 Pendo, Florida, extracting a signal from the noise. It's theCUBE, covering Pentaho World 2015. Now your host, Dave Vellante and George Gilbert. Welcome back to Pentaho World in Orlando, everybody. This is theCUBE. theCUBE goes out to the events. We extract the signal from the noise. Rosanne Secone is here as the CMO of Pentaho. Rosanne, welcome to theCUBE and congratulations. Second year, great event. Thank you so much, we're so delighted to be here. So let's give people the stats. You talked in your keynote about who's here and what the breakdown is. Five or 600 people here. Give us the rundown. Yeah, absolutely. I mean, one of the things we're so excited about is having such a nice breath in our audience this year. They're from 27 different countries around the globe. We're representing small companies to large enterprise companies. Pretty much every image is represented. One of the other things I love about the stats for this conference is about... That agenda for those different audiences. I wonder if you could talk about that a little bit. To set different agendas for companies, to enable them to engage with their customers in a much more intimate way than they've been historically able to do. And it's gone from pilot projects to business value-driven use cases. And so we really wanted to talk about the power of big data at work because it's about implementations. It's about doing things differently. It's about showcasing user examples of big data and analytics. Yeah, and you guys had a big theme in the key notes this morning about operationalizing analytics. And it's clear, Pentaho's not an out-of-the-box BI tool. That's right. I love the saying, a fool with a tool. It's a platform. And it's been built up over a decade plus. 11 years old, just a year older than Hadoop. Which is kind of ironic and interesting. So, okay, so you're not an out-of-the-box BI tool. What is Pentaho? So Pentaho is an end-to-end integrated platform to enable companies to manage their data. So take data from pretty much any source you can imagine, use that data, manage that data, and then expose the information for analytics. And a lot of our best use cases are embedded analytics. Because most companies want to embed analytics in a workflow, in another application, in a portal. They're really trying to create an analytic capability that enables a regular person, a business person, to make decisions for their business. And it's really, the data is 80% of the challenge for people is getting the data prepared in a way where you can present that data in various formats and enable that decision-making. And George, we saw a great example of that with FINRA this morning. Go ahead. Absolutely. Yeah, and I was going to ask, I mean, you span, it's sort of like a data value chain all the way from the raw materials to the finished analytics. And some have taken the approach of trying to baloney-slice this. What do you get in return for providing that end-to-end experience? Yeah, I think our founders of the company rooted this company in reality and say, what happens out there? And I don't know too many companies that have nice, neat data all ready to go but all you need is an analytics tool on top. Most of the challenge is in trying to get the right kind of data sources prepared and into that flow. So, if you think about that end-to-end flow, the value isn't in just the analytic piece, but it's in the flow, the modeling, kind of the inner relationships of the data, the context of the data, and then bringing that sort of analytical data pipeline to bear on the other side. There's something, that's something in there you said that's really significant because in the data warehouse, it's already curated. So, you can focus just on the presentation and it sounds like when you're in the data warehouse, because it's so much more raw, you really need to sort of integrate backwards into making, into tools that'll make that experience of refinery, of refining the data more powerful. I think it depends on the use cases. So many customers we talk to, it's a blended data story. They don't just go to one place to access that data. They'll go to Hadoop, they'll go to Mongo, they'll go to their enterprise data warehouse, they may have a data mark, they may have a data stream coming in. So, they're trying to figure out how to blend that data and provide context around that data, right? So then when they're providing meaning to that data, it's more useful to the person who at the end of the day is in charge of the decision. And so, to us that philosophical value chain was really important in what we built on our platform. And to go longer, the idea that you're going all the way back to the sources, which is specifically different because in a data warehouse, you don't need to as much. Right, well I think it depends on what a customer's trying to do with the data. So there's a lot of problems in this space. Thank you guys for that. You mentioned 70, 80%, whatever the number is, of the time it's spent on trying to do stuff with the data, get the data quality right, get the data in, cleaning it, et cetera. And what a lot of customers are going to just throw it into the data lake or data ocean or whatever term you want. No schema on right will figure it out later. The problem is figuring it out is hard. The other problem is, yeah, EDW, critical, tried and true, well understood, but it's insights for a few. So the challenge is how do you get insights in the hands of many that can actually act on it? Fundamentally, you're solving those two problems, correct? I think so. In fact, what I think we try to do on our analytics side is focus on what's the ultimate decision making power that's needed and what's the use cases really that's being driven by that. And so what you'll see a lot of times is you might have data analysts who want to go deep with the data, they want a tool that goes deep, that can provide deep context. They may be able to try to find meaning in the data, but once they find meaning in that data, they want to operationalize that to everybody else. And they want an easy consumption model for those types of reports or those types of applications. And so to us, the spectrum of how people use data is so broad to encompass all the different members of the audience that want to use that data. So you have your data analyst that might go really, really deep, you'll have a business analyst who might go a little differently and then you're going to have business users like myself. I'm very metrics and operational driven, but I don't want to look at every piece of data. I want to see something that I can make actionable. So this is the second Pentaho world. And subsequent to the first, you were acquired by Hitachi. How has that changed things? Any surprises that you see? Yeah, it's a great question. You know, when you, the first year you read a conference at a new company, you're just trying to think about all the basics, right? You want to make sure you get a good audience. You want to make sure you have great speakers. You want to have really good track sessions, really kind of the basics. And then in the second year you're looking at, what can you do a little different? Bring a life stream to the environment. Bring more people into that conversation that you're having. You know, we wanted to add an internet of things related sort of topic. And when, what's kind of cool is we, I got to know Hitachi and the HDS team based on the Pentaho world last year. So they were here last year. They were part of our advisory board. They got to see sort of Pentaho in action in terms of sort of the bringing big data to life. So we developed a lot of great relationships from that. And then the acquisition happened. So there was really a tight alignment in our vision from day one, which enabled us at this conference to create a whole other track session around internet of things related solutions that's related to big data, but then obviously related to other things, right? You have to have the right vertical knowledge from a consulting organization really to kind of lock down what those solutions might look like. There's hardware that's kind of associated with some of those appliances. So they really bring to the table the extensions around some of the core that Pentaho has. And so what you'll see at the conference is places where the Hitachi capabilities really complement that core analytic platform that we bring to the table. So it's had a direct impact on the brand. So it's interesting that the internet of things mean three, four years ago, nobody was talking about IoT. So it seems to me, I think Chris said, look, strategy shouldn't change. Strategy should be consistent. So the strategy is, look, there's all this data. Somebody's got to deal with it. That's us. The founders of Pentaho clearly had that vision and then whatever wave of data comes, you want to be able to ride it. So my specific question is, how has the brand and the brand message changed? And what do you want the brand to stand for? Moving forward. So to me, the Pentaho brand still stands for, we always talk about it as the future of analytics. So we want to accommodate in our brand the new waves of data that are coming in into play. What are the things our customers are trying to do that they've wanted to do for 30 years, but the data never caught up to the, and the technology never caught up to the use case. So people have been talking about 360 degree view of a customer for 20 years, but now they can do really personalized experiences with more real time related experiences for customers. And the data, the analytics are enabling that and the new technologies are enabling that. The decision support world, George, and the EDW world have been promising that for years. And let's face it, it did not live up to that promise. Now you're up. Yeah. And you think about it, there's commoditization of a lot of the costs of big data sources. Like Hadoop has created this amazing opportunity for people to more cost effectively leverage their data that they couldn't really access before because it's too expensive. And so it's bringing this whole new insight into how do you leverage data in a way that's more meaningful for that problem you're trying to solve, not for everybody, but for the problem you have, the use case you have. So this is critical, because Dave's saying, so what's new now? We've heard the customer 360 story going all the way back to the first data warehouses and maybe even before, to the mainframe reporting stuff. So, and you've identified one critical new ingredient, which is the price has dropped by more than an order of magnitude. We hear reports that some customers clock their, their data warehouse investments at 100,000 a terabyte, which is kind of hard to believe. We thought it was in the 35 range. Besides price, what else has happened that we can deliver on these? You know, use cases that we were stuck on for decades. Yeah, well I think it's price, I think it's the new emerging streaming technologies you see with things like Spark that enable that. I think it's cloud is a huge enabler because a lot of the data is actually being created in the cloud, right? And so you have this whole enabler of information that resides outside of the Four Walls of a company that people are using to do that. And that's different than it was 20 years ago when data warehouses were being built. They weren't really built with a very open sort of framework that said data is being created everywhere you could imagine, everywhere you could imagine. So how do you bring that to bear when you're trying to solve a business problem? And that paradigm is really different than I think the traditional paradigm from 20 years ago. Well, and Quentin said, you know, it's getting cloudy and he's told right on the theme back in 2010 was big data gives the cloud something to do. And people are saying, well, this cloud sort of niche use cases and of course you were at Amazon re-invent last week, you know, $7.3 billion run rate, 80% growth. And then you juxtapose that against what happened this week with Dell and EMC. Basically a flat, you know, big companies ringing cost out, you know, trying to drive cash flow totally different story. The innovation is very clear and the cloud and big data. And it's just interesting to see you guys and Hitachi, your vision around that, much different than what we're seeing with sort of old line technology companies. Talk about that a little bit. I think one of the things I love about the approach Hitachi is taking is they build all the machines where these sensors are. They build mining equipment and healthcare equipment. And so the concept of building machines with sensors and then wanting to use that information, right? To drive more analytics, better decision making for the people that actually use that equipment. That's part of the DNA of that company. And so it isn't like it's an IT company or software company all of a sudden that wants to grab these sensors coming from somewhere. They really understand those industries. They have Hitachi Consulting that's built vertical practices around those industries. So the ability to take know how physical, the physical world and the virtual world and bring that together. And then leveraging Pataho as that analytics platform in the middle to be the glue for that. It's really credible. I mean if you think about it, because of the know how they bring to the table and the software expertise we bring to the table and the use case driven sort of approach. We both I think are very pragmatic about bringing a user-based, a use-based approach to sort of solving these problems that happen to have an internet of things component in terms of the data flow that comes in. Right, because even in a Hitachi scenario they don't just talk about the sensor data. They talk about all the other types of data that really provide that full solution for those examples. Along the lines of this customer journey where many of us started or many customers started in the data lake, get their feet wet. Part of what limits progress is, skills and processes and product capabilities. How do you see that journey? How do we push back the boundaries? Yeah, I mean that's a great question and I'll talk about it from a business point of view and we have technologists that can address it from a technology point of view. But what I see on a business point of view is as technologies mature, one of the first things they do is get easier. And the reason they get easier is because the economics say as a business, if you make your tools easier, more people will use them. And so I actually think the maturity of these Hadoop systems and stuff like that are starting to simplify because they know the way to access more people to get that to be normal in a mainstream environment means you have to have the right tools that simplify the ability to leverage them. So what used to be hard, you know, really hard is still somewhat hard, but I think there's a lot of innovation going on to try to simplify that. And I think we'll see that scale of simplification continue to move forward and that's going to be the true innovation in my mind. Is that? We're out of time but I wanted to give you the final word. Yeah. You're entering a new decade of your life as Pentaho, Hitachi acquisition, et cetera. Where do you see this going? Where do you want to take it? What's the vision? For Pentaho? Oh yeah, my vision for Pentaho and it's what we always said. So we look at this big data blended capability as being fundamental for companies that want to take their data and use it a meaningful way. And to do that they have to be able to blend that data together, provide context around that data and then consume it however they want to. It may be a report, a dashboard, it may be an application, it may be a workflow that they want to stream into. So our vision is really to enable that whole value chain to occur. And I think that, as you said, that will be an ongoing vision for us as new data comes in, different types of technologies come in. And our goal is really to simplify that ability to really leverage the end user who ultimately makes the decision, you know, with these. Well Rosanne, thanks very much for coming to theCUBE. Thanks for having us here. It's my pleasure to be here. It's really great. Thank you guys so much. Pentaho taking all this data mess, making it actionable, not just out of the box tool. Excited to learn more about this. Stay with us. We got customers coming on. We got partners like Mike Olson at Cloudera. We got the analyst perspective from Forrester and two days of coverage here live. We're theCUBE. We're at Pentaho World, right back. What were they talking about last year at Big Data NYC?