 Welcome back. We are here at the Splunk 2012 user conference, the third annual worldwide user conference. I'm Jeff Frick with siliconangle.tv. I'm here with my partner Jeff Kelly from wikibond.org, the preeminent big data analyst. I kind of keep coming up with new, new, new adjectives. We've, we've been here all day. This has been a great show. You're in the cube and the cube is giving you the full day coverage wall-to-wall of what's going on here at the Splunk show. We've had a number of executives, customers, partners and we'll continue for wrapping up today. It's our last segment I believe, Jeff. And then we will be back all day tomorrow. So we invite you to participate. We're glad you're along. The theme for the show is data journey. So participate using hashtag data journey. And with that, let me kick it over to Jeff Kelly. Thanks so much. Of course, we saved the best for last. Cube alum, Ron Valken, CEO, founder of Think Big Analytics. Been on a number of times with us. Great to have you back. Jeff, always a pleasure to be here. So tell us a little bit about what's going on here at the show, what you're doing here. Maybe just for our new viewers who haven't seen you on the cube. Tell us a little bit about what you guys do, a professional services firm in the big data space. Absolutely. So Think Big Analytics, we're a professional services firm that's purpose built for big data. We started the company about two and a half years ago with a real exclusive focus on helping the enterprise take advantage of all these new disruptive data capabilities that big data and big analytics data science can create. And so we're here because we're always excited about new technologies and this fast moving space. It's important to be aware of what's going on and we're watching Splunk closely. Yeah, I mean, you're in a position as a service provider and see a lot of the different players in the market from a technology perspective. What's your take on Splunk and kind of maybe the uptick in adoption you might be seeing or what's your take out there in the field in terms of how Splunk's being used? Absolutely. So we definitely see Splunk as building on a heritage of machine data and search to really tackle some new use cases integrating with big data technologies like Hadoop and NoSQL. So what we're seeing, for example, we have a large credit card customer that is using Splunk to do operations on big Hadoop cluster to help them get their arm around events that are going on the cluster to troubleshoot and diagnose things more quickly. So that's an example. We see also people are using Splunk to pull data into Hadoop clusters to feed data into Hadoop. And we also see Splunk going after the larger opportunity for being a kind of platform for streaming real-time big data. We think that that's still white space. Hadoop is very established and effective as a workforce, a batch analysis and data science platform. But that was you get into streaming real-time that there's a number of approaches and Splunk is really putting a lot of energy into being an effective platform there. Yeah, that's interesting because we're seeing a lot more activity in the streaming big data. We saw a notable kind of change direction a little bit there focusing on that. And obviously Twitter's open source storm platform is obviously another option. So that's kind of the white whale of the big data play. It makes sense of this data that's coming in in real-time so that you can do it fast enough so you don't lose the customer or that you can improve the process or catch the bad guy. Yeah, definitely streaming big data is a hot topic and to an additional Splunk you've got technologies like Storm that are out there. What we think is interesting too though is a lot of times there's a real blend that you want to do deep analytics, you want to be able to load all the data into for example Hadoop and do data mining analysis, detect patterns and build models and then you push out to do near real-time response. The most traditional way to do that in a big data space is to have a no-sequel database that holds all those models and you do differential updates to it as events come in. But what we're seeing is that as customers are starting to do more complicated patterns and flows of data in those real-time environments, they want to have a scalable infrastructure for managing all those data pipelines the way it runs that they want to have a fabric for the logic as well as a scale-out platform for the data. And that's where we're seeing the interest for more advanced users around these streaming big-time data platforms. So these customers are they are they reacting in fear that they've got to get a handle on what's going on and this is really a new investment in new technology that they want to expand or is it more you know just a better ROI more of a shift in technology and really more of a cost savings driver that's driving these kind of purchase decisions. Well I think I think that there's of course an important other motivation besides fear and cost savings which is opportunity to create new value right. So we were working with a media giant that's very excited about personalization and building intimate relationships with their customers. We're working with one of the largest retailers looking to drive very close you know improvements in how they put products in locations and work with customers and build intimacy. We're also working with companies large credit card companies that are doing things like targeting offers effectively to those consumers so they can be more accurate in the way they respond. So you know there's a range of companies that are driving personalization driving intimacy creating new businesses on top of big data as well as cost cutting and as well as you know fear is the flip side which is if you're late in some of those requirements to create value then you may have to respond and it won't be on your own terms. Instead of you defining the terms of how is the market shifting someone else will do it. We definitely see that there's an element of that starting to happen that when you look at the pioneers of big data the companies like my old company Quantcast or Google or Facebook or LinkedIn, eBay that they are often creating new offerings and products and forcing incumbents in industries to respond. If you look at the media industry the challenge of the old guard that haven't been intimate with their customers is they're being intermediated right they're finding that they're no longer allowed to have a direct relationship with their customer they're being relegated to being a content provider to someone else who owns their customer right they're not being put out of business but lack of facility to work with data and to build intimacy with customers is putting them in a very disadvantageous position. So we see enterprises waking up to the importance of investing in big data so they can create strategic value now instead of waiting for two years when they're under the gun to respond to an unpleasant change in their industry. And you know as a service is from how are you helping that transition when you go into clients I mean you must see a mix of both startups that maybe kind of get that right away and maybe older more traditional firms that maybe need a little more goading what's the kind of strategy in terms of helping people make that make that connection that hey we've got to be proactive here we can't wait you know wait the two years because then it's too late we couldn't pass by. Sure you know definitely we see we do a lot of work in the enterprise and one of the things we do is we have a envisioning services something we call a brainstorm where we come in and we help customers really think through what are the ways that they can create differentiated value out of their data what are the new ways of working with data what are the use cases that value add that apply to them in their industry and let them really drive meaningful changes to their business so that that becomes a foundation that co-creation of ideas that drives into an execution program that's nimble with quick releases of value that drives them to transform and creates that value and learning in the market for how to really differentiate and be part of the companies that are succeeding on big data. So that's our general engagement approach we think a lot of times the challenge for the enterprise is recognizing that this is a different game that it's not a case of applying the playbook from last decade and and going out and finding commodity providers with low skills that you can ship a spec and have them build something that this isn't about implementing a package that's a commodity it's about creating something new and differentiated that works for you it's about your data and analyzing it flexibly and that's different than anyone else's and it's about turning the equation backwards so instead of the old IT mantra was how quickly can we summarize and get rid of data and the new mantra they get is how much data can i use to be smart and create value right so that's you know how we engage the enterprise and when we engage with small nimble firms startups that are typically no longer startups but growing it's because they just want best in class expertise that we've got brilliant engineers and data scientists who can provide acceleration and thought leadership in their programs and they recognize that they have that what's most scarce for them is time and that they can't afford to do it wrong or make mistakes can you can you speak specifically to to maybe a fast-wind um say customer intimacy engagement that you know you got a big enterprise they want to get closer they're they're afraid of the disintermediation and you've come in you've done your brainstorm you know what are some of the kind of quick wins that you can demonstrate to them kind of the aha moment to the guy that's down at mahogany row that doesn't really buy it yet until you come in and show them sure i mean a lot of times we will come in and we'll help customers put some of their data about customers together so their customers 360 customer view and we've done things like helped provide immediate insight into what marketing and advertising channels were more or less effective to allow for decisions to drive better results that then led to investment and how to start to organize new data sets to start to build personalization programs to reach out to those customers so those are examples of how you can quickly get in and start to get the data together get some basic analysis and insight and go from that to more proactive model built excellent so you know one of the issues we've kind of seen over the last few years is that people sometimes get hung up on the technology and of course this is a you know a this is a big no-no you don't want to just invest in the technology because oh everyone's talking about that you think it might help you someday and and you invest and you've got no use cases you're talking about kind of quick wins how is that conversation changed with you with your your clients and people you see in the industry is are you going into there have been any trends as as you go into new engagements in terms of their understanding of what big data is you know big data does not necessarily equal Hadoop it can mean a lot of different things and how's that perception changed that you've seen well i'd say that there's definitely over the last two and a half years we've seen there's a lot more information out there but there's also a lot more misinformation out there right there's a lot of not at wikibon.org we're still playing all night not here but the other other outlets you know what you see is a lot of times customers well they've they've read a lot and they're they're thinking about the problem but they can be just paralyzed because there's so many options right and so one of the big things that we do is we help get a customer off the dime and say you know this is how you can get started and we know a way to build this successfully it's going to work so instead of we still often see companies that are stuck in an endless cycle of proof of concept where they're trying out all the different technologies and not creating any value and we want to guide customers to get started small pilot something and then build on that success so keep rolling out incremental releases and generate value yeah that's interesting because it kind of we were talking a lot today we actually heard two or three times talking about DevOps and the kind of the the ability to kind of connect development with operations to quickly iterate and kind of release you know new applications and new use cases quickly and that seems to be a real theme and a real important aspect of what makes big data so different than the way we used to do we used to think of data management with a very structured you know a set in stone data model and an enterprise data warehouse and if you hadn't changed a data source data source add one or take one away you've got to you know change the whole model and you've got a whole big project underway and that's really changing with this idea of big data do you think people are getting that are you still to have to convince people about this kind of new way of doing things well Demi I agree that it's it's very important point about big data that it's agile data we started saying look we think there's really four v's in big data you know everyone says volume variety and velocity and that's true but the actually the most important v is value your ability to get quickly value out of your data so and how was that it's because you can be agile and working the data and instead of committing up front to here's how we're going to organize the data and we'll get everything structured the classic enterprise data warehouses well we'll build a model that describes every question we'd ever want to answer and then once we've built it then we'll have a perfect system except that we'll never get there and this business will have changed and it will be we won't let too many people access it because it's too costly right those were the little drawbacks and the theory of the enterprise data warehouse so the big data approach of let's get in there let's put the data in a raw form and let smart people start to make sense of it and answer questions and we'll invest in what's working when we've proven there's value in a way of organizing structure and data we'll structure right so you don't in a big data system you don't dump everything in raw bits and have no structure but you structure things once it's been proven to be value so that's a big shift I'm sorry I didn't mean to interrupt have you seen a change in kind of the sponsors the titles and the role of the sponsors of your engagements and has it kind of shifted out of kind of the business the pure business analytics or the where the data center type of folks into more of the business people and you know are we going to see you know chief big data officer here before too long well you know definitely we see an increased partnership between business and technology so whether it be a marketing group whether it be product or engineering and a technology firm whether it be risk management but you'll see the business partnering with the technology team the IT team and in fact we think that's good I get worried when you create a separate group that's neither the business or the technology side as to whether they have the alignment and sponsorship but it is important to have those close partnerships because this is about creating a technical capability it's really about being scientific and changing the way you operate your business it's making fundamental automated decisions based on data that's a big shift from how business has been done and it takes that partnership that you need the business to really understand the metrics and understand where the value can be created and you need the technologists to implement it and make it successful hopefully with best in class support and then ultimately deliver it to the line guys who don't fit either of those models but are executing a lot of the little decisions in the day-to-day work too yeah I mean we see that increasingly you want to automate the routine decisions and let people the line workers deal with the exceptions that can't easily be automated so their work actually gets more interesting but it also it's a lot more efficient than having to deal with everything on a manual basis so we've talked about security use cases a lot today what are you seeing out there in terms of applying big data to catch the bad guys so to speak we talked with a few people today about that very topic and I think it's getting a lot more play these days with some high-profile data breaches what role can big data play in kind of securing your infrastructure but other maybe even other parts of your business well definitely big data provides you a lot of capacity and capability to work with information to find patterns to go back and mine historical data when you see in a pattern and see what your exposure was to correlate different data streams and to even starting to bring in unexpected data sets like can you do text mining for news to find people that might have a motivation now to break the law because they've just been convicted or gone bankrupt or there's a variety of these kind of use cases where you might use interesting other data sets that you weren't traditionally thinking about to intersect and start to get some insight into your problem what external data sets what partners data can you source and take advantage of in order to be effective in security so we've worked with a number of large enterprises that have interesting data sets in the space that are looking at how do they build more effective security including for example predictive models to be able to find malicious botnets before they're unleashed on the world right that's the key right before it does the damage or quickly they're out there I mean you want to be able to predict it before it happens but it's also often the case that as something is developing being able to characterize it and respond quickly can make a big difference all right well we're time for one more question so why don't you tell us a little bit about what you guys are doing in the coming six months year what are your priorities because I think I like to ask all our guests that that really helps us understand kind of where this market's going what are your priorities to think big analytics and what are you guys really jazzed about so you know think big we continue to be super excited about how the enterprise can take advantage of big data and create value and we see that really maturing in this next six months as we as companies are really starting to go beyond proofs the concept and invest in putting together the infrastructure in order to not only nimbly create applications but start to do data science and and create advanced analytics on top so we're seeing this maturation the enterprises tried some of the toe dipping and realized what is it really going to take to succeed here and we're seeing a lot of excitement going into the fall and next year for really taking advantage of big data to create unique business value and connect with customers and so how to out of potential customers link up with you guys so definitely we always welcome hearing from customers on our website thinkbiganalytics.com follow us on twitter I'm Ron Bodkin at Ron Bodkin on twitter would love to hear from you and certainly appreciate the opportunity to be here today all right great yeah if you're you're thinking about starting a big data practice definitely look these guys up think big analytics doing some really interesting work Ron thanks so much for being here again we appreciate it we'll see you again soon I'm sure on the queue thank you and Jeff I think that about wraps it up for today you're watching The Cube so look at Angle's premiere video production we're here live at .com 2012 Splunk's End User Conference third annual here at the Cosmopolitan Hotel in Las Vegas we've had a long but very interesting day with a lot of great guests we're gonna be back tomorrow morning around 10 a.m. for another complete day of coverage any final thoughts you wanted to want to share with the audience Jeff that was fantastic it's just great to have a service provider on who is kind of sits above the technology stack helps customers kind of sort their way through where do I go what are my options where do I start how do I deliver by I think your fourth V on big data is incredibly insightful and something that should be more widely adopted by more people because it is ultimately about the value it is what is this worth to me well thank you we definitely think there's a need for this new breed of integrator that's specialized in big data and we're excited to be there yeah I think that's you're in a great spot and really a great way to close out our day Jeff so I'm glad you saved the best for last although don't tell tell Eric and Rob that we will keep that to ourselves so thanks so much thanks for being with us today we had a great time we'll be here back here tomorrow morning as I mentioned 10 a.m. please join us then you can follow us the cube hashtag of course and data journey hashtag is the hashtag here for the Splunk conference but we'll see you here tomorrow morning at 10 a.m. Pacific at silkenangle.tv the cube