful at guild and and how are the challenges different years because there's a sort of relatively more narrow community or city group to I was called the chief marketing and digital officer at Citigroup and and you know a tremendous budget and a lot of transactions you have to drive every day a lot of people you want to open credit cards and bank accounts so around the world I think that the the relentless focus on on marketing being art and science you know art and science and I think that's you know that passion for analytics passion for measurement having been CEO that passion for being able to say this is what we're doing and this is what we're driving so you've been kind of a data geek in your career you mentioned the financial services you can't to measure everything but back to the ad question you know the old saying used to be wasting half my advertise I just don't know which half yeah and my archives is wasted but now for the first time in the history of business in the modern era you measure everything online that's right so does that change your view and the prism of how you look at the business cuz you mentioned multi-touch yeah so now does that change the accountability for the suppliers I mean at agencies doing the big campaign I think it changes the game for all of us and there's no destination this is every day you can get better at optimizing your budget and and I would be the first to tell you as much of a sort of engineering and data geek because I've always been and deep-fried in the reality is there is art even in those attribution models what look back windows you choose etc that you know you're making decisions as a company but once you make those decisions you can start arraying all of your campaigns and saying what really moved the needle what was the most effective it's not an indictment that say what are we can do differently tomorrow you know the best marketers are always optimizing they're always figuring out at what point in the final can we get better tomorrow well in answer about talent because that's one of the things that we always talk about and also get your thoughts on Women in Technology scheme we were just at Grace Hopper last week and we started to fellowship called the tech truth and we're doing it's real passion area for us we have a site up QP 65 net / women in tech all women interviews we're really trying it the word out but this is now a big issue because now it's not stem anymore it's team arts is in there and we were also talking to the virtual reality augmented reality user experience is now potentially going to come into the immersion students and there's not enough artists yeah so you starting to see a combination of new discipline talents that are needed in the professions as well as the role of women in technology yeah your thoughts on that because this isn't you've been very successful what's your view on that at what's your thoughts about thank you for what you're doing right it takes a lot of people up there saying that this is important to make a difference so most of all thank you you know I think that this this is obviously a place I've been passion about forever I remember being a and being pregnant and that becoming this huge you know issue a news story and you're trying to juggle it right and how could a woman CEO be pregnant so it's so funny how people ridiculous took attention but but I think that the point is that the the advantage as a company has when there are great women in engineering and great women in data science and great women and user experience and design are just palpable they're probable in a variety of ways right when the team thinks differently the team is more creative the team is more open to new ideas the output for the customers are better right I mean they just saw a snapchat today just announced that in 2013 70% of their users were women so all the early adopters were women you know now it's balance but the early the early crowd were women and so we have got to figure out how to break some of the minds now I'm incredibly encouraged though while we still have a long way to go the numbers would suggest that we're having the conversation more and more and women are starting to see other women like them that they want to be it's a global narrative which is good why we're putting some journalists on there and funding it as and just as a fellowship because this it's a global story yeah okay and the power women I mean it's like there are real coders and this real talent coming in and the big theme that came out of that was is that 50% of the consumers of product are women's but therefore they should have some women features and related some vibe in there not just a male software driven concept well and should too when a powerful individual male individual like Satya steps in it and and you know understands what the mistaken and someone like refer to his speech two years ago where he said that you should just bad karma don't speak up and opening up transparency he got some heat yeah but that talk as you probably know but my opinion it's it's it's a positive step when an individual like that it was powerful and opening transparency within their company yeah that's it is that great networking I host a core I've been doing this for a year years with a good friend of mine Susan line from AOL we host a quarterly breakfast for women in tech every every quarter in New York City and we've been doing it for a long time it's amazing when those women come together the conversations we have the discussions we have how to help each other and support each other and so that's that's a real passion we were lost in a few weeks ago for the data science summit which Babu Chiana was hosting in and one of the folks was hosting the data divas breakfast we a couple there were a couple day two dudes who walked in and it was interesting yeah the perspectives 25 percent of the women or the chief data officer were women mm-hmm which was an interesting discussion as well so great 1,000 men at 15 you know as you see that techno but it's certainly changing when I get back to the mentoring thing because one of the things that we're all so passionate about is you've been a pioneer okay so now there's now an onboarding of new talent new personas new professions are being developed because we're seeing a new type of developer we're seeing new types of I would say artists becoming either CG so there's new tech careers that weren't around and a lot of the new jobs that are going to be coming online haven't even been invented yet right so you see cognition and what cognitive is enabling is a new application of skills yep can your thoughts on that because this is an onboarding opportunity so this could change the the number of percentage of women is diverse when you think about what I mean it's clear your notion of steam right your notion of stem that is a male and female phenomena and that is what this country needs it's what this world needs more of and so there's a policy and education obligation and all of us have to the next generation to say let's make sure we're doing right by them in terms of education and job opportunities when you think about onboarding I mean to me that the biggest thing about onboarding is the world is so much more interconnected than it used to be if you're a marketer it's not just art or science you have to do both it's a right brain left brain connectivity and I think 1020 years ago you could grow up in a discipline that was functional and maybe siloed and maybe you were great at left brain or great at right brain and the world demands so much more it's a faster pace it's an accelerated pace and the interconnection is critical and I've one of the things we're doing is we're putting together these diamond teams and I think it's going to really help lead the industry diamond teams are when you have on every small agile marketing team and analytics head a product marketing had a portfolio marketing had a design or a social expert these small pods that work on campaigns gone are the days that you could say designer designs it product comes up with the concept then it goes so it's design team then it goes to a production team then it goes to an analytics team we're forcing this issue by putting these teams together and saying you work together every day you'll get a good sense of where the specialty is and how you learn how to make your own discipline better because you've got the analytics person asked a question about media buying and media planning advertising as we're seeing this new real-time wet web yeah world mobile world go out the old days of planned media buyers placed the advertisement was a pacing item for execution yep now things you mentioned in the guild flash sales so now you're seeing new everyday flash opportunities to glob on to an opportunity to be engagement yeah and create a campaign on the fly yes and a vision of you guys I mean do you see that and does it change the cadence of how you guys do your execution of course of course that's one of the reasons we're moving to this diamond team and agile I think agile will ultimately be as impactful to marketing as it was to engineering and development and so I think the of course and that has to start with great modeling and great attribution because you have to know where things are performing so that you can iterate all the time I mean I believe in a world where you don't have marketing budgets and I know that sounds insane but I believe in a world where you set target and ranges on what you think you're gonna spend at the beginning of the year and every week like an accordion you're optimizing spend shipping code you've been marketing you should be doing like code so much of marketing is its episodic you boom and then it dies in a moment it's gone to the next one and you're talking about something that's I love that you know the personas to your point are much more fluid as well you got Millennials just creating their own vocations yes well this is where I think consumer companies have led the path and you know if you think about a lot of b2b companies we've had this aggregated CIO type buyer and now we've got to get much more sophisticated about what does the developer want you know what's important to the developer the messaging the tools the capabilities the user experience what about the marketer you know what the person in financial services and so both industry and professional discipline and you know schooling now with Watson you don't have to guess what they want you can actually just ask them yeah well you can actually the huge advantage you got you observe the observation space is now addressable right so you pull that in and say and that's super important even the stereotype of the persona is changing you've been saying all week that the developer is increasingly becoming business oriented maybe they don't they want they don't want to go back and get their MBA but they want to learn about capex versus op X and that's relevant to them and they to be a revolutionary you have to understand the impact right and and and they want to ship code they want to change the world I mean that is every engineering team I've ever worked at the time only worked with I mean I've been as close to engineering as from day one of the internet or early on in the internet great engineers are revolutionaries they want to change the world and they change the world they want to have a broader and broader understanding of what levers are at their disposal and I will say that I you know and I am one of the reasons I came to yam is I am passionate about this point technology cannot be in the hands of a few companies on the west coast who are trying to control and dominate the experience technology has to exist for all those amazing developers everywhere in the world who will make a difference to end user this is IBM strategy you actually have a big presence on the west coast also in Germany so you guys are going to where the action centers ours but not trying to just be so Malory point is what exactly because my point is IBM has always been there for making businesses stronger and better we don't monetize their data that's not our thing our thing is to use our cloud our cognitive capabilities and Watson to make actual businesses better so that ultimately consumers have better health care and better results I know you're new on the job silence this is not a trick question just kind of a more conversational as you talk to Bob lower Bob Chiana Jeanne yeah what's the promise of the brand and you used to be back in the days when you know Bob piano we talk about when we I worked at IBM in the 80s co-op student and it was you'll never get fired for buying IBM mainframe the kind of concept but it's evolved and I'll see we see a smarter plan what's the brand promise now you guys talk about what's the brainstorm on its head I think that I think the greatest innovators the world the most passionate business leaders of tomorrow come to IBM to make the world better and I I believe this is a brand for the forward the forward lookers the risk takers the you know the makers I think that you come to IBM because there's extraordinary assets and industry knowledge real humans real relationships that we exist to make your business better not our business will be a vibrato be exist to make your business better that has always been where IBM has been strong you know it's interesting that brings up a good point and just riffing on that Dave and I were just observing you know at the Grace Hopper with our tech truth mentorship which is promoting the intersection of Technology and social justice you're seeing that mission of Technology business value and social justice as an integral part of strategies because now the consumer access the consumerization of business yeah software based is now part of that feedback you're not doing good Millennials demand it I mean Millennials now when you look at the research in the next generation high Millennials are very very you know they want to know what are you doing for the world I mean who could do a 60 minute show besides IBM who could have who could be on 60 minutes changing cancer changing cancer outcomes for people beside IBM that that is an extraordinary testament to what the brand is and how it comes to life every day and that's important for Millennials we had Mary click-clack Clinton yesterday she is so impressive we're talking about how though these ozone layer is getting smaller these are us problems it can be solved they have to be so climate change can be solved so the whole getting the data and she's weather compass oh she's got a visit view on that is interesting her point is if we know what the problems are we as a community global society could actually solve them completely and it's an you know the more we make this a political and we say here is a problem and we have the data and we have the tools we have the people and capabilities to solve it that is where IBM Stan's tallest well I think with Watson use its focused on some big hairy problems to start with and now you're knocking off some some of the you know maybe more mundane but obviously significant to a marketer incredible that a company can start with the hardest most complicated problems the world has and actually make a difference my final question when I asked Mary this yesterday and she kind of talked about if she could have the magic Watson algorithm to just do something magical her and what would it be and she said I'll send Watson to the archives of all the weather data going back to World War two just compile it all and bring it back or addressability so the question is if you could have a Magic Watson algorithm for your chief marketing officer job what would you assign it to do like what would it be it's like first task well first of all reaction of course I'm a mom of six year olds an eight year old and so I want Watson to optimize my time no but a chief marketing officer I mean I think it really does go back to getting Watson's help in understanding how we use a dollar better how we use a dollar smarter how we affect more customers and and and connect connects with more customers in the way we you know we communicate the way we engage the way we've put our programs out that would be extraordinary and that's possible that's becoming more and more possible you know bringing science into the art of marketing I think will have great impact on what we're doing in also just the world I mean nobody wants to have you know maybe targeted ten times for something that's sold out well we asked one more time here so I got some more couple of questions because it's not getting the hook yet I gotta ask you see you mentioned Travelocity you know the web you've been through the web 1.2.0 yeah yeah so on so URLs and managing URLs was a great tracking mechanism from the old impressions weren't working and go to call to action get that look right there but now we different where that world is kind of like become critical infrastructure for managing technology since you're kind of geeking out with us here what's your view of the API economy because now apps don't use URLs they use tokens they use api's they use new push notification based stuff what sure how does api's change the marketing opportunities both right it's clearly changes the engineering environment and sort of opens up the world of possibilities in terms of who you partner with and how etc and I think it changes the marketing world too and entirely right you think about the API economy and the access you have to new ways of doing business new potential partnerships new ways of understanding data you know that that is absolutely you know at the fore of a lot of our thinking it might change the agency relationships to if they got to be more technical in changing as much as fast as companies are and they have to you know they are an extension they're your best you should be able to look in a room of agency and your team and not know who is who when you can tell who is who you have a problem and so agencies themselves have to become you know way more scientific harder-hitting faster pace and outcomes orient and somebody sees now are saying you know what pay me on outcomes I love that I love that mode to say we're in the boat with you pay me on outcome and the big s eyes are right there - absolutely yes Michele Palooza new chief marketing officer at IBM changing the game bring in some great mojo to IBM they're lucky to have you great conversations and thanks for coming on the cube live at Mandalay Bay this is silicon angles the cube I'm John four with Dave Volante be right back with more after this short break

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

from galvanized San Francisco extraction signal from the noise it's the kue cover the apache spark community event brought you IBM now your host John free George okay welcome back everyone we are live in San Francisco for this special q presentation with the IBM sparkman the event here live at galvanized in San Francisco workspace incubator great place for developer education IBM's big announcement today their commitment to spark they didn't see any numbers but I'm counting in the hundreds of millions of years to quote Papa Chiana on my call with him on Friday with rod $17 fuck yeah holler last for hundreds of millions yeah hundred millions of dollars getting late in the day going to be your coming rod Smith's our next guest rod welcome to the cube thank you very much with a catalyst behind spark at IBM worked hard on it yeah you guys tell a story what's the story well we worked on big data and I have a group of folks that go out and work with customers all the time and what we were doing Hadoop we would do these cool applications that sometimes you know small clusters 20 minutes you get a result and a customer would say can you do that in a couple seconds kind of look around and go what changed it means it did the business problem and they couldn't tell us but it's one of those data points in your head that go something's not quite right you know what's what's changing or what are they trying to tell me that they can't and that's when we started learning you know customers were looking for technology that they could iterate on quickly you know open-ended questions it wasn't the give me a problem do the game pew pew output I'm done this was oh gee there's the journey I now see some interesting insights I have other questions was it was something not right the data that they got didn't match their hypothesis or was it the expectation that if I can do it fast on google and find a Thai restaurant down the block well so I can it went that way something doesn't right what was with me that said why can't you tell me what you're really trying to accomplish what I learned is that as we go through these kind of digital transfer mation real real time they were thinking about how their business is going to change so fast and so the problems always been for technologists and vendors like IBM tell us the problem we pick out the technology and you're pretty well stuck with it it stays that way and they wanted more flexibility open-ended questions lots of different data sources on demand when they had to have it on this they wanted to see results along the way and they would rather have analytics be approximation that they could use quickly rather than after the fact and more accurate okay so you know when you went through that it wasn't they couldn't find a bi person to talk bad about and I couldn't find a data person so you know it was fun to try to put piece puzzles together and that's where spark came into this so I see a lot of other trends are kind of vectoring into that convergence which is in-memory databases you know the community flash for persistence store on the storage side so this you as a close to all that action what was the aha moment for for within IBM is han hey you know what this spark thing is the next Linux me we got to get out in front of this and help the community go faster and then kind of rising tide floats elbows what was that flash point flow we we had two of them one was that in our commerce group there's ways that they work on online pricing and there's a vendor stander which takes about a week when you get data off of a site or retail site they analyze that they correct the analytics they put it back up again takes about a week but we showed them a spark we could do it in about four hours a week down to four hours and now they started to think oh you know what do we offer customers now we have ways to have not just one product many products let's bring in other data location data traffic data weather data social data so that kind of exploded internally on this is a big change this is something that we can relate to cus of multiple data source of the need for unification and speed and and speed speed first because be first that's a heck all the speed i want to bring other data sets and it's time to value i mean if you're going to be a digital business and look at real time where it's going Netflix others have really set the standard on ok so then i'm a so let's take a next level so rod you're crazy we can't do that it would disrupt all these other businesses we have so how does that conversation happen within IBM the way that happens in IBM is rod you are crazy and you're going to cause me odds it up so please go away and I don't go away easily but you keep pushing on this and part of my job is to work with customers can I show value so I can take the product team saying you need to take this more seriously I've got currency now and then as you just said the marketplace starts to light up spark is on the front page as people are talking about how they're using it well Hadoop is growing too at the same time so it loop does it seeds the market seats the Mars you see you're playing ahead do but if you see the customer challenges and you're like you guys just connect the dots and and then it's back to the customer is talking about what their problems they want to use or the solutions are looking for so yeah it takes time because it's it's risky meaning that all of us have quarterly is what we're doing but how do we now make it safer for people in IBM jump in the water so that eventually they don't hate me so what's your what's your comment when a friend says hey rod you know linux was great but it's a different era oh you know here with cloud and mobile open source with the patch he's evolved to the point where it's very manageable for vendors to be contributed as well with with non company contributors how do you guys see the difference between those two worlds because really this is a Linux moment but there's no big bad main many many computer companies name frames out there but their specialized for like the Z systems are great but like this is scale out commodity hardware a dupe now that's growing how do you how do you describe that because there is a Linux correlation what linux was for open source then operating systems now this is kind of distributed analytics I think you're you're you know the the part of this is kind of real-time digital business transformations and while there is not a you know bad company out there you know amazon and others have shown how they can be online businesses and use analytics and be very effective but i'm a brick and mortar company and an online business how do i do the same thing and spark starts to really show that no they don't have a corner on the market we can compete so that's the big factor on this is well it's not one company doing this it's I need to be able to compete at the speed the businesses that didn't have to see that Amazon started kind of post recession or you know Dom bubble bursting you know web services was just kind of kicking through if we remember our history lessons and what happened was they really had no traction they built some building blocks right they made a good decision to integrate to core building blocks compute and storage and they built from there so in a way you guys can enable companies to have their own amazon like extensive experience because it's a fresh clean cute paper right it is and I think we're spark it's interesting is like you said in two verticals what do i do to retail what do I do in health care what are we doing finance right very specialized I we've shown in Watson you can do Watson for cancer research you can do Watson for cooking right but they're very vertical now so specialized domain expertise becomes really interesting right that's the big part and that's the part I really liked about spark they were the community really thought about solution developers you know they stayed away kind of middle ground I you don't have to be a deep dated person or a deep analytics API person what's the problem you want to solve how can I help you do that I think that's a you know that's interesting is that that's because most people go Jay this is speeds and feeds software we look at the solutions more holistic but then you're really talk about customer problems right the so-called outcomes that go on well that's what and I think that's the part that I've enjoyed is I want to talk to you you know about what your problem is I don't want to talk technology I you know I don't want to have to make a technology choice from stay one spark helps me with that I don't notify programming while all those things come together so I can concentrate we can concentrate on talking to the customer but you know learn from them what are you trying to accomplish so you watch the next things on your list good I just gonna say you know looking at your LinkedIn page i love this at BP emerging technologies for 20 some odd years so you see here you've seen a lot of technology's come a lot of emerging technologies and the acceleration of these technologies is only going more right you have a whole lot more in your portfolio you have to look at today then then you did yesterday or five years ago yeah why is sparks a special in the cornucopia of technologies that you've seen coming over the years it's a good question and and as I've done merging technologies I've learned that I have to you know listen to customers very carefully on it and when I hear those kind of repeatable business patterns do I see an economic change a transformation that really sticks with me and sometimes the old things have start really big you know they start out good and then they fade away but I always look for technologies that seem to have lots of dimensions to them from a business value standpoint that's what attracted me to spark and my team working with some customers on pocs we could do them quickly you know I really like to get to the point where you know we an industry we with notebooks and others we can do solutions in less than four hours for a customer what better thing to take your you know employee to lunch and spat them on the back for you know something that you didn't expect for weeks well one of the exciting things that you guys have done is you shine the spotlight on spark and you opened up the conversation globally around IBM is making a big move spark was a little bit of an outlier and the mainstream press I mean the press we're picking up spark oh yeah berkeley some credibility of great people behind it but now it's like wow it's going to get the attention of CX cxos out there and they're going to be like hmm if ibm's looking at it must be relevant because of the history you guys have with innovation but they're going to ask you the question I'm going to ask you which is it's not baked out yet where are we with this what are you guys going to do how does IBM work with the community to continue to bake out spark because a lot of people are using it bringing it in but it's evolving super fast and that's going to be the question is it baked and how does it get baked faster so I think there is lots of areas that if we just talked about if I'm doing retail or health care or fine it's going to be lots of specialized analytics because that's what spark for me is is enabling custom analytics on this second part is as you think about how you want to look at bigger problems I think that many times are learning is to try to you know once we got a technology lets make everything fit it rather than starting to separate it by business problems and I think we can do that now or we can bring to the table technology learning best practices around this and solutions I think you know at the end of the day it's house part can be integrated into a business solution and our customers very quickly and hopefully those customers see it broadly from interoperability standpoint of what they're going to do so the final question I have for you is what was the biggest learning that you've taken away from this process that was magnified through this whole journey of a taking IBM from being a participant in the as a citizen in the community early on as a founding member of spark this is back in two thousand nine so it wasn't like no one knew he was going on and you know we bird cover on Hadoop from the beginning so we'd love to watch these ecosystems grow but from from the early days to now today mmm what was the biggest thing that you learned that was magnified out of all the reactions all the feedback all the customers what can you share I I think for me when we did a spark hack you know our hackathon piece when 28,000 IBM ER showed up with ideas that told us twenty eight thousand 28,000 so now you stopped and 28,000 people who were focused on the customer so they had a thought of how this could be relevant this is great I mean this isn't like back talking for this isn't one little vein with a little stream it's big and it big was what we can do for our customer when was that um about two months ago how did you pull that off just out an email blast all the IBM's put on the message board to a crowd chat what did you do well when you put out an email blast the second one is you put on a webcam to explain to people what you're going to do with it what you'd like them to do and I'll we're setting it up and and then you step back and you know kind of cross your fingers hope people show up and then when you know you invite ten thousand and twenty eight thousand show up you kind of know that we're turning a corner as a company on understanding how we can use that for this this also highlights this whole connectedness apps internet of things and people are things to so their mobile device when you have that kind of people close to the action the creativity is there right there on the front lines and they don't feel like that the work they do is going to be taken by the machinery in the old days I got to go back all these hurdles I gotta jump now they could instantly be there with some solutions so that's that's super compelling the next question is security and how does how do you see that leaving in because now one of the things that came up will first meeting let me back up but I get this you think about security question for a second last week ahead dupe summit we were talking with the Hadoop ecosystem Hortonworks ODP conversations etc but when you looked at kind of like reading the tea leaves it was sparked that was kind of stealing the show the subtext was smart all the spark sessions were packed the developers had was salivating over sparks like to hear that I did why why is that why are the Hadoop developers salivating over spark is it because they wanted to go faster do they see extensions any thoughts I think that I've say it two ways one is I think there was and since I did who do for quite a while I think people thought for a while Hadoop was going to be an analytics platform and it it kind of went down the path of being immoral generalized platform so you can do more than MapReduce jobs so there's been this pent-up demand for really analytics focus and spark offered that focus and the performance side I think that's the parts in Hadoop sold kind of a false dream or it didn't materialize fast but I don't think of material out of false treaty I'm saying if they promise them around yeah it well and people set those you know well the fresh maybe yeah I don't think the vendors all I think was more than well vendors you know it did to unstructured data does that unstructured data does that storing data and I didn't be able to act on it creates some interesting dynamics I mean I've worked with customers who you know started to put data in Hadoop but to have put data dupe you know we're only going to do a year's worth of data and then putting three years of data because they want to do monte pucker up my Carlo simulations against a Monty Python it's time you threw water on us and we love yours we on the cube but the problem says we're talking about before like you know our internal use we can produce you know interesting innovations in days that's going to attract audiences because now they can show their you know business people what they can do for them that's what's really driving this I mean if you gotta see XO you know CMO says you know show me what you can do you know do segmentation on my population for these products they want it in in minutes not so you know going to run it in different jobs and the over a certain period of time I was just talking with the CEOs of docusign box 18 1018 Syrian kinky was executive director and then EVP a platform that Salesforce the common thread amongst those executives was the new digital transformation has such a dynamic or impactful economic impact yes I mean dr. Sanyal using examples how literally Deutsche Telekom saved 230 million dollars on one process yes one process yes with analytics and yeah process improvements extreme it sounds funny but it's extremely low hanging fruit they haven't had technology and the economics and be able support it now we do and now you're seeing the solution developer go I think I can make a business result faster yeah and if they can show it then businesses react and I think that's the beautiful thing about what Hadoop is done I mean I brought that up earlier trying to tease that out with reality we're seeing is that that mark is continuing to grow but there's a world beyond Hadoop yep I mean Hortonworks this public company I mean IBM is massive so you got Hadoop and then sparks a beautiful extension to that that enables so much more well I think spark will go further because it's more to me is another dimension it's an integration technology so i can have sparked up to legacy systems without hadoop you know in there doing analytics in there being an avenue for doing joins on data doing analytics on unstructured and transactional data whether data pulling it all together and I think that's the again talking about multi-dimensional that's what that was hard even five years ago so any relational database that's a nightmare yeah and you're asked about security so you want to touch on yeah okay go ahead so part of the things that I like about spark is the technology is called resilient distributed data sets r dds so I read data from a source and I make it into this r DD I can work on it that gives me a great data point or a great interaction with a Cassandra datastax did a really great job of a spark driver so you think about this in businesses for a db2 or something now I know where I can put my security and my governance I can put those at certain endpoints now as i'm reading in my application writing these things out so again back to my point of an integration it's not something that i'm trying to get around a business i'm at integrating extending their life and/or capabilities that's right so I got to ask you the internal IBM question my last question is it what's the vibe like at IBM because you know I've been you know I worked at IBM way back in the day back in the 80s and the cultures changed right so much mm-hmm but there's still a huge technical group of people at IBM so I got to ask you the question with all this new cloud innovation all this new capabilities to do stuff differently what's it like for all the technical guys at IBM right now because they got to be like Hayden we can now do this we can so new capabilities are emerging what's the what's the vibe like and what are some of the things that that are low-hanging fruit that are that our game change because low-hanging fruit is game-changing today oh yes I what's the vibe eternally at idea I've internally is very hot I mean the guys and gals at this you look at cloud computing look we've done with bluemix it got is getting you know great recent press it's getting great results with customers back to this time to value piece it's new to us I mean there's only a small group that started that so now the rest of the IBM arts are going this is really cool how do we do it now you've got analytics that you know we're starting you've been you know competencies are on this now you can take the real-time aspect so yeah the five is really all those little silos you know identity system here I got to build all the software now you can gotta go horizontal yeah so you know that's kind of a new thing that's kind of exciting it's gonna be fun to watch my final question I guess is my final final question is have you been keeping track this is the sixth and final time analytics well rods great to have you on the cube you're awesome great great commentary great great insight spark in the cloud is what data bricks announce what about an on-premise i'm a customer i want i want on prem I don't necessarily want to do what's next I 40 s or other stuff oh I think you're going to see you know like hybrid models for cloud where spark as a service is there on prem i think one of the really exciting parts to me is that one the unified program model to the portability of the analytic models so let's say I start on prom because I'm worried about security and other things and then I want to move it to a cloud service well I don't have to go rewrite it I can just move the analytics over from a model standpoint so I think you're going to see this evolved very fast as people want to do either on prem or hybrid or you know dedicated cuz of the integration capabilities and the distributed nature of it that's the point yep awesome well I'll let you get the last word on the segment share what the folks who's not or aren't watching what is this all about today why is in San Francisco today IBM's announcement what's so groundbreaking about it I know you're part of it a little bit biased but share the folks why what why now what's this all about what's what's what's going on here well we think that the kind of epicenter for spark innovation is here in San Francisco amp lab with data bricks and others are doing here and we want to be a part of that and I think spark technology senator setting up is about how we can contribute and learn and you know help the community grow we think this is gonna you brought some food to the party I mean you are I said earlier beer right you bring a you know the ml yeah you got them back other wine napa valley of course you got to go to wine well craft beers good north north bay thanks so much for coming on the cube really appreciate the insight because it is a great color from an expert IBM here we're on the ground this is the cube special presentation live in San Ruby back with more with live coverage of the breakouts in the event tonight IBM spark community event here in san fran at the galvanized workspace education center we write back