(upbeat music) >> Hello everyone, welcome to this CUBE conversation. I'm John Furrier, your host of theCUBE here in Palo Alto, California in our studios. Got a great guest here, Rachel Obstler, Vice President, Head of Product at heap.io or Heap is the company name, heap.io is URL. Rachel, thanks for coming on. >> Thanks for having me, John. Great to be here. >> So you guys are as a company is heavily backed with some big time VCs and funders. The momentum is pretty significant. You see the accolades in the industry. It's a hot market for anyone who can collect data easily and make sense of it relative to everything being measured, which is the Nirvana. You can measure everything, but then what do you do with it? So you're at the center of it. You're heading up product for heap. This is what you guys do. And there's a lot of solutions, so let's get into it. Describe the company. What's your mission and what you guys do? >> Yeah, so let me start maybe with how Heap was even started and where the idea came from. So Heap was started by Matin Movassate, someone who was working at Facebook. And this is important 'cause it gets right at the problem that we are trying to solve, which is that he was a product manager at Facebook and he was spending a lot of money on pizza. The reason why he was spending a lot of money on pizza is because he wanted to be able to measure what the users were doing in the product that he was responsible for, and he couldn't get the data. And in order to get the data, he would have to go beg his engineers to put in all sorts of tracking code to collect data. And every time he did so, he had to bribe him with pizza because it's no one's favorite work, number one, and then people want to build new things. They don't want to just constantly be adding tracking code. And then the other thing he found is that even when he did that then it took a couple weeks to get it done. And then he had to wait to collect the data to see what data is. It takes a while to build up the data, and he just thought there must be a better way. And so he founded, he with a couple other co-founders, and the idea was that we could automatically collect data all the time. So it didn't matter if you launched something new, you didn't have to do anything. The data would be automatically collected. And so Heap's mission is really to make it easy to create amazing digital experiences. And we do that by firstly, just making sure you have all the data of what your users are doing because you would think you want to create a new digital experience. You could just do that and it would be perfect the first time, but that's not how it works and users are not predictable. >> Yeah, remember back in the day, big data, Hadoop and that kind of fell flap, but the idea of a data lake started there. You saw the rise of Databricks, the Snowflakes. So this idea that you can collect is there. It's here now, state of the art. Now I see that market. Now the business model comes in. Okay, I can collect everything. How fast can I turn around the insights becomes the next question. So what is the business model of the company? What does the product do? Is it SaaS? Is it a as a package software? How do you guys deploy? How do your customers consume and pay for the service? >> Yeah, so we are a SaaS company and we sell largely to, it could be a product manager. It could be someone in marketing, but it's someone who is responsible for a digital service or a digital product. So they're responsible for making sure that that they're hitting whatever targets they have. It could be revenue, it could be just usage, getting more users adopted, making sure they stay in the product. So that's who we sell to. And so basically our model is just around sessions. So how many sessions do you have? How much data are you collecting? How much traffic do you have? And that's how we charge. I think you were getting at something else though that was really interesting, which is this proliferation of data and then how do you get to an insight. And so one of the things that we've done is first of all, okay, collecting all the data and making sure that you have everything that you need, but then you have a lot of data. So that is indeed an issue. And so we've also built on top of Heap a data science layer that will automatically surface interesting points. So for instance, let's say that you have a very common user flow. Maybe it's your checkout flow. Maybe it's a signup flow and you know exactly what the major milestones are. Like you first fill out a form, you sign up, like maybe you get to do the first thing in the trial. You configure it, you get some value. So we're collecting not only those major milestones, we're collecting every single thing that happens in between. And then we'll automatically surface when there is an important drop off point, for instance, between two milestones so that you know exactly where things are going wrong. >> So you have these indicators. So it's a data driven business. I can see that clearly. And the value proposition in the pitch to the customer is ease of use. Is it accelerated time to value for insights? Is it eliminating IT? Is it the 10X marketer? Or all of those things? What is the core contract with the customer, the brand promise? >> That's exactly. So it's the ability to get to insight. First of all, that you may never have found on your own, or that would take you a long time to keep trialing an error of collecting data until you found something interesting. So getting to that insight faster and being able to understand very quickly, how you can drive impact with your business. And the other thing that we've done recently that adds a lot to this is we recently joined forces with a company named Auryc so we just announced this on Monday. So now on top of having all the data and automatically surfacing points of interest, like this is where you're having drop off, this is where you have an opportunity, we now allow you to watch it. So not only just see it analytically, see it in the numbers, but immediately click a show me button, and then just watch examples of users getting stuck in that place. And it really gives you a much better or clearer context for exactly what's happening. And it gives you a much better way to come up with ideas as to how to fix it as one of those digital builders or digital owners. >> You know, kind of dating myself when I mention this movie "Contact" where Jodie foster finds that one little nugget that opens up so much more insight. This is what you're getting at where if you can find that one piece that you didn't see before and bring it in and open it up and bring in that new data, it could change the landscape and lens of the entire data. >> Yeah. I can give you an example. So we have a customer, Casper. Most are familiar with that they sell mattresses online. So they're really a digital innovator for selling something online that previously you had to like go into a store to do. And they have a whole checkout flow. And what they discovered was that users that at the very end of the flow chose same day delivery were much more likely to convert and ultimately buy a mattress. They would not necessarily have looked at this. They wouldn't necessarily have looked at or decided to track like delivery mechanism. Like that's just not the most front and center thing, but because he collects all the data, they could look at it and say, oh, people who are choosing this converted a much higher rate. And so then they thought, well, okay, this is happening at the very end of the process. Like they've already gone through choosing what they want and putting it in their card and then it's like the very last thing they do. What if we made the fact that you could get same day delivery obvious at the beginning of the whole funnel. And so they tried that and it improved their conversion rate considerably. And so these are the types of things that you wouldn't necessarily anticipate. >> I got to have a mattress to sleep on. I want it today. Come on. >> Yeah, exactly. Like there's a whole market of people who are like, oh no, I need a mattress right now. >> This is exactly the point. I think this is why I love this opportunity that you guys are in. Every company now is digitalizing their business, aka digital transformation. But now they're going to have applications, they're going to have cloud native developers, they're going to be building modern applications. And they have to think like an eCommerce company, but it's not about brick and mortars anymore. It's just digital. So this is the new normal. This is an imperative. This is a fact. And so a lot of them don't know what to do. So like, wait a minute, who do we call? This is like a new problem for the mainstream. >> Yeah, and think about it too. Actually e-commerce has been doing this for quite a while, but think about all the B2B companies and B2B SaaS, like all the things that today, you do online. And that they're really having to start thinking more like e-commerce companies and really think about how do we drive conversion, even if conversion isn't the same thing or doesn't mean the same thing, but it means like a successful retained user. It's still important to understand what their journey is and where you going to help them. >> Recently, the pandemic has pulled forward this digital gap that every company's seeing, especially the B2B, which is virtual events, which is just an indicator of the convergence of physical and online. But it brings up billions of signals and I know we have an event software that people do as well. But when you're measuring everything, someone's in a chat, someone hit a web page, I mean there are billions of signals that need to get stored, and this is what you guys do. So I want to ask you, you run the product team. What's under the covers? What's the secret sauce for you guys at Heap? Because you got to store everything. That's one challenge. That's one problem you got to solve. Then you got to make it fast because most of the databases can't actually roll up data fast enough. So you're waiting for the graph forever when some people say. What's under the covers? What's the secret sauce? >> Well, it's a couple different things. So one is we designed the system from the very beginning for that purpose. For the purpose of bringing in all those different signals and then being able to cut the data lots of different ways. And then also to be able to apply data science to it in real time to be able to surface these important points that you should be looking at. So a lot of it is just about designing the system for the very beginning for that purpose. It was also designed to be easy for everyone to use. So what was a really important principle for us is a democratization of data. So in the past, you have these central data teams. You still have them today. Central data teams that are responsible for doing complex analysis. Well, we want to bring as much of that functionality to the digital builders, the product managers, the marketers, the ones that are making decisions about how to drive impact for their digital products and make it super easy for them to find these insights without having to go through a central team that could again take weeks and months to get an answer back from. >> Well, that's what brings up a good point. I want to dig into, if you don't mind, Rachel, this data engineering challenge. There's not enough talent out there. When I call data engineer, I'm talking about like the specialist person. She could be a unique engineer, but not a data scientist. We're talking about like hardcore data engineering, pipelining, streaming data, hardcore. There's not many people that fit that bill. So how do you scale that? Is that what you guys help do? >> We can help with that. Because, again, like if you put the power in the hands of the product people or the marketers or the people that are making those decisions, they can do their own analysis. Then you can really offload some of those central teams and they can do some of the much more complex work, but they don't have to spend their time constantly serving maybe the easier questions to answer. You have data that's self-service for everyone. >> Okay, before I get into the quick customer side of it, quickly while I have you on the product side. What are some of your priorities? You look at the roadmap, probably got tons of people calling. I can only imagine the customer base is diverse in its feature requests. Everyone has the same need, but they all have different businesses. So they want a feature here. They want a feature there. What's the priorities? How do you prioritize? What are some of your priorities for how you're going to build out and keep continuing the momentum? >> Yeah, so I mentioned earlier that we just joined forces with a company name Auryc that has session replay capabilities, as well as voice of customer. So one of our priorities is that we've noticed in this market, there's a real, it's very broken up in a strange way. I shouldn't say it's strange. It's probably because this is the way markets form, startups start, and they pick a technology and they build on top of it. So as a result, the way the market has formed is that you have analytics tools like Heap, and they look at very quantitative data, collecting all sorts of data and doing all sorts of quantitative cuts on it. And then you have tools that do things like session replay. So I just want to record sessions and watch and see exactly what the user's doing and follow their path through one at a time. And so one is aggregating data and the other one is looking at individual user journeys, but they're solving similar jobs and they're used by the same people. So a product manager, for example, wants to find a point of friction, wants to find an opportunity in their product that is significant, that is happening to a lot of people, that if they make a change will drive impact like a large impact for the business. So they'll identify that using the quant, but then to figure out how to fix it, they need the qual. They need to be able to watch it and really understand where people are getting stuck. They know where, but what does that really look like? Like, let me visualize this. And so our priority is really to bring these things together to have one platform where someone can just, in seconds, find this point of opportunity and then really understand it with a show me button so that they can watch examples of it and be like, I see exactly what's happening here and I have ideas of how to fix this. >> Yeah, something's happening at that intersection. Let's put some cameras on. Let's get some eyes on that. Let's look at it. >> Exactly. >> Oh, hey, let's put something. Let's fix that. So it makes a lot of sense. Now, customer attraction has been strong. I know it's been a lot of press and accolades online with when you guys are getting review wise. I mean, I can see DevOps and app people just using this easily, like signing up and I can collect all the data and seeing value, so I get that. What are some of the customer value propositions that are coming out of that, that you can share? And for the folks watching that don't know Heap, what's their problem that they're facing that you can solve, and what pain are they in or what problem do they solve? So example of some success that's coming out of the platform, enablement, the disruptive enablement, and then what's the problem, what's the customer's pain point, and when they know to call you guys or sign up. >> Yeah, so there's a couple different ways to look at it. When I was talking about is really for the user. There's this individual person who owns an outcome and this is where the market is going that the product managers, the marketers, they're not just there to build new features, they're there to drive outcomes for the business. And so in order to drive these outcomes, they need to figure out what are the most impactful things to do? Where are the investments that they need to make? And so Heap really helps them narrow down on those high impact areas and then be able to understand quickly as I was mentioning how to fix them. So that's one way to look at it. Another use case is coming from the other side. So talking in about session replay, you may have a singular problem. You may have a single support ticket. You may have someone complaining about something and you want to really understand, not only what is the problem, like what were they experiencing that caused them to file this ticket, but is this a singular problem, or is this something that is happening to many different people? And therefore, like we should prioritize fixing it very quickly. And so that's the other use case is let's start, not with the group, like the biggest impact and go to like exactly some examples, let's start with the singular and figure out if that gives you a path to the group. But the other use case that I think is really interesting is if you think about it from a macro point of view or from a product leader or a marketing leader's point of view, they're not just trying to drive impact. They're trying to make it easy for their team to drive that impact. So they're thinking about how do they make their whole organization a lot more data driven or insights driven? How do they change the culture, the process, not just the tool, but all of those things together so that they can have a bigger business impact and enable their team to be able to do this on their own? >> You guys are like a data department for developers and product managers. >> Essentially, like we are the complete dataset and the easy analysis that really helps you figure out, where do I invest? How do I justify my investments? And how do I measure how well my investments are doing? >> And this is where the iteration comes in. This is the model everyone's doing. You see a problem, you keep iterating. Got to look at the data, get some insight and keep looking back and making that product, get that flywheel going. Rachel, great stuff. Coming out here, real quick question for you to end the segment. What's the culture like over at Heap? If people are interested in joining the company or working with you guys. Every company has their own kind of DNA. What's the Heap culture like? >> That's a great question. So Heap is definitely a unique company that I've worked at and in a really good way. We find it really important to be respectful to each other. So one of our values is respectful candor. So you may be familiar with radical candor. We've kind of softened it a bit and said, look, it's good to be truthful and have candor, but let's do it in a respectful way. We really find important that everyone has a growth mindset. So we're always thinking about how do we improve? How do we get better? How do we grow faster? How do we learn? And then the other thing that I'll mention, another one of our values that I love, we call it, "taste the soup". Some people use to call it dogfooding, but we are in Heap all the time. We call it Heap on Heap. We really want to experience what our customers experience and constantly use our product to also get better and make our product better. >> A little more salt on the sauce, keep the soup, taste it a little bit. Good stuff. Rachel, thanks for coming on. Great insights and congratulations on a great product opportunity. Again, as world goes digital transformation, developers, product, all people want to instrument everything to then start figuring out how to improve their offering. So really hot market and hot company. Thanks for coming on. >> Thanks, John. Thanks for having me. >> This is theCUBE conversation. I'm John Furrier here in Palo Alto, California. Thanks for watching. (gentle music)

>>From the Mandalay convention center in Las Vegas, Nevada. It's the queue at IBM. Insight 2014 here is your host, Dave Vellante. >>Hi, welcome back to IBM insight everybody. This is Dave Volante with John furrier. We're here with the cube. The cube is our live mobile studio. We go out to the events, we extract the signal from the noise. Carla Gentry is here otherwise known as at data nerd. Carla, great to see you. Welcome to the cube. You are a data scientists. Do you have your own company? Um, we were just talking to, uh, to dr Ahmed Bouloud from a university in um, Istanbul and he said, well, it's data science. It really, really isn't a such thing as a data scientist. And so he and I are arguing a little about it. So I said, come back and see Carla, right? You're a data scientist, right? >>Well, you know, right out of college I started with a RJ criminal associates up in Chicago. And um, that that's what we all were a bunch of data nerds in there playing around with terabytes of data before anybody even knew what a terabyte one terabyte was really big. Right? Right back when the terabyte was big data, but a, you know, gleaning insight for a discover financial services. And then, you know, I've worked with consumer packaged goods, the education, I mean it's, it's been a wonderful, wonderful career. And what's so great about this is to be able to walk around and see how much data is a part of more people's lives now than it was 20 years ago. I mean, 20 years ago you couldn't have, you know, gotten thousands of people together talking about data analytics. Well, you know, the interesting thing about what you're saying without you, you CPG education, financial services, John and I talk about this a lot, how the data layer is becoming a transport mechanism to connect the dots across different industries and data scientists. >>You guys don't like to get locked into one little industry niche. Do you you'd like to gather data from all types of different sources? Talk about that. Well, that's the thing. Uh, unfortunately, uh, we get bored very easily because, you know, we like to have our fingers in a lot of different pies. But, you know, you wouldn't want to be necessarily siloed with just one kind of information because curiosity makes you think about everything. Education, risk, you know, I'm that way. I have no walls. You know, I can, I can glean insight from any type of data. If you've got a database, uh, we can jump in with both feet. Is data is data and why is the data more transformative today in this day and age, you know, circa 2014 versus say, when you came out of college, why is it that everybody's talking about data that data is able to, to change industries, transform industries. >>What's different? Well, now the, you know, data can actually give you, you know, an insight into your customer mean, you know, what is your customer buying, you know? Um, so when you go to, you know, run a campaign or something like that, you, you're not shooting in the dark. You know, you're actually, you have a face to your customer. So you know, you can make decisions and it's not just marketing, you know, which is what I started out in, you know, trying to do increase and lift, you know, sales. But now you know, you have risk, you have, you know, data breaches. You have, you know, what keeps CEOs up at night, you know, it's not only the cash flow, you know, it's the mitigated risk that's involved. And when you're looking at your, your data and you're collecting this information that gives you a view into what's really going on so you can sleep at night and have a little bit of comfort mostly, >>well not sleeping at night, it's a couple hours of sleep. The notification when I opened CEO's and CIO's, CFO's, chief data officer, you've seen much more formal roles around data where data is real key asset. And this is awesome because it brings to the forefront the role of data. And so I want to get your perspective on this. You brought into the kind of the, kind of the trajectory of where we've come from, um, and talking about the role of software because really what this highlights here at IBM insight is okay, it's not just data per se, you know, how software that's a key part of it. So it's now also an integral part of the platforms. You have a developer angle, you have the data asset, and now you've got this real time in the moment experience. And IBM is talking about engagement a ton here. And so what's your take on all that? I mean it's, it's exciting. Certainly if you're in the data business. >>Well definitely, I mean, real time data, of course it's very expensive. Um, but it's, it's more attainable now than it ever was. Um, the thing is now is you don't necessarily have to be a data scientist to be able to go and get at your data. I mean, thanks to software tools, you know, like IBM, they give you that benchmark, you know, the, these tools, uh, where you can use BI and things like that. To be able to get a view into your business. And you know, it's not just for, you know, your analytical department anymore. Um, so I think it's what it's done is it's actually made it more attainable now. You know, it was like people looked at data wagon back then, Oh, and it was so scary, you know, but now it's, it's bringing it to the forefront to where we can make decisions. We can want our bitter, our business better. And like I joined forces with a repo software years ago to look at the supply chain. Now when you talk about that, that's what keeps the lights on. But you're only as strong as your weakest link. So when you're working with third parties, you have to make sure that everything is going smoothly. So >>I want to get your take on a couple of things in. He chose SA was on earlier and she's an awesome guest. She's been on many times. She's dynamic and articulate and super smart, brilliant and beautiful. We love talking with her. She said, I asked her what are the top three customer issues? And kind of a double edged question. She said three things, customer experience, operational assets, AKA the supply chain, and then risk security and governance. And then we weaved in context computing and then cognitive. So let's break that down. So customer experience, internet of things is a data play, you know, probes and sensors and machines certainly get that >>analogies. People are things. Yeah, well you know, here's the thing that you think about. Data. Data is a person that record that you have in that database equates to a real live person and you want to, you know, you're not going to be friends with your, your customers, but you want to know more about them so that you can serve them better. Um, you know, for me the biggest thing is, you know, people will go out and spend millions of dollars on a database but not necessarily know what to do with it. So it comes down to what question are you trying to answer? >>Yeah. And the infrastructure piece is interesting because you want to have that agile flexibility, which is kind of a buzz word amongst vendors. Hey, be flexible. But there is meaning behind it. Right. So context computing is relationships across entities. The streaming stuff is very, very interesting to me because now you have streaming data coming off of devices and again brings up the real time piece. So making sense of all this means it puts it in the forefront. >>And what you can do with that data is if you do have a client or a customer and you let them link in socially, like log in through Twitter or LinkedIn or Google, Facebook, now you can append that social data. So now you, you've got an ideal, you know sediment and you know when you're positive you it's first party data. Yeah, exactly. The Holy grail of active data is first party data. Exactly. >>Cause we'd love the crowd chatting and love people. The logging in and, and thanks for, by the way, for hosting the crowd chat with Brian the other day. It was really fantastic conversation. My pleasure. Let's talk about cognitive because this brings a human element of it. And one of the things we've been teasing out of the past couple of shows we've been at around big data is the role of the developer where the developers in the old days from even going back to the mainframe days, cold ball, they were adding in these rooms, almost like almost an image of coders in the back room coding away. But now with the customer experience front and center with mobile infrastructure, the developers are getting closer to the customer experience. And so you're seeing more creativity on the developers side with the use of data. Could you share just observation, anecdotes, things you've been involved in that can tease out where this is going and how people should be thinking about it? >>Oh, do you know 20 years ago if he tried to show someone and graft with, you know, 16 different things at one time going on, they were like, that's messy. Now you can actually find the sweet spot or where everything interacts. So you know, when you're talking to an artist, a digital artist who's working with data and giving that picture, that's exciting for me. And going back when we were talking about cognitive computing, when you're talking about the Watson on ecology, that's exciting. Yeah, that's the highlight of it's almost magic. It's almost like black magic, this Watson stuff and people are really just now getting their arms around that and that is essentially making sense of the data, but that's the thing. See, it's no longer magic now. That's what they thought 20 years ago. Poof. People like me, they kept in a little closet, you know, and then our office and they only came to Moses when they needed something. >>Now we're an integral part and we actually are in the business development meetings and we're a liaison between the it department and the C suite. One of the, one of the things that it's interesting about your role as not only you out in the field doing some great work, you're also an influencer here at the IBM influencer program, so I want to get your take on this balance between organic data and kind of structural data. Organic data means free forming unstructured data and then existing data that comes in that's rigid and structured because of business processes. And I get that is data warehousing business has been around for years, right? It's intelligence, it's all fenced in, all structured. But now you have this new inbound data sources coming in, being ingested by these large systems, data changes the data. So you now have a new dynamic where latency, real time insights, these are the new verbs, right? >>So talk about that role, the balance being organic data and the structure data and what the opportunities are. Well, the wonderful thing about, you know, now that unstructured data was scary way back in the day. So now it's not so scary, you know, now we can actually take this data and make business decisions, but uh, you know, like social data and things like that. When you can add that in a pin that and get to, you know, what we all want is a better view of our customer and to be able to, you know, do better business with them. Like, um, like supply chain management and things like that. I mean you're, you're looking at open people, you know, collecting information from varying sources and this all has to be put together. So I think they mentioned earlier this morning how 80% of it is we're data janitors cleaning up this, that and the other. >>Whereas what we really want to do is, you know, glean the insight from it. But I think, uh, the tools these days are making that much more easier no matter what the source is that we can actually put it all together, what we used to call the merge Burj back in the old days. It takes weeks to do the merge purge and yeah, who all here knows what a DLT is trying to solve this problem for a while with traditional technology 17 years. So let's talk about, you know, the, the promise of BI and the traditional data warehouse 360 degree view of my customer, real time information. And that's what it's about. It's about drilling down predictive analytics, all these promises. Did the data warehouse live up to those promises in your view? Well, initially, maybe not, but you know, things are, it just seems in the last few years that people have had an epiphany of how this is really adding value to their company. >>Now back in the old days, they all knew that, you know, insight is wonderful, but now you can see it visibly showing signs actually making a difference in company so they can keep an eye on everything that's going on. Now, going back to what keeps CFOs, you know, up at night with the risk and stuff, there's still always the risk, but at least now you can get a little better handle on it. And thanks to the age of technology and the data that we have accessible to us today and the tools we have available to us today. It's, it's made a dramatic change. What are the technology catalyst? Is it do? Is it no sequel? Is it, what are the, what are the tools that are sort of the foundation of that change? Well, I think always the, you know, the new tools and making it so that you don't have to go out and learn SQL. >>You don't have to be a programmer, you don't have to, you know, go to college for four years and learn mathematics and engineering to actually be able to work with this data. So thanks to, you know, tools like had it been other tools. I mean you can really sit down and glean insight without having to write one single line of code. So the things we're getting some questions in the crowd chats, um, um, at furry, at data nerd, what are the key things that are messy, scary right now for CEOs and CFOs? So things are becoming less scary. What is the scary things right now? Oh, the scary thing is the breaches. You know, when you hear about target and these big names, you know, people getting access to your, your credit card data. That's, that's scary. So, you know, we've got to really try to lock down that risk, you know, and I know everybody's scrambling scratching their head, figuring out how we're going to keep these breaches from happening again. >>Yeah. Big data solves that. I mean you have big data technology, which is a combination of machine learning, streaming where you're getting massive surges of data coming in to these ingest systems where you can apply some reasoning to it, some cognitive, some insights to look for the patterns and that's where machine learning shines. Um, how do you see that aspect of machine learning and these new tools affecting that kind of analysis? Will I see it opening up a lot of different doors for a lot of different people and making a difference because, uh, you know, everybody knows that data is important, but not a lot of people know how to deal with it, especially when it gets into the zettabytes of data. When you have tools, you know, like the IBM tools that can handle this type of load and be able to, to give you, you know, instantaneous information. >>And, and like what we saw this morning where, uh, like risk, I mean an oil and gas industry, you know, you, you have to worry about, you know, as someone going to get injured on the job and they showed the the center, whereas she walked toward it, it went off. I mean the internet of things, being able to let us know in real time if there's a danger, you know, to personal life or to your database and then predictive to be able to say, well this is what we think is going to happen in the future and to be able to move and act on that. It's a very exciting time. You mentioned IBM, so obviously is a leader in here, >>Jeff Kelly's report shows IBM is the number one big data player. But big part of that is IBM. So big, right? >>Well and you guys were around a long, you've been around a long time. You guys were playing with big data way back before. Big data was big data. So yeah, we guys, us guys, yeah, well social, social data, >>those guys, right? So we're not all right, but so, but, but so you bring up IBM, a lot of people have a perception IBM big, hard to work with, but you're, >>but that's changing. So talk about that change. What I'm excited about is the Watson's analytics. I mean that in itself right there and made me sit up and, you know, get excited about the data world all over again. You know, to be able to excite you about Watson analytics platform? Well, I really like, uh, the, uh, the oncology, uh, Watson, um, they had the, the one for the, uh, not necessarily for the police, but for the, uh, the crimes. I mean, in real time, if you can see that a crime is about to happen and you can prevent it, or if you see someone's health is failing and you're able to step in. And that's why over there, earlier I was talking about IBM cognitive abilities can save lives, you know, so I mean, my, my mom passed away from cancer, so, you know, the, the, um, oncology Watson was very exciting to me, but it's gonna make a difference. And I think the thing is now is that how it's changed is to make them user friendly where you don't have to have a data scientist or an analyst to come in. You know, they talk about how expensive data scientists are. Now the reason I opened my business was to make it affordable to small businesses, you know, so although you know, people look at IBM and think it's scary, I think they're going to see now that the, the direction that they're moving is becoming more user friendly and more available. >>So Carla, I wonder if you could talk about how you engage with clients. So you mentioned small business, right? Cause you have a lot of, a lot of businesses, small midsize companies don't have the resources. Right? Um, so where did they start? Did they start with a call to you and, >>well, uh, most of the time it's a call where, you know, we spent all this money on this database and we still can't get what we want out of it. So it comes down to what question are you trying to answer? I think that's the most important thing because that directly deals with what data that you need. And if you don't have it internally, can we get it externally? You know, can we go through open source, can we get census data? Can we get, you know, work with hospitals and doctors and things like that and use this to be able to feed this information into them to make a difference. >>So what do you do? I mean, are you so CEO calls up small companies, is that got all this data? It's unstructured. I get some social data. I get my customer data trying to make sense out of. I'm trying to figure out, you know, who's >>ready to buy, where I should be, you know, focus my products. Uh, and I got all this, this, this date. I don't know what to do with it, but I know there's some gold in there. I know there's a signal in that data mining, right? So how do I get it? How can you help me? Well, it's gap analysis. First off, I would come in and I would sit down and first of all, I need to see what variables you're collecting. Uh, if you're telling me you you're collecting your name, address and phone number, but you want to do a predictive model, we can't get that. So, um, you know, the question that you want to answer is, is most important? Are you wanting to increase your sales? Are you wanting to get your, to know your customers better, to be able to service them better? >>Like in the healthcare industry, you know, you really want to know what's going on health wise, you know, so, uh, I sat down with them when we do a gap analysis, what are you missing? What do you have? How can we get it? What do you want? Where are you at? Yeah. And here's, here's what you have, here's what you're missing. How do we get at that? And that's oftentimes starts with data sources. Exactly. So then you go get the data sources and then more than what you do, well then we merge it back in. And here's the thing, you have to have that way to connect them. You know, the relational databases will always exist to where you have, you know, client information here and you've got other information over here and you have to always bring that back together. So, um, you know, it's a wonderful time. >>You're a data hacker in a sense, right? Is that fair data nerd in a complimentary way? I mean hacking is about exploration. Yeah, exactly right. So I mean, so you have the skillsets as a data scientist to pull all this data together, analyze it and well, you're going to bring in an external source and then when you bring it externally, you want to make sure that you can match it back up. And now that's the important and without a unique quantify or how do you do that? And that's why when you see databases with all these little arrows and everything pointing to where things belong, I mean we have to be able to pull that in to make decisions. >>Yeah. We were talking with frons yesterday to another influencer. We were talking about this particular point. He was ex P and G back in the day, which is very data-driven. Of course, they're well known for their brand work and certainly on the advertising side, but they're, they're quant jocks over there. They love data. Their data nerds over there, they're kicking out on data. And he used to say that the software would cut off data points that were skewing way outside the median. And so they would essentially throw away what are now exploratory points. So this kind of brings up this long tail distribution concept where, okay, you can get the meat of what you want in the head of the tail and distribution, but out into the long tail is all these skew data points that were once skew standard off the standard deviation that are now doorway. So, you know, we're old enough to know that that movie with Jodie foster with contact where they, they find that little white space, they open it up and there's a, a huge puzzle. That's the kind of things that's happening right now. So exactly >>the same thing. Well, yes, yes. I mean, you know, the thing is, uh, you know, a lot of people don't necessarily have the information that they need. So they're seeking it, you know, when they're going to what Avenue, where, where do I go to get this data? You know, and thanks to open source and things like that. You, you know, we've been able to get more information and bring it together than we've ever been able to do before. And I think people now are more open to analysis where it's not necessarily a dirty word. It doesn't necessarily mean you have to go out and spend $300,000 a year to hire a data scientist. You can sit down, you know, and look at what you have and uh, someone else mentioned that. Take the people that you have that know what's going on with your company. You know, they may not be data scientists, they may not be analytical, but they have insights they have. >>There's more of a cultural issue now around playing with data and an experimental sandbox way where you don't need to have the upfront prove the case. And then pre prefabricated systems you can say, I'm going to do some stuff in jest, for instance, bringing in data sources and play with the data. >>Well, and you mentioned, you know, outliners I mean everything when, when you look graphically at data, you expect everything to fall within this little bubble, this, you know, this thing. But when you see, you know, all these outliners going on for me, usually that means a mistake. Okay. So, and if it's not a mistake, it's something that calls attention. So it's definitely not something you just want to toss aside >>talking about creativity because creativity now becomes, you know, uh, uh, an aspect of the job where you gotta be creative, where it's not just being the math geek or being super analytical and you have to kind of think outside the box or outside the query, if you will, to do the exploration. What's the role of creativity in the new model? >>Well before, I think that we always thought of ourself as just being, you know, matter of fact, you know, just the facts please, you know, but now, you know, you can look at things visually and see, you know, and it is an art form to be able to find that sweet spot in the data. And um, you know, before, you know, years and years and years ago when you would take something like that to a CEO, he would say it was messy, you know, so now you get that creative side where you can actually make things visually attractive. And I think that's important to people too because it's not just data, it's the way you present it. >>It's also the mindset of understanding MSCI is a good start, start with messy and then versus getting the perfect answer. As we were saying, using it with pop-up Jana earlier about, don't try it at the home run right away. Hit a few singles. He's in the baseball metaphor given the world series going on. So totally awesome. Um, but I want to get your final thoughts as we wrap up the segment here on the practitioners out there. What's, what should they do? So there's an approach to the job now, right? So there is a shift and inflection point happening at the same time. What advice would you give to folks out there who say, Oh, I love Carla's interview. I want to do that. I just don't know where to start, what to do. How do I convince management I want to be, I want to get going. What do you, what would you share for advice? >>Well, I'm sure it's the platform. I mean, you know, think about the foundation of a house. Now if you have a strong data foundation, you can build on that. It's just like your house. If you have a weak foundation, your house is going to tumble down. So if you have a strong, you have a strong foundation or with your data and everything is built right now. When I say built right means, what are you trying to do? What are you trying to accomplish? You know, if it's risk, then you need to be, you know, looking at those, those factors. You know, how many people have been hurt? How many of you people been injured? You know, how many people died? You know, I mean, how many breaches do we have? You know, so it starts with the question, what is it that you're trying to accomplish? And then you go from there and collect the right variables. So don't wait, you know, a year later and call a data scientist and going, I've spent, you know, millions of dollars on this. I'm still not getting what I want. So think about an initially in the setup and you know, be involved, involved your analyst, involve your data scientists, make sure that they're in your business meetings because we're the liaisons between it and the Csuite. >>Yeah, and that's the key roles team as a team, that person really is collaborative. We heard from a med earlier pair programming pair, not pay eggs in an accent, pair programming, work in pairs, buddy system. This is really a true team effort. >>Well, I always said, you know, I am a team of data. Scientists can write programs, we can glean insight, but the team part has to come from working with it and working with your C suite. So very much agree. It's definitely a team sport. >>Carla Gentry, owner and data science analytical solutions influencer here at the IBM special presentation and second experience, second screen here in the social media lounge. Really doing a real innovative social business. Again, activated audience, you're an influencer, but also you're really a subject matter expert. Thanks for coming on the cube. Really appreciate and thanks for hosting the crowd. Chat with Brian Fonzo is really good content now. This is the cube. We are live here in Las Vegas. Extracting the ceiling from the noise, getting the data and sharing it with you. I'm John Frey with Dave a lot there. We'll be right back after this short break.

okay welcome back live in Las Vegas is the cube ibm's information on demand conferences q exclusive coverage SiliconANGLE will keep on here live I'm John furry the founder of silicon Hank I'm Joe mykos Dave vellante co-founder Wikibon org our next guest is a Fred Balboni global leader business analytics optimization IBM GBS global business services you know obviously big data is powering the world I mean just can demand for information and solutions is off the charts afraid welcome to the cube anything there's a services angle here where you know services matters because one in the channel partner is this good gross profit for helping customers implement solutions that they have demand for so you've a combination of a market that's exploding with demand people know it's a game changer with big data analytics cloud is obviously right there in the horizon in terms of on prem of Prem then you've got now see mobile devices bring your own device to work which is thrown off more data okay and then people want to be in all the different channels the social business so you know CIO to CEO says hey this new wave is here if we don't think about it now and get a position and understand it the consequences of not doing anything might be higher than they are so we've heard that how do you look at that and what are you guys doing what's the strategy give us a quick update and from from GBS i think that the to make this successful first of all it services is important it's the last mile you know that means the point you may it's the last mile and without without that you cannot ever deliver the value the the really interesting challenge that every executive faces is you need to be able to we can easily get our head around big data technology and I shouldn't trivialize that but you can go and understand the technology what's possible in big data you can also get your head around analytics and the analytics algorithms and the kind of insights that can be drawn from that the real challenge is how do you articulate what's kind of possible to a client because many of the use cases are very niche and so clients often say yet that's right but it's big it's possibly bigger than that yeah that's right it's possibly bigger than that the other issue or the other challenge to get we've got a hurdle we've got a jump on me articulate this to the businesses clients businesses think in terms of process you don't think in terms of data you know you don't go talk to a CIO CEO and say you know tell us what's the key attributes of your customer and they don't think that way they can talk to you about servicing a customer or selling to a customer or managing customer complaints so that the processes but the data it's a tough thing so the first part the services is so crucial in this is being able to articulate the value of analytics and big data to a client in the businesses terms so it becomes a boardroom conversation kind of so that's that gets the program started and then quickly being able to fill in with use cases because clients don't want this to be they don't want to start from a blank sheet of paper and they don't like going to give me some quick wins here so it's kind of those timetable what kind of timetables mmmm back in the 80s 90s when client-server rolled out it was months and months yeah project management meetings roll out the Oracle systems roll out the big iron now I mean I'll see maybe shorter spurts little different hurdles what's the timetable only some of these horizons for these quick wins okay so project implementation I come on now let's let's know it's it's I think that that we're measuring project implementations in weeks I think cloud-based technology allows us to provision environments on the order of a couple of weeks and that used to be on the order of five to six months so I think that's going to that accelerates everything and that also allows you to do a lot of a lot more speed to value get applications or analytics use cases up there much more rapidly one two as you start to build these portfolio of use cases and if they're built on acceleration tools I mean acceleration so you've got those code sets that are already there that you can add you can jump on top of I mean you can get these use cases up there in 6-8 weeks we have one we have an example a really large major company i'd rather not i'd rather not because it's not externally referenceable but a really a significant client that had on the order of more than more than 5 million discreet customers and doing detailed customer analytics on their customer base against their products and we were able to get that baby up and running in three and a half months now that two to three years ago traditional logic would have told you that was a nine to twelve month project and by the way you know ten years ago that would have been a 18 to 24 month project yeah so I think that yeah we're moving much more rats the expectation now too I mean the customers realize that too right the absolute not but but there's one thing I want to talk about this it's still this is the one thing that if you'd asked me what's most important this speed thing allows you to go rapidly to places but you you better have a navigation roadmap on where you're going because if you're going to do all kinds of little code drops that's great but you want to make sure you're getting leverage so you're going somewhere so therefore there's a scale but this is where roadmapping becomes really really important for every the technology side of the business you have to have a technology roadmap the other thing that's really important out of this is if you don't let's use the client-server example you used because this kind of has a you know we've all been here right here we've all lived seen this movie before yeah if you if you don't in the build this roadmap another thing that happens do you remember when CIOs finally said okay I'm taking control this client servicing sure what do they end up with they ended up with all these departments of computing in the costs work going astronomical so if you've got a road map you can also address the issues of managed services because you don't the least thing you want to be is having all these data Mart's that are scattered everywhere because you get no economies you get no economies of it but a cloud would bring you you get Noah kind you get no economies and being able to do that and you end up having to have all these maintenance teams you know that maintenance and by the way analytics by its nature has constant maintenance little adjustments and changes you're getting new economies of that because they're all managed is discrete units so therefore there's a lot to be as you build this roadmap you've got to think about the managed services environment as well so Fred you talked about earlier clients don't think in terms of data they think in terms of their business process is that a blind spot for clients because there are some companies Google for example that does think in terms of data in your view should clients increasingly be thinking in data terms or does our industry have to evolve to make the data map to business process I actually I kind of just take it as a thick I don't I don't I don't choose to question why I just accept it um i but i would say i which i would say customer's always right I just I just think the industry i thought that definitely but i think just the industries at a stage where you know we've always you know back in the old days of you know i'm going to show my age here but you know the procedure division in the data division oh my god looked at all and and and we you know the procedure division is where you actually did all the really and i think if the reason is we got understand the paradigm under which modern computing was created I don't to be like we go into history lesson but the paradigm under which modern computing was created was that we use computers to automate tasks so we've always taken this procedural approach which went then we went to process reengineering and that became a boardroom conversation so just I think we've conditioned over the last 40 years businesses to think about using technology to gain business efficiency they've always thought in terms of process so that's why this data element yeah companies like Google founded on analytics clearly have got a whole different headset in a different way to approach these which gives them a built-in bias when they address the problems they've got in their businesses sure but you don't come a decline saying hey you got to rethink the way in which you look at data you come in and say let's figure out how we can exploit data in your biz erect what we do it two ways we do it two ways first of all let me not dress let me not dress monton up as lamb at the end of the day it's its data its data okay now the question is how you articulate that and it's twofold we tend to I like to use a metaphor to describe the data so if its customer that the metaphor we've been using recently is DNA DNA strands to be able so you use a metaphor that there's a language that the business can relate to and you can create a common language very easy one in that way you can have an account because you're never going to drag a CEO into your fourth normal form data model so so therefore you've got to you've got to talk a language one number two you talk about as a collection of use cases so you use use cases as a vehicle to have the process conversation and because with the use case you also can talk business outcomes benefits and you can tell kind of a story you don't have to drag them through the details of the process but you can tell them a story whether it's you know I if you can understand called detailed called detailed data records and the affinities you can understand the social networks and therefore you can reduce churn within your telco customer base as an example quick but if you follow I do so you talked about its little use cases and they begin to understand wow what's possible and then you talk about their data as a DNA chain and they get I got it I actually need to get the DNA chain if I'm going to actually think about think about my customer base or my product base or whatever the lingua franca the business is still the businesses language it doesn't result of data but data can enrich the conversation in a way that can lead to new outcomes the data in rich's the conversation when you talk about the business outcomes that are created as the part of the use case well it's like a three third order differential equation but i go back i watch this yeah i just go say your tweet your epic soundbite machine just can't type fast enough on the crowd chat it's good for good for Twitter viewing yeah I've just opened a Twitter account please look me up I'm looking for friends I promise to start posting you got people watching all right all right so so in terms of customers right give us a little bit peak of some of the customer responses when you when you open the kimono show them the road map you know the messaging around on IBM right now is pretty tight here at IOD last year was good this year is better you look really unified face to the customer when you show them the road map what's the feeling they get it they feel like okay I got some trust IBM's got some track record history do they is the is the emotion more of okay where do I jump in how do I jump in there doing it and this little shadow IT going on all over the place we know with Amazon out the area so so when you're in there you've got to have these are conversations what do they like and what's that what's the level of response you get from CIOs and then also the folks in the trenches so there's always a question which there's a couple of questions first of all is how can I get how can I get value from this and that in that and that's you know a I'm tightly coupled to my existing transaction processing which is kind of like if you will call that turbocharged bi and and which is which is where so many people have come from is this turbocharged bi environment and listen that's an important part of your reporting business you need to do that to keep the wheels on the question is as you move to this notion of analytics giving you great insight then then you've got to say okay I need to go from turbocharged bi to really augmented components so clients I'd say there's a large there's a large group of people that are right now moving from turbocharged bi to the notion advanced use cases so there's this some disco a large discussion right now how do I show me do use cases by which i can I can rapidly that would be advanced how to linux up the calling advance limit well no we have well 60 60 use cases industry-based use cases that we as a services business put together on top of that we have about seven or eight key code fragments that we uses accelerators I mean we call them wink we call them assets and we just them up as accelerators but their code fragments that we bring to a client as the basis that we put on top of the the blue stack of technology to actually get them a speed to value because we really want to be able to get clients up and running within this notion of non idealities it's like literally being best practices in the form of technology to the customers well you're on an IBM thing I mean dare I called an application no I wouldn't dare call it an application we're not in that business but the point is is that it is it's starting to feel like an application because it's really moving down these unreal integrated solution is really where we going it's an accelerant this code correct so it's leverage the economies of scale is every success breeds that's exactly it more and then on top of that we would have that just don't throw a few other things that we do to accelerate these things we actually have five what we call signature solutions which is services software together with a piece of services code coming together to solve a problem we've got that round risk and fraud around customers I mean some specific very narrow things if somebody wants to you know because often IT departments they want to buy something they want to buy something they don't want to go down the parts they want to buy something and so fine here's a package solution let's go buy something um and then last but not least one thing we haven't talked much about but I always like to throw this out there because I think this is one of the things they and we didn't talk about it much in the main 10 or any better sessions but let's not forget about IBM research I'm really proud to report to you now since we started this category we've done 61st of a kinds with IBM Research so this is about client says I've got this problem i think it's unachievable i cannot solve this problem you know help me map in my oil exploration like things that are considered big problems big problems let's let's apply this group that does patent factory you know that IBM is but 15 years in a row let's apply those people to my our problems and we have 60 we have 16 so we do about 15 to 20 a year so it's not like we like we're not cranking these out like I'm hundreds of thousands of licenses but it's where basically our services business our software business and IBM Research go work on solving a client specific problem you heard Tim Buckman this morning when he was asked to know why IBM that was said IBM Research was the first answer that's right he gave we talked to him about that on the cube you know in his is insane me as a customer and we you know we always love to hear from customers I mean you know the splunk conference just had was just last week as an emerging startup because probably well aware of those guys they have customers that just say just glowing reports you get to the same same set of customers you know he is someone of high-caliber at the command and control in his healthcare mission and he's automating himself he it's and essentially creating this new data model that allows it to be pushed down to be listen you've got to do this and I'll tell you why you remember the the governance discussion is it was well I'm most excited about is the governance discussion five to eight years ago was an arcane discussion available of data modelers and like what do we do the governance discussion is quickly moving into the language of our business people and the reason is because they're beginning to do you remember the days of accounting systems when they say we want our accounting department to focus on analyzing the numbers and not collecting and forming the numbers well we're here again and if you've got good data governance you can focus on creating the insights and determining what actions you want from the insights as opposed to questioning the numbers and questioning the validity and the heritage of the number the validity and the heritage of the numbers and in this place everywhere yep financial services companies are the most stressed about it because the validity and heritage is required when you want to prove a compliance to a federal statute yes but it means everywhere if you're a consumer packaged goods company and you don't believe that sales are down in a certain market or a certain chain store first thing they do is they start challenging the numbers if you have good governance you can now start that you can now start to trust these systems of record but let's talk about data quality data quality but it's also the governess in the death of mindset is much broader iteration right how we said the first you know that folks from the nonprofit said you want to go on the record but he's basically saying I'll say basically when you put stuff out when you package and then bring it out it still might have some flaws in the data quality but it's the iteration is transformational but once that's in market saying that's changing he things prepare pre-packaging data and then bringing it in is not the better approach but I want to ask you about the your what you just said about this governance conversation that is date the core of this debate around the data economy what is the data economy in your mind given what you do the history that you've lived through we've seen those movies now the cutting edge new wave that will create new well for new ways change from transform business all that stuff's great but what is the data conn what does that mean to business executives that they're focusing on outcomes is is it changing data governance is it changing the value chains is it changing what's your thoughts on that the data economy is about discovering those points of leverage that that the data tells you that your instincts don't the data tells you that your instincts don't one of my favorite stories three years ago four years ago we were called in and clients said this is my problem the going and problem was I got to take 200 million dollars out of my advertising spend budget two hundred million dollars out of my advertising spend was he's a retailer end and the problem is is out of my 600 million dollar advertising budget the problem I have is also have all kinds of interesting theories and models that my agencies have told me I'm not quite sure do I just take 200 off the board across the board do I take 200 off to minimize my risk just spread it around how do i how do I manage the process and what we actually did was we built a super super set of sophisticated analytics which tied to their transaction systems but also tied to their social media system so we also understood and what we did was we were able to understand which customer cohorts responded to which media types then we added one more parts of the model which is we understood the trending in the cost of free-to-air cable radio internet all the different media types and as we looked at the cost models of them and we understood which customer cohorts responded to which media types we suddenly realized that they were super saturated in certain media types they could like doubled their spin and they wouldn't got want any lift in the advertised in their in their sales what we did was we got 200 million out of their budget and increase they got 300 million incremental sales that Christmas season because we help them get really smart about the play let me tell you I tell us privately i maked media buyers look at me like like I'm like a pariah yeah but but it is actually really you know really started to rethink now there's just a really great example because I think we've all can relate to that but that's the data economy where you find these veins of gold in these simple correlations and from that simple correlation you can instantly go and your business you can get the lift listen I can get five percent I IBM get five percent ten percent lift in some small segment business I've got the volume that's going to make a significant difference to my share one small piece of data could open up a window kind of had with Jodie Foster we would contact words like one piece of data opens up a ton of new data I mean that totally is leverage and it changes the game for that customer and and that to me is that is the guts of the data economy identifying those correlations and and what we're finding is our most recent study we just released it here the thing the IB the IBM Institute for business value big data and analytics study w IBM com it's the Institute for bit I bv study on big data just released and said 75 percent of all companies that are outperforming their peers have said big data analytics is one of the key reasons and the human component not to put are all on machines it's really about it's an ardent science its a mix of both the math and the human piece well you know there's this notion of not only do you create the insight but you've got to take action on the insight you know it's not enough to know if I could predict for you who's going to win tonight's basketball game you still got to place the bet you still have to take action on the inside and so therefore this notion of action to insight is all about trust trust in the insight trust in the data and trust in the technology that the business trust the technology and it's until you take that leap of faith remember when the Indiana Jones movie when he liked the leap of faith and you've got to like to step out and take that leap of faith once you take that leap of faith in you suddenly have trust in the data so that's that trust to mention and that's a human thing that's not a that's that's not a that's an organizational thing that is not a lot of technology in that one okay Fred we gotta wrap up i'll give you the final word for the folks out there quickly put a bumper sticker on iod this year's and put on my car when I Drive home what's that bumper sticker say for this year it's not all about the technology but it starts with the technology ok we're here live in Las Vegas we're going to take about that bet that was going to win the games and I will be the sports book later this is the cube live in Las Vegas for information on demand hashtag IBM iod this tequila right back with our next guest if the short break exclusive coverage from information on demand ibm's premier conference we write back the q