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TIBCO Software, Inc. (NASDAQ:TIBX)

February 11, 2013 7:35 pm ET

Executives

Matthew Quinn

Analysts

Raimo Lenschow - Barclays Capital, Research Division

Unknown Analyst

Okay, perfect. Thanks for joining us for our next session. Two to go. I'm really happy to introduce Matt Quinn, the CTO from TIBCO here today. We're doing this whole thing as a fireside chat. So I'll fire some questions and hopefully you can join us a little bit later. Matt, you were here last year already as well, so hopefully we can make this a tradition. And Matt, notice we make this a tradition.

Okay. If you look -- I mean, I have to say, if I look at TIBCO and the assets that you guys acquired over the last few years, there's been a lot around big data and the big data portfolio. I think, first for the benefit of the audience, starts off talking a little bit about your vision around big data or maybe just kind weave in some of the acquisitions you've done of late.

Matthew Quinn

Sure. So maybe the best place to start is actually just come and looking at the TIBCO portfolio as a whole. So the first thing is we kind of look at ourselves as a platform today versus just a software portfolio. And the platform is really kind of divided into 5 broad chunks. The first is what we call the automation area. This is really the efficient movement of data around the enterprise and across partners. It's managing the business processes, and it's also collecting all of the events needs and the data itself.

The second piece is what we call event processing. And so this is where we do a lot of correlation around the events, around the data that we're collecting in that automation space. We've got the analytics portfolio, largely made up of the Spotfire acquisition that we made 6 years ago, 7, as well as the LogLogic piece that we did last year. And that's really around how do I visualize the information that's coming out of all of these different databases.

And then the final 2 areas are cloud and social. And obviously, in social, we have the tibbr asset. And in cloud, we acquired DataSynapse a couple of years ago. And DataSynapse actually has a couple of different aspects to it in relation to big data, and that's really around the high-performance compute grid, allowing us to kind of scale out and process a lot of stuff less on the data side but more on the compute side.

Now specifically, from a big data point of view, what we really look at, at a very high level, is that we believe our job is around closing the loop between what we call data at rest, which is all this historical information, all the transactional information. Some of it stored in database and some of it not, and data in motion, all right. So being able to kind of bring those 2 worlds together. And obviously, TIBCO has come from that data in motion world, that event world. But we believe that kind of operationalizing big data service stake is really where the focus is today.

Question-and-Answer Session

Unknown Analyst

And help us understand a little bit better. If you kind of see your customer, how do you pitch the portfolio and what's the reaction from the customer? Because it seems very, very broad. But in a touch, it's also different parts of where to -- different parts of the customer buying center in a way. So kind of help us understand how that all fits together.

Matthew Quinn

Yes. So maybe the best way to do that is to really just come and focus on the big data area rather than kind of the broader portfolio we hear all afternoon. And so what we've done is we've been very effective at maintaining these companies that we've acquired and the assets that we've built over time and created these business units around them. And so for example, Spotfire will focus a lot on people looking for next-generation analytics tools, really looking at that visual analytics piece. LogLogic is really talking to the chief security officer and people who are looking at specifically at machine data forensics and those types of things. And we like the fact that the tribal knowledge that's around those particular assets remain because often they are competing against very different people. They're talking to very different people. What we've done very effectively though over the last 12 to 18 months, really last 2 years as we started to identify ourselves as a platform is integrate these things together to create other new solutions or to gain a competitive advantage in an existing area. So for example, the first thing that we did after acquiring LogLogic last year was to integrate it very tightly with the Spotfire tool to provide a better way of visualizing all this mass quantity of log and machine data that LogLogic was actually creating. Now 2 things happened there. One was that there was a lot of upsell and cross sell with that. So we're able to kind of penetrate more broadly into accounts that already had LogLogic, but also gave us a competitive edge against other competitors in the marketplace who perhaps didn't have as good a visualization tool for the data while still providing great functionality for the individuals who are using it.

Unknown Analyst

Yes. I mean, if you stay for a second on the LogLogic subject. Obviously, we had earlier the -- Godfrey Sullivan, the CEO from Splunk, talking about how the world for them has moved on from just kind of security -- from a security focus to being a much, much broader platform. But in theory, with LogLogic, you have the assets, with Spotfire, you have the front end, what's your vision around that? At the moment, you just mentioned it's more in the security space, are there broader ambitions there?

Matthew Quinn

Yes. I think actually that space itself is broadening out. And we're certainly seeing this from our customers. On one end of the spectrum, you have people really looking at this log data as being a way to look for things like abnormal network detections or looking at the spikes in network traffic and always kind of adding more and more information on that side of it. On the other end of the spectrum, we're getting a lot more interest around kind of application-level monitoring and application-level management. Because often times, it becomes a really expensive process to actually augment an instrument individual applications, right. This is where the kind of tibbr leaves an open view of the world, it kind of comes from it. When you've got potentially thousands of applications, oftentimes log information is the only place we're going to get any visibility into the applications and what it's doing. And so there's a lot more focus around that just from trying to make sure you're managing your application portfolio in an effective manner versus just purely on the security side of it. Now obviously, as we go forward, we're seeing people blend the machine data that we're capturing as part of LogLogic with what we call BusinessEvents. Kind of business-level events and the business-level data trying to gain an advantage or look for opportunities and threats really more on the business side not just on the technical side.

Unknown Analyst

Yes. The -- and so from that perspective, could I -- could we expect -- I know it's probably the wrong question, but if you think about the broadening, so if you kind of match kind of fulfill this promise and you come back here next year, could we think about a broader, even a broader LogLogic that we see today?

Matthew Quinn

Yes, absolutely, absolutely. And I think that -- and again, this is being really something that we've done before. This not the first time that we've kind of gone down this path. And if you look at -- if you kind of compare and contrast LogLogic with what we've done with Spotfire, so Spotfire, we initially kind of had it standalone going after different parts of the market, especially areas like pharmaceutical, and really kind of focusing on the data scientists. And what we did with Spotfire in the intermediate years is really blend it with a lot of the TIBCO technologies that we have as part of the platform. We're doing exactly the same thing with LogLogic. And so looking at opportunities to pair the log management, log capture functionality of LogLogic with that BusinessEvents product, which has a lot of event correlation and business-level correlation and rules, obviously, we've already done the Spotfire piece, pushing that into our broader SOA offering because many of these applications are exposed in web services interfaces all the way out to -- we have new products in the area of API management, which is a big hot topic at the moment. So all of these companies want to create these company-level interfaces for either B2B or partner-level services, being able to broaden our logging capabilities after that and then obviously looking at the cloud as well.

Unknown Analyst

And how do -- if you go out and meet the customer, how much of kind of big data picture is that or is lot of the stuff that you're doing there. I mean, yes we can bundle them into a nice marketing presentation as big data. But on the other hand, all of these are solving very specific individual business problems. Other customers at the moment what -- in terms of what you're seeing, are they kind of buying the big data vision and then just need to fill their demands or is it still very much like a ROI-type driven business as well?

Matthew Quinn

Look, I think that the big data market itself is clearly changing, right. So if I look at last year, the majority of the customers were interested in, not just ourselves, I think of all vendors in what the story was and what this big data thing was. There was a lot of education kind of going on. I think there was a lot of tire kicking. We have customers who have stood up at hundreds of instances of Hadoop to different projects trying to look to see what value is there from that kind of the side of the house. I think that today especially, I think that most CIOs and CMOs and even CFOs are really grappling with how to reduce the time to make decisions. And so oftentimes, this is kind of backing into the broader big data themes. And they're looking at how do I operationalize big data? And that's really where we started kind of talking about it. So yes, the individual products and the individual parts of the portfolio are very important, but we want to kind of level set with all of our customers that look at finding the pattern in the data is one thing, finding it again and finding it again, finding it again and then being able to anticipate the patterns that we found is really where the value is because you're trying to ultimately just reduce the time to make better decisions.

Unknown Analyst

Yes. I don't know how much you can talk about it, but I don't want you to name companies if you don't want to. But can you just maybe give us a couple of examples of kind of what people have been doing?

Matthew Quinn

Yes. So there's a couple of different angles. I think that the one that we're seeing probably the most of at the moment is the idea of upsell, cross-sell. And it's a pretty interesting space, and obviously we made an acquisition a couple of years ago of a company called Loyalty Lab which kind of manages broader event-based marketing or loyalty. And so the idea behind Loyalty Lab is you've got customers and you really want to turn them into fans, right? Really kind of hard core. They only identify with your brand. And so what we look at there is it's a kind of classic case of operational big data because what you're doing is you're taking point-of-sale data. And oftentimes, you're taking historical point-of-sale data going back 10, 15 years, as well as the realtime data. You're taking social data and graph data from all these different sources. You're taking a lot of unstructured information, and you're taking the general, what we would classify as kind of reference data or master data management that bring all of those things together in order to, at that moment of interaction, either upsell or cross-sell you with the product or service that may be in line with either what's in your basket or maybe a purchase you made kind of 5 or 6 months or your general demographics. And so that we see a lot of really not just in the retail space but across different industries, telcos, retail banking. Even insurance are looking at those types of things. And it's an interesting study because on one hand you've got potentially petabytes of data that you have to mine and create models for and you have to find those patterns of interest. You got the marketing people who want to get involved to create these offers based off this dynamic data. And then you actually have the moment of truth, that point of interaction where you may have a second, maybe a millisecond, to actually create the offer and deliver it to the customer or to the business partner. And so it really kind of blends all that together. Now that's one side. Now on the other side is kind of the classic what I would almost call the classic computer science side of big data which is largely what we're seeing here is around things like fraud, but fraud at volumes. So they're looking at things like institutional fraud, when you're trying to handle kind of a million messages or million different transactions a second during peak times. And so that, again, it's the same type of pattern as what we said before. You've got all this historical information that you're trying to mine, you're trying to create and visualize these different patterns. Not really a human involved in that part of it, but they want to really create these models and these rules that can be acted on in realtime. Now the challenge there isn't necessarily just handling the volume, we're very good at that, we have the software platform to handle those volumes, but it's really what action is appropriate. And so that's really where context becomes a huge issue, right? How do you not just detect the fraud but also look for the context of which the fraud was happening and create either dynamically the business process or pick one that's appropriate in terms of your action or reaction to the original threat.

Unknown Analyst

Yes. And talk a little bit about thing that -- if you talk big data, we talk a lot of unstructured and social data, et cetera, but one aspect of big data is obviously velocity as well. And just a lot of stuff that you always have been doing, messaging, velocity messaging, ultra messaging. Can you -- I mean, I know customers are probably not going to say, "Oh, that's big data," but they always have been doing it and it's one of the aspects here. Can you just talk a little bit about how that has changed? Because in terms of the -- we had SAP talk about a realtime company and the opportunity to run a realtime company and the fact that TIBCO should be right in the middle of that as well in my view?

Matthew Quinn

And we are. And I think what was interesting about realtime in general, and messaging as being one of the kind of the cornerstones of realtime, is the fact that when you talk to financial services companies, banks, they feel like they're being doing big data for 20 years ever since the advent of industrial strength enterprise messaging. And to a certain extent, they're right. They're dealing with the velocity. I think what's different a little bit is that, especially if you go back kind of 15 to 20 years, most of the data were very structured and it was actually -- there wasn't a lot of variety to the data. It was all kind of -- there was lots of the same types of data coming through. I think what we're seeing now especially from the messaging point of view is 2 things. One is messaging in general. And really in-memory, data group is, as well to a certain extent, go hand-in-hand here of being asked to not just handle data in volume and data at speed but also handle different variety, different types of data and bring it all together. And so being out of mix with those payloads from very small messages that you'd see coming out of an exchange with a much larger data sets that may come through periodically, people don't want to build 2 different messaging environments, they just want to build 1 and then reuse across multiple different areas. And that's why -- quite frankly, that's why we invested in the TIBCO FTL product, the ultralow latency product. Because I think the other thing that's always missed is, yes, doing it at speed is one thing but doing it in a way that is predictable with the latency is very predictable is also very important. I want to know that if something happens that within 300 nanoseconds, I'm going to receive that information, not a minute or 30 seconds, but it has to be predictable.

Unknown Analyst

Yes, yes. And maybe if you look at that market, I mean, is there -- has there been any, I mean, in a way that's core TIBCO and you're very strong in there and you should be very strong in there because that's where you came from, has there been any change in terms of either competitively or from an understanding of a customer perspective and that's kind of a very critical part and hence TIBCO should be actually a more strategic vendor going forward.

Matthew Quinn

Yes, look, I think that what happened is interesting because this whole talk about big data originally was all centered around just yet another big database, right? Just using different technologies, different words to describe, storing data in ever-larger data stores. What's actually happened I think over the last 12 months is as the pivot has occurred away from science experiments to how do I reduce the time to decision-making, messaging and realtime infrastructure has actually become much more important. And so it's actually being at the forefront of many of the discussions that we obviously have with customers. Now I don't think that anything is really dramatically changed in terms of the overall competitive landscape, but this has been -- this is a space that's been around for 25 years. And there's been a number of different vendors and different technologies that have come through. But really the only constant has been TIBCO in that market for 20 years.

Unknown Analyst

And how do you see customers prioritizing at the moment in terms of making decisions and not after financial metrics to be very clear? I'm just thinking like so we have like a tera data reporting numbers being low. We're doing big data and everything is great but actually people don't have money because their key budgets are not growing. So if you have a conversation with clients at the moment, how are they prioritizing their spending? I mean, is this fancy Hadoop stuff that you could do, but how -- I mean, what's the main driver for you and how you're fitting into that?

Matthew Quinn

Well, I think this is actually one of the great things

[Audio Gap]

buying behavior. It's actually been very, very useful, not just the new TIBCO customers but also for existing customers that have maybe already laid out their SOA or BPM strategy and now looking at where they take the investment next.

Unknown Analyst

I mean one of the discussions I have with investors is that you have a very, very broad portfolio and you feel almost sorry -- you feel lucky for [indiscernible] broad portfolio but you feel sorry for them as well because it's very broad portfolio and it's just very difficult, you cannot -- you have usually contacts in one part of the organization, but the portfolio kind of fits into other part of the organization. What kind of work you're doing internally to help your organization to...

Matthew Quinn

Well, I think there's a couple of different things, right. But the first is that we often go in there with things like the big data vision, right, and that's a great entry point for customers to understand kind of the broader TIBCO platform. The second thing is that often times, we're not just going in there with raw underlying products and technologies, we're going in there with a solution. So for example, in Europe, we've been particularly successful in taking many of our technologies including Spotfire and LogLogic, bundling it together, adding unique intellectual property on top of it in terms of templates and other things and going after order management within telecommunications and things like that. and So often times, depending on the vertical and depending on the maturity of the organization that's supporting that, we can go in there with solutions, we can go in there with focus on any part of the platform. And so we -- I think we've got very good at choosing the right mix of high-level solution versus lower-level products for these different areas. We see, for example, a very big difference between the buying behavior in, say, Asia and Europe and in the U.S. And so we have really -- the platform morph them for those particular areas.

Unknown Analyst

The -- let's shift gear a little bit here. You see [indiscernible] a couple of quarters ago very vocal about in-memory and how it will change the world. Now so far, you haven't -- I mean, I'm sure you're using it -- well, just to help me to understand, how does it fit into the TIBCO vision?

Matthew Quinn

Yes, so TIBCO has a product called ActiveSpaces. We've had it for about 3 or 4 years now. Now ActiveSpaces represents kind of 2 things for us. One is it's our standard in-memory data grid. And so it has the ability to store potentially petabytes of data in-memory and also share nothing, kind of shot the data over -- of the disk, if need be. Now at one level, we sell ActiveSpaces as a product, like we do in integration and everything else. And customers use that often times to store all of their transient operational data, like flight data, that may only live for 7 or 8 days. And what they really want out of that is they want really fast access to the data without having to go all the way back to many different databases that may contain the information. But on the other side of it, ActiveSpaces has the ability to tell you when something has changed. And this goes back down to kind of the messaging history of TIBCO. So don't just create a database or yet another database. And like as Vivek is often fond of saying, database is like the phone that doesn't ring. What you want to be able to do is you store the data and when it changes, tell people that it's changed. Now that's a huge feature for us and it really is one of the biggest differentiators we have around this kind of space and not just a database but really blending messaging and in-memory data goods together. Now we also use ActiveSpaces across the platform. So it's integrated with Spotfire. It's integrated with our event correlation engine. It's even integrated with our SOA and integration suite, and we're also -- we've also integrated out things like Hadoop to bring data both in and off of Hadoop clusters. And so it becomes -- it's almost like a fabric. So if I look back in history, TIBCO always had messaging as that underlying fabric or really underneath all of TIBCO's products. Well the second piece to that today is really the in-memory data group. And so it's hugely important to us and we believe that it's hugely important for the future as well.

Unknown Analyst

Yes. So from your perspective, so in-memory, if I understand you correctly, is very, very good tool for operational stuff, doing write the analysis -- so like -- so that's the way you have to -- I see it with you guys.

Matthew Quinn

Yes, I mean, in some ways, going all the way back to the beginning of the conversation, when I talk about closing the loop between data at rest and data in motion, oftentimes that glue piece is actually the ActiveSpaces in-memory data grid because it has the ability to integrate after things like Hadoop and other traditional databases, but it's also connected into Spotfire and now event correlation. So it has the ability to -- everyone is looking at the same data set now. So it's not like Spotfire is looking at a whole bunch of different databases and maybe a couple of Hadoop clusters. It's all looking at the same data. The event correlation is looking at the same data. Everyone is looking at the same data. And that's really one of the things that we've learned not just generally but also more specifically even the Spotfire, right. Oftentimes, customers will bring things like Spotfire in as a way to create common visualizations on common data sets so that people are now arguing over my spreadsheet is better than your spreadsheet, the discussion is much more high-level at that point.

Unknown Analyst

Yes. From your perspective, it's good to talk, I mean, about in-memory but some vendors are a lot more vocal than you guys. Does that kind of cloud the market a little bit in terms of what data -- what in-memory is good for and should be used for and what it probably is not what it to be used for?

Matthew Quinn

No, I think because what we've done is -- because we sell many things and we've done a great job of tightly integrating and loosely coupling many of these assets, we often let the power of our in-memory data grid to be exposed through technologies like Spotfire and through BusinessEvents and other products. So we're not just going to market with 1 product. We're going together -- going to market with a solution that is powered by these in-memory data grids. And I think that's a more effective way of doing because otherwise you just become a widget vendor, right, a one-trick pony selling one thing versus actually going to market with a complete kind of end-to-end solution for a particular market.

Unknown Analyst

Yes, perfect. Okay. And then if you look at the markets that you have, and as you talked about with your customers, so we're like -- and I'm mindful that Click is going to presenting next then you have Spotfire, how do you shape-up in terms of the debates with the client on some of the products that are kind of competing in the special area like Spotfire with Tableau and with Click? What's the positioning that you see works best for you and where do you win, where do you lose?

Matthew Quinn

Well, I think that the way that we go to market is really like everything else. We want to make sure that the products that we go in the market were the best in class, right? So yes, it's great to talk about the platform. Yes, it's great to talk about the breadth of the portfolio and the fact that we can create these solutions from all the different component pieces in the platform. But especially for products like Spotfire and ActiveSpaces and BusinessEvents, now messaging, we want to make sure that these things are absolutely best-in-class. And so there's a lot of our in-data, there's a lot of engineering work, there's a lot of marketing work that goes into those individual products, right, because we don't want to get into a position in which I think that some vendors are in today which is you've got all Class B products, but magically, when you put them all together, you've got an A-grade solution. We want to make sure that all these things are fantastic. And I think that when we go to market with that sort of just something approach, we win a lot of the times. Now I think that what we also can then do is we can leverage the broader platform to show different parts of the solution where appropriate, right. So yes, it's fantastic to talk about Spotfire for visual analytics, to talk to the data scientist, to talk to the business units, but by the way, we've got the underlying connectivity and integration at all of these different applications to bring that through. And I think that when we are effective at doing this and we're effective at sales execution that we can do this.

Unknown Analyst

And linking that, in terms of where you are in positioning with the client and the client understanding of that trying to -- me as a German, trying to kind of talk baseball again...

Matthew Quinn

[indiscernible]

Unknown Analyst

Actually, we should have that chat. Where about are we, in terms of the innings like in terms of...?

Matthew Quinn

In terms of big data in general?

Unknown Analyst

No, just more of your positioning of the different products in your area and just bring that vision across the customer base?

Matthew Quinn

Well, I think look, as we've mentioned in very beginning, I think that we have a very broad platform and I think the different spaces have evolved at different paces. I think that visual analytics, for example, if I'm going to use the baseball method here, I'd say we're about the third or fourth innings. I think that generally speaking, people have a better understanding today than what they certainly did 3 or 4 years ago in terms of what the visual analytics and data discovery market actually is. I think that it is very clearly kind of 2 or 3 main vendors who supply that. And I think everyone identifies with those 3 vendors. I think that the pretenders generally the kind of the broad or stack vendors who have tried to play in this space have been shoved out of the way. So I think that it's starting to mature. And I think that people outside of just the pure data science area are starting to appreciate the capabilities of these products and technologies bring. I think in areas like event correlation and kind of the broader logging space, event correlation as a space has been around for a while but it's always been a more missionary selling. I think it always kind of remained that way for a period of time. Logging has become obviously quite popular over the last 12 to 18 months. And perhaps it's maturing faster than people would maybe have given it credit a couple of years ago, both through acquisitions of companies like LogLogic and other factors in the marketplace.

Raimo Lenschow - Barclays Capital, Research Division

Yes, perfect. I think we're just running out of time. Matt, that was really helpful. Thank you. That was great.

Matthew Quinn

Thank you.

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Source: TIBCO Software Inc. Presents at Barclays Second Annual Big Data Conference, Feb-11-2013 04:35 PM

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