 Live from Seattle, Washington, it's The Cube at Tableau Conference 2014 brought to you by headline sponsor, Tableau. Here are your hosts, John Furrier and Jeff Kelly. Welcome back. We are here live at the Tableau Conference 2014 in Seattle, Washington. I'm Jeff Kelly with Wikibon. You're watching The Cube. As we've been talking today about a lot of the themes here at the show at Tableau Conference, obviously customers is an important theme. The product is an important theme, but partners is another important part of the picture here at Tableau as they're growing, their partner ecosystem is growing. And we've got one of their important partners here, Treasure Data, Hannah Smalltree, director at Treasure Data is joining us on The Cube. Welcome back. Thank you. Thanks for having me back. So yeah, so I know you just got into town this morning. So you haven't been to the show for too long, but what's your take on the vibe here? It's good. Obviously, they seem to be pretty excited, these customers. It has been an exciting day as we were just talking about off-camera. People are really excited about what they can do with Tableau. And I'm really excited to hear more about what they can do with their data when it becomes easy and simple to access and analyze it. So yeah, so set the stage for us for our audience about Treasure Data. You guys are fairly new on the scene, but been doing a lot of work for a number of years though, kind of building up the infrastructure and the service. Tell us a little bit more about Treasure Data. Yeah. So Treasure Data launched our service about two years ago. It's a cloud service for managing massive volumes of big data. So things like event data and log data, sensor data that's coming from different products like smartwatches or cars or other vehicles. So we help manage data that comes in at high velocity, often high volume. Our customers need us to help them manage millions and billions of data records a day. So we provide capabilities for them to easily collect streaming data, high volumes of it, store it in our cloud service, and then analyze it so their analysts can use SQL or, as we're talking about today, Tablo to analyze the data. So we really turn to Tablo for those visualization capabilities of big data. Treasure Data provides the big data back end really quickly and efficiently. You don't need any special skills to use it. And Tablo provides that pretty visualization for the front end so you can visualize big data. And your cloud base, so there's no kind of installing servers. It's all through the cloud, right? Some people call it software as a service for big data or I'm hearing big data as a service. But we are all in cloud base and with Treasure Data, we're also managing and monitoring and maintaining the service for our customers. So not only are we cloud based, you don't need to know anything about managing cloud infrastructure. We handle all of that. So that's a fully managed service. That's different than a infrastructure as a service or even a database as a service where you've got to still manage your environment. This is completely, soups to nuts, the entire package delivered as a service. Yep, our staff are data experts. That's what they like to do. So when you're talking about the amount of data, the type of data that your customers are interacting with, gets me thinking about the Internet of Things, Industrial Internet, whatever you want to call it. The smartwatch, obviously Apple had their big announcement yesterday with the Apple Watch. I'm wearing my jawbone. So talk a little bit about what you're doing in that space. I think there's huge opportunity there, but it's really early days. People are still trying to wrap their heads on the concept, let alone actually, what are we going to do with all this data? What's your approach and what are you seeing in that market? Yeah, so in the last couple of years, we had first started with a lot of application logs and web access logs. And then increasingly, we've seen a lot of interest in sensor data. Last week, we had an announcement with Pioneer, a big electronics company that we all know about. They do a lot of work with automotive manufacturers. So working with their onboard devices to collect data about the operation of the car and other things going on around it. We're working with them to collect that sensor data. They will use that sensor data to develop services for other people in the auto manufacturing chain. So the repair shops, the insurance companies, the car rental companies. So they're using sensor data to do some really interesting analysis around how we can make driving our cars a better experience. And we're also, as I mentioned, working with some wearables companies. They're using sensor data to figure out how to build a better product. So as we have all of these smart, connected products, many of them are generating data. Our customers are interested in using that data to provide services to their customers, to understand how people are using their products and services. But sensor data is massive. It comes in really fast and furious. And it can be very difficult to even just collect and put in one place. Right. With the innovations going on in the sensors themselves, and how small they are, how powerful they're getting, I mean, there isn't any kind of physical device you can think of that can't be outfitted with a sensor these days. So the volume of data is just going to explode. It's proliferating. I can't say that word so quickly. So we're expecting upwards of 30 billion devices connected to the connected internet. And when you think about all those devices sending out data points every minute or even every five minutes, that very quickly gets to be a massive volume of data. And data collection is a huge bottleneck for companies in these internet of things implementation, just physically figuring out how to move that data around and get it to one place where you can get value out of it. Well, yeah. And the other thing is just the storage implications, which is why I like the cloud approach. We've done a lot of research at Wikibon around, we do our research core research areas are big data in cloud. But of course, where they intersect is where we get really interesting, which is the space. That's where we are. That's exactly where you are. Talk about that market generally, not treasured data specifically, but where do you see the big data cloud market, for lack of a better term at this point? There's a lot of, certainly a lot of opportunity, but there's still some of those issues around privacy, security, can I move my data off-site? How do you see that, do you see that conversation evolving? Are the concerns that your customers might come to you changing? How do you see that progressing? I think things have changed so much. And especially in the last decade, full disclosure, Jeff and I both used to work together, as journalists, and we've seen huge changes in what people think of when they think about the cloud particularly. So big data in the cloud are very well suited for each other. The data types we're dealing with are typically remote, so they're already outside the firewall. So that's thing number one, when you're talking about different remote data sets, mobile applications, websites, already outside your firewall. So people are much more comfortable with aggregating that outside of the firewall, and then just moving the valuable data into the enterprise where they can manage it. That also has the benefit of, collect it outside the firewall, billions and billions of rows, run some queries, do some analysis with treasure data to make it much, much smaller, and then bring it behind the firewall. But I see a lot more understanding of how the cloud works, a lot more appreciation of the fact that these are new data types. We're not talking about taking your finance database and uploading it to the cloud. That's the kind of thing people typically do want behind their firewalls. So we're talking about these new data types, these things that are already outside your organization. How can you effectively bring those together and make sense of them? And then bring the important stuff into your organization. Yeah, I mean, you really need a fundamentally new approach to managing data in this space where the amount of data, the type of data is just exploding. So we're here at the Tableau conference. I understand you have a customer here, I think giving a case study presentation. Talk a little bit about your customers, that customer. What are some of the things your customers, your joint customers with Tableau are doing? Yeah, so we have several joint customers with Tableau. We do have a partnership. As I mentioned, Tableau provides the very important visualization capabilities on top of treasure data. Tomorrow morning, our customer, Muji, a global retailer, will talk about how they're using treasure data and Tableau to help support a new loyalty program. So Muji is a, it's a really cool store. There's many stores throughout the United States. I encourage you to check them out. They're sometimes called the Asian IKEA. So they make a huge line of products, different housewares. They're actually in a lot of different businesses. And they have a lot of customers online. They have a lot of customers offline. And they really wanted to understand customer behavior as best they could. So they came out with a new mobile loyalty app. They're using treasure data to help collect all of the clicks, what people are doing in that mobile loyalty app. They're also looking at clicks on their website. What are people doing on their website and who are those users? Then they're correlating that data from the mobile app and the website. They're putting all of that into treasure data, where they're doing different queries and analysis to understand how people are using the website and mobile app. Then they're also taking a subset of that data and putting that into their cloud data warehouse, Redshift in this case, which has a different set of capabilities. There, they're combining it with other data from e-commerce and POS systems so they can understand things like how do my high value customers use the mobile app or use the website? And what incentives can I give them to do more with us to go into stores? So actually they had an interesting challenge around driving people online into their physical retail stores because a lot of us buy more when we're in the retail store and we can pick it up. You know, I do. Indeed. So I'd love to talk about a little more about Tableau, the company. Your take on what they're doing, their strategy. So they're growing like crazy, as you know, in terms of revenue, in terms of employees, in terms of partners. Kristen Shabbo, the CEO announced yesterday they're going to invest more in R&D in the next two years than they have in their previous 10 years. Do you think that's a good strategy at this point for Tableau? I mean, that's a really big investment to be putting back into the product. I know from an investor's perspective, maybe they'd like to see a little bit of that money return to investors, but I know the customers love it because that means new functionality. I'm guessing partners like yourselves think it's a great move as well. We really appreciate Tableau and Treasury data have a similar philosophy about providing a simple interface to do very powerful things. But as any of us who have tried to build a simple interface to do powerful things know, that is not easy. And that's kind of what Apple's done really well too. So simple intuitive interface, those things cost money, but it is worthwhile money if you can empower your organization to take advantage of new data sources using these cool interfaces and making it really easy for different types of users to consume different data types. Yeah, so we had Jack McKinley on earlier who oversees visual analytics for Tableau and he talked about that. He goes simple is great, but if it's not useful, then it doesn't matter. So it's not easy to be simple. Simple and powerful. Simple and powerful, right. Really difficult combination. It's easy to be simple. I can develop a very simple tool. It doesn't do anything. Exactly. So to combine the usefulness, the power, and then the simplicity is a real, if you can pull that off, I mean, we saw what Apple, I mean, that's what Apple's philosophy right now. And it takes money and it takes smart people. It does. And if you want to do it fast, it takes more money and more smart people. Exactly, exactly. So in terms of the rest of the ecosystem around BI, how do you see Tableau? I mean, as you mentioned, we worked together, we covered this market for a long time. You've got, you know, Tableau came in really as a disrupter to the traditional enterprise, software, business intelligence vendors, the SAPs, oracles of the world. But they've been around for a little while now and now we're actually seeing what I might call the third generation of BI tools. You're seeing cloud, cloud first companies come about who are offering BI tools, some focused on just mobile BI. So Tableau's not the new kid on the block anymore. How do you see them responding now in terms of, you know, they've got kind of competition on both flanks? I think what's interesting in the BI market is it started out as one kind of tool and then there were all these different competitors in the market. And really, as we started to have all these different kinds of BI tools, different tools made sense for different groups of users. So similar to some of our early surveys that we used to do, nobody, very few people have just one BI tool. They have one BI tool maybe for this group, maybe the data scientists need a different type of interface. What's important is that everyone's working at the consistent data set and everyone has a similar understanding of the data. But I think that there are room for different analytics tools. That's the first thing. Tableau's done a very good job at reaching a group of business analysts who I believe were underserved by some of the other tools. So some of the enterprise reporting features were not giving people the power that they needed. And so I think Tableau's done an excellent job at reaching out to a large underserved market and giving them a tool which made it very easy for them to do the type of analytics they want. That said, I think there's still room for other tools that do more specialized things in vertical industries or have different sets of capabilities. Some of these other new BI tools do some of the other tools work with Tableau and you think they're competitors, you dig into it, you say, oh, no, they're actually partners and they work together and they complement each other. And as long as you're working off a consistent data set like you will get from a treasure data or another backend, it's okay for people to bring their own tools to the game. And what's interesting is the cultural impact of tools like Tableau. So the idea is we hear about democratizing data, democratizing business intelligence, but it's also democratizing, if it's not coupled with a cultural change, democratizing decision-making authority in a company, it's almost like, well, what's the point? So if you're going to provide a tool like Tableau to your frontline workers to find actionable insights in their data, you've also got to give them authority to take those actions. Are you seeing, in terms of your customer base, how are they dealing with some of these cultural shifts as data becomes more ubiquitous? People have new tools where they can come up with their own insights. They don't necessarily have to go to IT. They go to somebody like Treasure Data, they go to the cloud, they go around IT. And now they've got all this actual insight, but you've got a couple that, again, with the cultural change to actually give them the authority to do that. Are you seeing that in your customers? Absolutely, and it's interesting. Another topic near and dear to our hearts is data governance, kind of a boring topic, it seems like, but when you dig into data governance, it's really important for people to know the kinds of decisions that they are empowered to make and to know the boundaries of those decisions. So we actually encounter, our customers encounter similar challenges because we're democratizing access to big data. We're making it very easy for anybody who knows SQL can now run analysis on big data. But it's still a similar problem than what it's always been, dances with data. We can interpret the same data point different ways. I can take an Excel spreadsheet and manipulate it to prove my point. So at the end of the day, you have to come back to data governance, you have to understand the data sources, and you have to have agreement on how you're going to interpret them. So things like good old data governance and data stewardship, I think that's also what's leading to roles like the chief data officer, who is really starting to oversee all of these different data initiatives because there has been a bit of a governance problem. On the one hand, it's good, that's great for innovation that we're all out doing stuff and building things, but as a business, you do need to be cognizant. Data's very valuable and how you interpret it, those interpretations are almost more valuable. Yeah, in some ways, it depends who you ask, from an innovation perspective, people are like, yeah, it's great, people are experimenting, they're looking at new ways of exploiting their data, you talk to like a chief risk officer and they're scared to death about what's on these capabilities. Some of it is about just where you let that go. You can come up with all the fun stuff you want, but do not put it on Twitter, do not let it outside the company, you can bring it to a meeting and we can all talk about it, but having those governance rules around how data can be used and understanding that some data's more valuable than others, some stuff you can go crazy with, you can do whatever you want with, and some stuff you really can't. And so, the need for effective governance is more important than ever. Yeah, and the other challenge is that these rules and norms are still evolving, you don't kind of know where, what they're going to be yet, and there's going to be missteps, and unfortunately for some people they're really going to step in it, and it's going to be, that's how we learn lessons as an industry, but somebody's going to take the fall sometimes, but hopefully at the end of the day, we're moving forward and creating more value than risk. Than less, yeah, absolutely. Well, we've got time for one more question, so I just want to hear what's on your roadmap. Obviously, you guys are growing as well. What's kind of top of mind for you? What's your to-do list over the next six, 12 months? Yeah, so as I mentioned, we're doing a lot of really interesting work with customers who either make large applications or large websites, reaching lots of people. That's kind of been our bread and butter, and we're continuing to grow those capabilities, expand our SQL support. We just announced new capabilities for collecting mobile data, so data from different kinds of mobile apps and mobile devices, we make it very easy to stream that into our service so you can analyze it with SQL very easily. And then just more and more, we're seeing interest in the connected world, so internet of things. Different wearables companies, we're working with several wearables companies. Vehicle telematics has been another area, as I mentioned, with Pioneer. We've worked with a couple other telematics companies, energy companies with sensor data. So there's a ton going on with sensor data, but I also, I don't want to let go of the fact that there's still lots of app logs and there's lots of data out there that's not being stored that there's huge potential with. So we're just seeing people, especially as they realize how easy it is to look at this data with treasure data, storing more and more data and doing more and more interesting things with it. So I'm just excited to see people store more, analyze more, combine more interesting data sets and really be able to play with data without limits, without the technical limitations we experienced in the past. Absolutely. Well, we'll be looking forward to all of that and we'll see you again on theCUBE soon. I hope you're now, you're a veteran here on theCUBE. A veteran on theCUBE, I encourage everyone to come. Oh, we have a new blog too, so at treasuredata.com we have a new blog. I'm a midway through a post right now, so really trying to get a lot more news out there about treasure data, so please work with us and try our service, you can try it for free online at treasuredata.com. I'd love that approach, the cloud approach. Cloud approach, try free, no credit card. You know, then buy, buy, then drink. It's great stuff. Get on it. Great. Well, Hanna Smalltree from Treasure Data, thanks so much for joining us again. We'll see you on theCUBE again soon. Thanks for watching everybody. We'll be right back with our next guest after this.