>> Announcer: Live from Las Vegas, it's theCUBE. Covering Splunk.conf19, brought to you by Splunk. >> Okay, welcome back everyone. We are here inside for Splunk.conf, their 10th-year conference. We've been here seven years. I'm John Furrier, the host. Our next guest is Sherrie Caltagirone, founder and executive director of the Global Emancipation Network, a cutting-edge company and organization connecting different groups together to fight that battle combating human trafficking with the power of data analytics. We're in a digital world. Sherrie, thanks for coming in. >> Thank you so much for having me. >> So love your mission. This is really close to my heart in terms of what you're doing because with digital technologies, there's a unification theme here at Splunk, unifying data sets, you hear on the keynotes. You guys got a shout-out on the keynote, congratulations. >> Sherrie: We did, thank you. >> So unifying data can help fight cybersecurity, fight the bad guys, but also there's other areas where unification comes in. This is what you're doing. Take a minute to explain the Global Emancipation Network. >> Yeah, thank you. So what we do is we are a data analytics and intelligence nonprofit, dedicated to countering all forms of human trafficking, whether it's labor trafficking, sex trafficking, or any of the sub types, men, women, and children all over the world. So when you think about that, what that really means is that we interact with thousands of stakeholders across law enforcement, governments, nonprofits, academia, and then private sector as well. And all of those essentially act as data silos for human trafficking data. And when you think about that as trafficking as a data problem or you tackle it as a data problem, what that really means is that you have to have a technology and data-led solution in order to solve the problem. So that's really our mission here is to bring together all of those stakeholders, give them easy access to tools that can help improve their counter posture. >> And where are you guys based and how big is the organization? What's the status? Give a quick plug for where you guys are at and what the current focus is. >> Yeah, perfect, so I am based in San Luis Obispo, California. We have just started a brand new trafficking investigations hub out at Cal Poly there. They're a fantastic organization whose motto is learn by doing, and so we are taking the trafficking problem and the tangential other issues, so like we mentioned, cyber crime, wildlife trafficking, drugs trafficking, all of this sort of has a criminal convergence around it and applying technology, and particularly Splunk, to that. >> Yeah, and I just want to make a note 'cause I think it's important to mention. Cal Poly's doing some cutting-edge work. Alison Robinson, Bill Britton, who runs the program over there, they got a great organization. They're doing a lot of data-oriented from media analysis, data, big focus there. Cal Poly quite a big organization. >> They are, and they're doing some wonderful things. AWS just started an innovation hub called the DX Hub there that we are a part of, really trying to tackle these really meaty problems here that are very data-centric and technology-centric. And Cal Poly's the best place to do that. >> Great, let's get into some of the details. One of the things around the news, obviously seeing Mark Zuckerberg doing the tour, Capitol Hill, DC, Georgetown, free speech, data. Facebook has been kind of blamed for breaking democracy. At the same time, it's a platform. They don't consider themselves as an editorial outlet. My personal opinion, they are, but they hide behind that platform. So bad things have happened, good things can happen. So you're seeing technology kind of being pigeonholed as bad. Tech for bad, there's also a tech for good. Pat Gelsinger, the CEO of VMware, publicly said technology's neutral. We humans can shape it. So you guys are looking at it from shaping it for good. How are you doing it? What are some of the things that are going on technically from a business standpoint that is shaping and unifying the data? >> Yeah, I mean, it's absolutely certain that technology has facilitated human trafficking and other ills throughout the world. It's a way that people bring their product, in this case, sadly, human beings, to the market to reach buyers, right? And technology absolutely facilitates that. But, as you mentioned, we can use that against them. So actually here at Conf we are bringing together for a first time the partnership that we did with Splunk for Good, Accenture, and Global Emancipation Network to help automatically classify and score risky businesses, content, ads, and individuals there to help not only with mitigating risk and liability for the private sector, whether it's social media giants or if it's transportation, hospitality, you name it, but also help ease the burden of content moderators. And that's the other side of it. So when you live in this space day in and day out, you really exact a mental toll here. It's really damaging to the individual who sits and reads this material and views photos over and over again. So using technology is a way to automate some of those investigations, and the identification of that content could be helpful in a variety of ways. >> In a way, it's a whole other adversary formula to try to identify. One of the things that Splunk, as we've been here at Splunk Conference, they've been about data from day one. A lot of data and then grew from there, and they have this platform. It's a data problem, and so one of the things that we're seeing here is diverse data, getting at more data makes AI smarter, makes things smarter. But that's hard. Diverse data might be in different data sets or silos, different groups. Sharing data's important, so getting that diverse data, how difficult is it for you guys? Because the bad guys can hide. They're hiding in from Craigslist to social platforms. You name it, they're everywhere. How do you get the data? What's the cutting-edge ingestion? Where are the shadows? Where are the blind spots? How do you guys look at that? Because it's only getting bigger. >> Absolutely, so we do it through a variety of different ways. We absolutely see gathering and aggregating and machining data the most central thing to what we do at Global Emancipation Network. So we have a coalition, really, of organizations that we host their scrapers and crawlers on and we run it through our ingestion pipeline. And we are partnered with Microsoft and AWS to store that data, but everything goes through Splunk as well. So what is that data, really? It's data on the open web, it's on the deep web. We have partners as well who look at the dark web, too, so Recorded Future, who's here at Conf, DeepL as well. So there's lots of different things on that. Now, honestly, the data that's available on the internet is easy for us to get to. It's easy enough to create a scraper and crawler, to even create an authenticated scraper behind a paywall, right? The harder thing is those privately held data sets that are in all of those silos that are in a million different data formats with all kinds of different fields and whatnot. So that is where it's a little bit more of a manual lift. We're always looking at new technologies to machine PDFs and that sort of thing as well. >> One of the things that I love about this business we're on, the wave we're on, we're in a digital media business, is that we're in pursuit of the truth. Trust, truth is a big part of what we do. We talk to people, get the data. You guys are doing something really compelling. You're classifying evil. Okay, this is a topic of your talk track here. Classifying evil, combating human trafficking with the power of data analytics. This is actually super important. Could you share why, for people that aren't following inside the ropes of this problem, why is it such a big problem to classify evil? Why isn't it so easy to do? What's the big story? What should people know about this challenge? >> Yeah, well, human trafficking is actually the second-most profitable crime in the world. It's the fastest-growing crime. So our best estimates are that there's somewhere between 20 million and 45 million people currently enslaved around the world. That's a population the size of Spain. That's nothing that an individual, or even a small army of investigators can handle. And when you think about the content that each of those produce or the traffickers are producing in order to advertise the services of those, it's way beyond the ability of any one organization or even, like I said, an army of them, to manage. And so what we need to do then is to be able to find the signal in the noise here. And there is a lot of noise. Even if you're looking at sex trafficking, particularly, there's consensual sex work or there's other things that are a little bit more in that arena, but we want to find that that is actually engaging in human trafficking. The talk that you mentioned that we're doing is actually a fantastic use case. This is what we did with Splunk for Good and Accenture. We were actually looking at doing a deep dive into the illicit massage industry in the US, and there are likely over 10,000 illicit massage businesses in the US. And those businesses, massages and spas, that are actually just a front for being a brothel, essentially. And it generates $2 billion a year. We're talking about a major industry here, and in that is a very large component of human trafficking. There's a very clear pipeline between Korea, China, down to New York and then being placed there. So what we ended up needing to do then, and again, we were going across data silos here, looking at state-owned data, whether it was license applications, arrest filings, legal cases, that sort of thing, down into the textual advertisements, so doing NLP work with weighted lexicons and really assigning a risk score to individual massage businesses to massage therapist business owners and then, again, to that content. So looking, again, how can we create a classifier to identify evil? >> It's interesting, I think about when you're talking about this is a business. This is a business model, this business continuity. There's a supply chain. This is a bona fide, underground, or overt business process. >> Yeah, absolutely, and you're right on that too that it is actually overt because at this point, traffickers actually operate with impunity for the most part. So actually framing it that way, as a market economy, whether it's shadowy and a little bit more in the black market or completely out in the open, it really helps us frame our identification, how we can manage disruptions, who need to be the stakeholders at the table for us in order to have a wider impact rather than just whack-a-mole. >> I was just talking with Sonia, one of our producers, around inclusiveness and this is so obviously a human passion issue. Why don't we just solve it? I mean, why doesn't someone like the elite class or world organization, just Davos, and people just say they're staring at this problem. Why don't they just say, "Hey, this is evil. "Let's just get rid of it." What's the-- >> Well, we're working on it, John, but the good thing is, and you're absolutely right, that there are a number of organizations who are actually working on it. So not just us, there's some other amazing nonprofits. But the tech sector's actually starting to come to the table as well, whether it's Splunk, it's Microsoft, it's AWS, it's Intel, IBM, Accenture. People are really waking up to how damaging this actually is, the impact that it has on GDP, the way that we're particularly needing to protect vulnerable populations, LGBTQ youth, children in foster care, indigenous populations, refugees, conflict zones. So you're absolutely right. I think, given the right tools and technology, and the awareness that needs to happen on the global stage, we will be able to significantly shrink this problem. >> It's classic arbitrage. If I'm a bad guy, you take advantage of the systematic problems of what's in place, so the current situation. Sounds like siloed groups somewhat funded, not mega-funded. This group over here, disconnect between communications. So you guys are, from what I could tell, pulling everyone together to kind of create a control plane of data to share information to kind of get a more holistic view of everything. >> Yeah, that's exactly it. Trying to do it at scale, at that. So I mentioned that at first we were looking at the illicit massage sector. We're moving over to the social media to look again at the recruitment side and content. And the financial sector is really the common thread that runs through all of it. So being able to identify, taking it back to a general use case here from cyber security, just indicators as well, indicators of compromise, but in our case, these are just words and lexicons, dollar values, things like that, down to behavioral analytics and patterns of behavior, whether people are moving, operating as call centers, network-like behavior, things that are really indicative of trafficking. And making sure that all of those silos understand that, are sharing the data they can, that's not overly sensitive, and making sure that we work together. >> Sherrie, you mentioned AWS. Teresa Carlson, I know she's super passionate about this. She's a leader. Cal Poly, we mentioned that. Splunk, you mentioned, how is Splunk involved? Are they the core technology behind this? Are they powering the-- >> They are, yeah, Splunk was actually with us from day one. We sat at a meeting, actually, at Microsoft and we were really just white boarding. What does this look like? How can we bring Splunk to bear on this problem? And so Splunk for Good, we're part of their pledge, the $10 million pledge over 10 years, and it's been amazing. So after we ingest all of our data, no matter what the data source is, whatever it looks like, and we deal with the ugliest and most unstructured data ever, and Splunk is really the only tool that we looked at that was able to deal with that. So everything goes through Splunk. From there, we're doing a series of external API calls that can really help us enrich that data, add correlations, whether it's spatial data, network analysis, cryptocurrency analysis, public records look-ups, a variety of things. But Splunk is at the heart. >> So I got to ask you, honestly, as this new architecture comes into play for attacking this big problem that you guys are doing, as someone who's not involved in that area, I get wow, spooked out by that. I'm like, "Wow, this is really bad." How can people help? What can people do either in their daily lives, whether it's how they handle their data, observations, donations, involvement? How do people get involved? What do you guys see as some areas that could be collaborating with? What do you guys need? How do people get involved? >> Yeah, one that's big for me is I would love to be able to sit in an interview like this, or go about my daily life, and know that what I am wearing or the things that I'm interacting with, my phone, my computer, weren't built from the hands of slave labor. And at this point, I really can't. So one thing that everybody can do is demand of the people that they are purchasing from that they're doing so in a socially viable and responsible way. So looking at supply chain management as well, and auditing specifically for human trafficking. We have sort of the certified, fair-trade certified organic seals. We need something like that for human trafficking. And that's something that we, the people, can demand. >> I think you're on the right track with that. I see a big business model wave where consumer purchasing power can be shifted to people who make the investments in those areas. So I think it's a big opportunity. It's kind of a new e-commerce, data-driven, social-impact-oriented economy. >> Yep, and you can see more and more, investment firms are becoming more interested in making socially responsible investments. And we just heard Splunk announce their $100 million social innovation fund as well. And I'm sure that human trafficking is going to be part of that awareness. >> Well, I'll tell you one of the things that's inspirational to me personally is that you're starting to see power and money come into helping these causes. My friend, Scott Tierney, just started a venture capital firm called Valo Ventures in Palo Alto. And they're for-profit, social impact investors. So they see a business model shift where people are getting behind these new things. I think your work is awesome, thank you. >> Yeah, thank you so much, I appreciate it. >> Thanks for coming on. Congratulations on the shout-out on the keynote. Appreciate it. The Global Emancipation Network, check them out. They're in San Luis Obispo, California. Get involved. This is theCUBE with bringing you the signal from the noise here at .conf. I'm John Furrier, back with more after this short break. (upbeat music)

>> Announcer: Live from Midtown Manhattan it's theCUBE. Covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Welcome back everyone live here in New York City this theCUBE's coverage of our fifth annual annual event that we put on ourselves in conjunction Strata Hadoop now called Strata Data. It's theCUBE and we're covering the scene here at Hadoop World going back to 2010, eight years of Coverage. I'm John Furrier co-host of theCUBE. Usually Dave Vellante is here but he's down covering the Splunk Conference and who was there yesterday was no other than Josh Rogers my next guest the CEO of Syncsort, you were with Dave Vellante yesterday and live on theCUBE in Washington, DC for the Splunk .conf kind of a Big Data Conference but it's a proprietary, branded event for themselves. This is a more industry even here at Big Data NYC that we put on. Welcome back glad you flew up on the on the Concord, the private jet. >> Early morning but it was was fine. >> No good to see you a CEO of Syncsort, you guys have been busy. For the folks watching in theCUBE community know that you've been on many times. The folks that are learning more about theCUBE every day, you guys had an interesting transformations as a company, take a minute to talk about where you've come from and where you are today. Certainly a ton of corporate development activity in your end it, as you guys are seeing the opportunities, you're moving on them. Take a minute to explain. >> So, you know it's been a great journey so far and there's a lot more work to do, but you know Syncsort is one of the first software companies, right. Founded in the late 60's today has a unparalleled franchise in the mainframe space. But over the last 10 years or so we branched out into open systems and delivered high performance data integration solutions. About 4 years ago really started to invest in the Big Data space we had a DNA around performance and scale we felt like that would be relevant in the Big Data space. We delivered a Hadoop focused product and today we focus around that product around helping customers ingest mainframe data assets into their into Hadoop clusters along with other types data. But a specific focus there. That has lead us into understanding a bigger market space that we call Big Iron to Big Data. And what we see in the marketplace is that customers are adapting. >> Just before you get in there I love that term, Big Iron Big Data you know I love Big Iron. Used to be a term for the mainframe for the younger generation out there. But you're really talking about you guys have leveraged experience with the installed base activity that scale call it batched, molded, single threaded, whatever you want to call it. But as you got into the game of Big Data you then saw other opportunities, did I get that right? You got into the game with some Hadoop, then you realize, whoa, I can do some large scale. What was that opportunity? >> The opportunity is that you know large enterprise is absolutely investing heavily in the next generation of analytic technologies in a new stack. Hadoop is a part of that, Spark is a part of that. And they're rapidly adopting these new infrastructures to drive deeper analytics to answer bigger questions and improve their business and in multiple dimensions. The opportunity we saw was that you know the ability for those enterprises to be able to integrate this new kind of architecture with the legacy architectures. So, the old architectures that were powering key applications impede key up producers of data was a challenge, there was multiple technology challenges, there's cultural challenges. And we had this kind of expertise on both sides of the house and and we found that to be unique in the marketplace. So we put a lot of effort into understanding, defining what are the challenges in that Big Iron to Big Data space that helped customers maximize their value out of these investments in next generation architectures. And we define the problem two ways, one is our two components. One is that people are generating more and more data more and more touch points and driving more and more transactions with their customers. And that's generating increased load on the compute environments and they want to figure out how do I run that, you know if I have a mainframe how to run as efficiently as possible contain my costs maximize availability and uptime. At the same time I've got all this new data that I can start to analyze but I got to get it from the area that it's produced into this next generation system. And there's a lot of challenges there. So we started to isolate, you know, what are the specific use cases the present customers challenge and deliver very different IT solutions. Overarching kind of messages around positioning is around solving the Big Iron to Big Data challenge. >> You guys had done some acquisitions and been successful, I want to talk a little bit about the ones that you like right now that happened the past year or two years. I think you've done five in the past two years. A couple key notable ones that set you up kind of give you pole position for some of these big markets, and then after we talk then I want to talk about your ecosystem opportunity. But some of the acquisitions and what's working for you? What's been the big deals? >> So the larger the larger we did in 2016 was a company called Trillium, leader in the data quality space. Long time leader in the data quality space and the opportunity we saw with Trillium was to complement our data movement integration capabilities. A natural complement, but to focus very specifically on how to drive value in this next generation architecture. Particularly in things like Hadoop. what I'd like to be able to do is apply best in class data quality routines directly in that environment. And so we, from our experience in delivering these Big Data solutions in the past, we knew that we could take a lot of technology and create really powerful solutions that were that leverage the native kind of capabilities of Hadoop but had it on a layer of you've proven technology for best in class day quality. Probably the biggest news of the last few weeks has been that we were acquired by a new private equity partner called Centerbridge Partners. In that acquisition actually acquired Syncsort and they acquired a company called Vision Solutions. And we've combined those organizations. >> John: When did that happen? >> The deal was announced July, early July and it closed in the middle of August. And vision solutions is a really interesting company. They're the leader in high availability for the IBM i market. IBM i was originally called AS/400 it's had a couple of different names and a dominant kind of market position. What we liked about that business was A. That market position four thousand customers generally large enterprise. And also you know market leading capability around data replication in real time. >> And we saw IBM. >> Migration data, disaster recovery kind of thing? >> It's DR it's high availability, it's migrations, it's also changed data capture actually. And leveraging all common technology elements there. But it also represents a market leading franchise in IBM i which is in many ways very similar to the mainframe. Run optimized for transactional systems, hard to kind of get at. >> Sounds like you're reconstructing the mainframe in the cloud. >> It's not so much that, it's the recognition that those compute systems still run the world. They still run all the transactions. >> Well, some say the cloud is a software mainframe. >> I think over time you'll see that, we don't see that our business today. There is a cloud aspect our business it's not to move this transactional applications running on those platforms into the cloud yet. Although I suspect that happens at some point. But our point, our interest was more these are the systems that are producing the world's data. And it's hard to to get. >> There are big, big power sources for data, they're not going anywhere. So we've got the expertise to source that data into these next generation systems. And that's a tricky problem for a lot of customers, and and not something. >> That a problem they have. And you guys basically cornered the market on that. >> So think about Big Iron and Big Data as these two components, being able to source data and make a productive using these next generation analytics systems, and also be able to run those existing systems as you know efficiently as possible. >> All right, so how do you talk to customers and I've asked this question before so I just ask again, oh, Syncsort now you got vision you guys are just a bunch of old mainframe guys. What do you know about cloud native? A lot of the hipsters and the young guns out there might not know about some of the things you're doing on the cutting edge, because even though you have the power base of these old big systems, we're just throwing off massive amounts of data that aren't going anywhere. You still are integrated into some cutting edge. Talk about that, that narrative, and how you. >> So I mean the folks that we target. >> I used cloud only as an example. Shiny, cool, new toys. >> Organizations we target and our customers and prospects, and generally we we serve large enterprise. You know large complex global enterprises. They are making significant investments in Hadoop and Splunk and these next generation environments. We approach them and say we believe to get full value out of your investments in these next generation technologies, it would be helpful if you had your most critical data assets available. And that's hard, and we can help you do that. And we can help you do that in a number of ways that you won't be able to find anywhere else. That includes features in our products, it includes experts on the ground. And what we're seeing is there's a huge demand because, you know, Hadoop is really kind of you can see it in the Cloudera and Hortonworks results and the scale of revenue. This is a you know a real foundational component data management this point. Enterprises are embracing it. If they can't solve that integration challenge between the systems that produce all the data and, you know, where they want to analyze the data There's a there's a big value gap. And we think we're uniquely positioned to be able to do that, one because we've got the technical expertise, two, they're all our customers at this point, we have six thousand customers. >> You guys have executed very well. I just got to say you guys are just slowly taking territory down you and you got a great strategy, get into a business, you don't overplay your hand or get over your skis, whatever you want to call it. And you figure it out and see if was a fit. If it is, grab it, if not, you move on. So also you guys have relationships so we're talking about your ecosystem. What is your ecosystem and what is your partner strategy? >> I'll talk a little bit about the overall strategy and I'll talk about how partners fit into that. Our strategy is to identify specific use cases that are common and challenging in our customer set, that fall within this Big Iron to Big Data umbrella. It's then to deliver a solution that is highly differentiated. Now, the third piece of that is to partner very closely with you know the emerging platform vendors in the in the Big Data space. And the reason for that is we're solving an integration challenge for them. Like Cloudera, like Hortonworks, like Splunk. We launched a relationship with Calibra in the middle the year. We just announced our relationship. >> Yeah, for them the benefits of them is they don't do the heavy lifting you've got that covered. >> We can we can solve a lot of pain points they have getting their platforms setup. >> That's hard to replicate on their end, it's not like they're going to go build it. >> Cloudera and Hortonworks, they don't have mainframe skills. They don't understand how to go access >> Classic partnering example. >> But that the other pieces is we do real engineering work with these partnerships. So we build, we write code to integrate and add value to platforms. >> It's not a Barney deal, it's not an optical deal. >> Absolutely. >> Any jazz is critical in the VM world of some of the deals he's been done in the industry referring to his deal, that's seems to be back in vogue thank God, that people going to say they're going to do a deal and they back it with actually following through. What about other partnerships, how else, how you looking at partnering? So, pretty much, where it fits in your business, are people coming to you, are you going to them? >> We certainly have people coming to us. The the key thing, the number one driver is customers. You know, as we understand use cases, as customers introduce us to new challenges that they are facing, we will not just look at how do we solve it, but and what are the other platforms that we're integrating with, and if we believe we can add unique value to that partner we'll approach that partner. >> Let's talk customers, give me some customer use cases that you're working on right now, that you think are notable worth highlighting. >> Sure so we do a lot in the in the financial services space. You know we have a number of customers >> Where there's mainframes. >> Where there's a lot of mainframes, but it's not just in financial services. Here's an interesting one, was insurance company and they were looking at how to transition their mainframe archive strategy. So they have regulations around how long they have to keep data, they had been using traditional mainframe archive technology, very expensive on annual basis and also unflexible. They didn't have access to. >> And performance too. At the end of the day don't forget performance >> They want performance, this was more of an archive use case and what they really wanted was an ability both access the data and also lower the cost of storing the data for the required time from a regulation perspective. And so they made the decision that they wanted to store it in the cloud, they want to store it in S3. There's a complicated data movement there, there's a complicated data translation process there and you need to understand the mainframe and you need to understand AWS and S3 and all those components, and we had all those pieces and all that expertise and were able to solve that. So we're doing that with a few different customers now. But that's just an example of, you know, there's a great ROI, there's a lot more business flexibility then there's a modernization aspect to it that's very attractive. >> Well, great to hear from you today. I'm glad you made it up here, again you were in DC yesterday thanks for coming in, checking out to shows you're certainly pounding the pavement as they say in New York, to quote New Yorker phrase. What's new for you guys, what's coming out? More acquisitions happening? what's the outlook for Syncsort? >> So were were always active on the M&A front. We certainly have a pipeline of activities and there's a lot of different you know interesting spaces, adjacencies that we're exploring right now. There's nothing that I can really talk about there >> Can you talk about the categories you're looking at? >> Sure you know, things around metadata management, things around real-time data movement, cloud opportunities. There's there's some interesting opportunities in the artificial intelligence, machine learning space. Those are all >> Deep learning. >> Deep learning, those are all interesting spaces for us to think about. Security and other space is interesting. So we're pretty active in a lot of adjacencies >> Classic adjacent markets that you're looking at. So you take one step at a time, slow. >> But then we try to innovate on, you know, after the catch, so we did three announcements this week. Transaction tracing for Ironstream and a kind of refresh of data quality for Hadoop approach. So we'll continue to innovate on the organic setup as well. >> Final question the whole private equity thing. So that's done, so they put a big bag of money in there and brought the two companies together. Is there structural changes, management changes, you're the Syncsort CEO is there a new co name? >> The combined companies will operate under the Syncsort name, I'll serve as the CEO. >> Syncsort is the remaining name and you guys now have another company under it. >> Yes, that's right. >> And cash they put in, probably a boatload of cash for corporate development. >> The announcement the announced deal value was $1.2 billion a little over $1.2 billion. >> So you get a checkbook and looking to buy companies? >> We are we're going to continue, as I said yesterday, to Dave, you know I like to believe that we proved the hypothesis were in about the second inning. Can't wait to keep playing the game. >> It's interesting just, real quick while I got you in here, we got a break coming up for the guys. Private equity move is a good move in this transitional markets, you and I have talked about this in the past off-camera. It's a great thing to do, is take, if you're public and you're not really knocking it out of the park. Kill the 90 day shot clock, go private, there seems to be a lot of movement there. Retool and then re-emerge stronger. >> We've never been public, but I will say, the Centerbridge team has been terrific. A lot of resources there and certainly we do talk we're still very quarterly focused, but I think we've got a great partner and look forward to continue. >> The waves are coming, the big waves are coming so get your big surfboard out, we say in California. Josh, thanks for spending the time. Josh Rogers, CEO Syncsort here on theCUBE. More live coverage in New York after this break. Stay with us for our day two of three days of coverage of Big Data NYC 2017. Our event that we hold every year here in conjunction with Hadoop World right around the corner. I'm John Furrier, we'll be right back.

okay we're back here live at the IBM iod information on demand conference hashtag IBM iod this is the cube so looking the anglo Mookie bonds flagship program we go out for the events extracting from the noise i'm john furrier might join my co-host Davey lonte and we'd love to have analysts in here and in this case former analyst James Cole Beatles welcome to back to the cube thank you very much John thank you Dave pleasure see you again finger of being at IOD you're a thought leader you are an influencer you work at IBM so you you're out there the front lines doing some great work so thank you very much tell us explains the folks out there not about the show because we've had some people coming in last year you were private in but what does this fit what is this vector in context to what's relevant the market obviously big data and analytics is the hottest thing on the planet right now and you got social business now emerging categorically here but it has a couple different flavors to it right within IBM's context yeah but the messaging is simple right you got analytics that drives value outcomes social business is the preferred way of people going to operate their businesses engagement and all that is great stuff new channels marketing eccentric cetera explain to them how I OD is fitting into these megatrends into mega trends I think the hottest trends why our customers caring about what's going on here is a lot of a lot of activity around customers what is what does IOD fit into that a bigger picture yeah well you know the world has changed the world culture has changed radically and really in the last decade or so none is everywhere in the world everything is now online and digital increasingly it's streaming in terms of culture look what's happening to Hollywood is being deconstructed by the netflixs of the world you know movies and TV and music and everything is delivered online now all engagement more more engagements with your employer with your you know with merchants with your family everywhere is online things like streaming media so if you look at how the world culture has changed I yesterday I spoke here on a topic that's near and dear to my heart called big media it's the support of the ascendance of streaming media and not just the area as I laid out but in education like MOOCs distance learning we use it internally at IBM for our think fridays and Ginni Rometty and the executive team you know every Friday its cloud or its big data or whatever you know we need all need to get up to speed on the world culture has changed now analytics is fundamental to that whole proposition in terms of world culture analytics driving gagement analytics in terms of you know in a business context analytics a 360-degree view and you have data warehouses and the master data and you have predictive models to drive segmentation and target marketing and all that good stuff you know that's been in business for a long time that those set of practices they have become prevalent in most industries now not just in say retailing you know the Amazons of the world they're pervasive across all industries big data is fundamental to that you know engagement model its social social in the sense that social is one of many channels through which business is engaged with through which many people engage the social is assumed assuming a degree of importance in the fabric of modern life that goes beyond simple you know engagement with you know brands and whatnot social is how people create is how they declare who they are it's their identity and so social in your personal life we all know about Facebook and Twitter and everything else and YouTube but social has revolutionized enterprise cultures everywhere you know we use social internally of course we use our own Lotus connections most large and even many mid-sized firms now use social for interactions among employees or throughout their Val you chain so social business is about all of that it's the b2c it's the b2b it's the e2e and employ to employ all these different models of engagement they all demand a number of things obviously the social platform they demand the data of various sorts structured unstructured in shared repositories or cubes or Mars or whatnot they it demands the the big data platforms not only at respite in motion the streaming media to make it all happen in real time so at IOD if you see what the themes are this year and really it's been a building for several years cloud everything social is running in the cloud now more and more not just public Claus but Federation's of public and private clouds it's it's all about cognitive computing which is a relatively new term in the Sun sets achieved a certain amount of vogue in the last year or so which is really fundamentally as an evolutionary trend it's basically a I for the 21st century but leveraging unstructured data and and machine learning and so forth and predictive analytics and you know well the whole world learn what metadata was with the whole NSA yeah comments no it's like me and then just to wrap it up in memory real-time blu acceleration you know you need real-time you need streaming you need collaboration and social you know peer-to-peer user-generated content all of that to make this new world culture really take off and IBM provides all that we recognize that that's where the world's going we've been orienting reorienting all of our solutions around these models cloud social increasingly going forward and you know we provide solutions that enable our customers in all industries to go there and big data is fundamental to all of that as we say we're computer science meets social science that's always been Silicon angles kind of masthead view but to unpack what you just said from the market relevance you mentioned Netflix we saw Amazon coming out their own movie they're going to go direct with their own programming so so but that speaks to the direct business model of the web was originally pioneered as hey direct business model cut the middleman out but now that dimension has been explored so that kind of what you're saying there so that's cool the end user pieces interesting image is social so what's your take on the end user orientation what's the expectation because you got social you got a trash you got in motion you got learning machines providing great recommendations got the Watson kind of yeah reasoning for people so personalization recommendation engines the sea change attention time currency big days of all those buzzwords all right what is the expectation for users in the future right now we're moving into this new world where I can self serve myself monologue based the information from the web now it's all coming at everyone real time the alarms are going off as Jeff Jonas says what is that prefer user experience the direct business model people get that I think the business to see that but now the end users are now at the center of the value proposition how do what's the role of the user now they're participating in the media there are also consumers of the media yeah and they now have different devices so what's the sources of data so fundamentally yeah the role of the consumers expectations now is always everything is always on everything is always online everything is all digital everything is all real time and streaming everything is all self-service everything is all available in the palm of my hand and then the back-end infrastructure the cross-channel infrastructure users don't care about individual socials they really don't they don't really fundamentally care about Facebook or Twitter or whatever you have they just care that what their experience is seamless as they move from one channel to another they're not perceived as channels anymore they're simply perceived as places or communities that overlap too in a dizzying array of socials thus social is where we all live and thus social increasingly is mobile increasingly mobile is you know the user expects that the handoff from my smartphone to my tablet to my laptop to my digital TV sentence and so forth that it all happens through the magic of infrastructure that it's being taken care of and they don't have to worry about that handoff it all it's all part of one seamless experience yeah they always just say the search business it's the it's the it's the intersection of contextual and behavioral yeah and now you take that online behaviors community contextual is context to what people are interested at any given time yeah it's so many longtail distributions at any given time so do you see the the new media companies that the new brands that might emerge mean there's all the talk about Marissa Mayer kind of turning over yahoo and yeah she some say putting lipstick on a pig but but but is that they're just an old older branch trying to be cool but is that what users want just like media but just user experience me like we're small media but we got big ideas but the thing is the outcomes right small frying big blues go figure are the outcomes still the same company still want to drive sales for their business sell a product provide great value you just want to find great content and find people I mean the same concept of the old web search find out and run sumit give any vision on how that environment will evolve for a user like is it going to be pushed at me do you see it a new portal developing is mmm Facebook's kind of a walled garden humble don't care about that what's your take on that the future vision of a user experience online user experience online future vision in many ways I think let's talk about Internet of Things because that keeps coming more and more into the discussion it's it's not so much that the user wants a seamless experience across channel cross device all that but a big part of that experience is the user knows that increasingly they'll have some confidence that whatever environments physical environments there in our being obviously there's privacy implications that surveillance here are being monitored and tracked and optimized to meet their requirements to some degree in other words environmental monitoring internet of things in your smart home you want to configure so you smart home so that every room that you walk into is as you as you're moving there even before you get there has already been optimized to your needs that ideally there should prediction Oh Jim's walking into the bathroom so turn the light on and also start to heat up the water because it's ten o'clock at night Jim's usually takes his bath around this time you sort of want that experience to be handled by the internet of things like nest these new tools like nest oh yeah yeah so essentially then it's my user experience is not just me interacting with devices but me simply moving through environments that are continuously optimized to my knees and needs of my family you know the whole notion of autonomous vehicles your vehicle if it's your personal vehicle then you want to always autumn optimize the experience in terms of like you know the heat setting and and the entertainment justement saan the you know the media center and they're always to be tailored to your specific needs at any point in time but also let's say you take a zipcar you rent a zipcar and you've got an ID with that company or any of the other companies that provide those on-demand rental car services ideally in this scenario that whatever vehicle you you rent through them for a few hours or so when you enter it it becomes your vehicle is completely customized to your needs because you're a loyal customer of that firm and they've got your profile information this is just a hypothetical I'm not speaking to anything that I actually know about what they're doing but fundamentally you know ideally any on-demand vehicle or conveyance or other item that you you lease in this new economy is personalized to your needs while you're using it and then as it were depersonalized when you check it back in so the next person can have it personalized to their use as long as they need it that's the vision of a big part of the vision of customer experience management personalization not just of your personal devices but personalization of almost any device or environment in which you are operating so that's one kanodia wants this question no I would ask one more question on that on the user experience came on Twitter from a big data alex says while you're on the subject which a my Alex I don't great great friend of the cube but thanks for the tweet today we don't have our crowd shado-pan we can get the chat going there but why not talk about AR and I've been in reality I mean honestly Internet of Things is now not the palm of your hand it could be on your wrist or on your clothing the wearables on the glasses and just gave out three invites to google glass so this is again another edition augmented reality is software paradigm as well what is that what is it what does that fit into that what's your take on augmented reality augmented reality ok so augmented reality is that which I don't use myself I've just simply seen it demonstrated and plenty of places so augmented reality is all about layers of additional information overlaid on whatever visual video view or image view that you happen to be carrying with you or have available to you while you're walking around in your normal life so right now conceivably if this is an AR a setting that I would environment or enabled device I would be able to see for example that ok who's in this room in the sense that who is declared that they are in this area of Mandalay Bay right now and why specifically are they doing to the extent that they allow that information to be seen and o of these people here which of these people if any might be the person I'm going to be speaking with it for 30 so that if they happen to be in this environment i can see that i can see that they're to some degree they may have indicated status waiting for james could be a list to get done with the Wikibon people oh that's kind of cool so I'd see that overlay and I walk to other parts of the Convention Center I might also see overlays as I walk around like oh there's a course down as several rooms down that I actually put in my schedule it's going to start in about five minutes I'll just duck you into there because it reminds me through the overlay that's the whole notion of personalization of the environment in which you're walking around in real time dynamically and contextual in alignment with your needs or with your requirements are in alignment also with these whatever data those environment managers wish to share to anybody who's subscribing in that contact so that's a context-aware that theme have been talking about here on textual essentially it's a public space that's personalized to your needs in the sense that you have a personalized view in a dynamically update okay that sounds like crowd chat Oh are we running a trip crouched at right now crouch at San overlay so just as lovely overlay so look to the minute social network yeah tailored to the needs of the group yep that adds value on top of that data yeah so James I gotta get your take on something so we had Merv on yesterday great Adrian with my great Buy analyst day and he was on last week at Big Data NYC you know we did our own little vent there Don coincident with hadoop world so Murph said well we're just entering the trough of disillusionment for big data yeah you love those Gartner you know I love medications tools I mean they are genius and I get him but he said that's a good thing because it goes left to right so we're making progress here ok right but I'm getting nervous the internet of things I love the concept we don't we don't work on industrial internet and you know a smarter planet it's in there so I love it but I'm getting nervous here's why I look back at a lot of the promises that were made in the BI days 360-degree other business predictive analytics a lot of things that are now talking about in the hood sort of Hadoop big data movement that we're actually fulfilling with this new wave that the old wave really wasn't able to fill because the cousin sort of distracted doing sarbanes-oxley and reporting in and balanced scorecards so so I'm nervous he's old school now it when he when he referenced is something that was hot in the mid part of the two thousand decade okay go ahead okay we had a guy on today talking about balance core would you know we're just talking about crowd chat that's the hottest day in 2013 like five years or hurt anybody mentions sarbanes-oxley so what kind of saved that whole business Roy thank you and Ron but so heavy right so what I'm nervous about as we as I've seen a number of waves over the years where the the vendor community promises a vision great vision great marketing and then all of a sudden something hotter comes along like Internet of Things and says don't know this is really it so my question to you is will help us it'll help me in my mind you know close that dissonance gap is are these two initiatives the sort of big data analytics for everybody putting analytics in the hands of business users yeah or is that sort of complementary to the internet of thing his internet of things just the new big trillion dollar market that everybody's going to go after and forget about all those promises about analytics everywhere help me sure Jay through that my job is to clarify confusion hey um you know if you look at the convergence of various call them paradigms there's a lot of big data analytics is one of them right now clearly there's cloud clearly their social there's big data analytics in mobile and there's something called Internet of Things so some some talk about smack smac social mobile analytic a que a big data cloud if you add IOT of there it's smack yet I don't think it works or smash yet but fundamentally if you think about Internet of Things it's it's all about machines or automated devices of various sorts probes and you know your smartphone and whatever I know servers or even you know the autonomous vehicles those are things that do things and you know they might be sources of data they would are they might be consumers of data they might conceivably even be intermediaries or brokers or routers or data what I'm getting at is that if you look at big data analytics I always think of it as a pipeline all data it's like data sources and data consumers and then there's all these databases and other functions that operate between them to move data and analytics and insight from one end to the other of the pipe in a conceptual way think of the internet of things as well a new category of sources of data these devices whether they be probes or monitors or your smart phones and new consumers and they all those same things are probably going to be many of them consumers of data and there's message passing among them and then the data that they passed might be passed in real time through streaming like InfoSphere streams it might be cached or stored and various intermediate databases and various analytics performed on them so think of you know I like to think of the internet of persons places and things persons that's human endpoints consumers and and sources of data that's all of us that's social places that's geospatial you know you think about it the Internet of geospatial you know geo spatial coordinates of of data and analytics and then there's things there's you know automated endpoints or you know hardware even Nana from macro to nano devices so it's just a new range of sources and and consumers of data and new types of analytics that are performed in new functions that can be performed and outcomes enable when you as it were stack in and out of things with social with claw with mobile new possibilities in terms of optimization in real time it throughout the you know the smarter planet if you think about the smarter planet vision it's all about interconnected instrumented and intelligent instrumented you know instrumentation that traditionally it suggests hardware instrumentation that's what probes our sensors and actuators that's the Internet of Things it's a fundamental infrastructure within smarter planet I'd love that thank you for clarifying i could write a blog post out of that and i think i'm very well made so um now i want to follow up and bring it back to the users I know snack and I thought you were going to say a story no smack MapReduce analytics and query or sell smack on the cube so so I want bring it back to the users so we had a great conversation yesterday actually last week I'll be met it was on off you know ah be met and he said look why are there any any you know where all the big data apps he said you need three things to for big data apps you need domain expertise you need algorithms which are free and you need data scientists like oh we'll never get there all right oh so rules really free while there are that was this argument yeah it means a source if people charge him for algorithms big trouble was this point I think okay sure so and then we had a discussion yesterday about how in the early days of the automobile industry you know the forecast was this is problematic the gap to adoption is just aren't enough chauffeurs know the premise that we were putting forth in the discussion yesterday I don't know who that was with was that with Judith it was good was that look we've got to figure out a way to get analytics in the hands of the business user we can't have to go through a data scientist or some business analyst no that's not going to work and we'll never get adoption so what what's going to bridge that gap is it is it the things you talked about before all these you know cool solutions that you guys are developing the project neo that you announce today visualization yeah there's another piece of that what puts it in the hands of guys like me that I can actually use the data in new and productive ways yeah well self-service business intelligence and visualization tools that are embedded in the very experience of using apps for example on your smartphone democratization of data science down to all of us you need the right tools you need you need the tools that the new generation of people like my children's generation just adopt and they work in there just a tune from from the cradle to working with data and visualizations and creating visual you know analytics of various sorts though they may not perceive it as being analytics they miss may perceive it as working with shapes and patterns and stuff yeah you would stop yeah so playing around you know in a sandbox i love that terminology data scientists working you know sandboxes which is data that's martes that they build to do regression analysis and segmentation and decision trees and all you know all that good stuff you know the fact is your sandbox can conceivably be completely on your handheld device with all the visualizations built-in you're simply doing searches and queries you know you're asking natural language questions you're looking at the responses you're changing your queries you're changing your visualizations and so forth to see if anything pops out at you as being significant playing around it you know it's as simple a matter that that these kinds of tools such as IBM you know cognos and so forth enable everybody to become as it worried a data scientist without having to you know become a maquette their profession it's just a part of the fabric of living in modern society where data surrounds us people are going to start playing with data and they're going to start teaching themselves all these capabilities in the same way that when they invented automobiles and you know wasn't Henry 42 invented them it was in like the late 1800s by engineers in Europe and America you know it's like we didn't all become auto mechanics you know there are trained auto mechanics but I think most human beings in the modern world know that there's a thing called an automobile that has an engine that needs gasoline and oil and occasionally needs to be brought to a professional mechanic for a repair and so forth we have many of us have a rough idea of something called a carburetor blah blah blah you know in the same way that when computers came up after world war two and then gradually invaded our lives through PCs and everything we all didn't become computer scientist but most of us have an idea of what a hard disk is most of it no most of us know something about something called software and things are called operating systems in the same way now in this new world most of us will become big data analytics geeks practical into the extent that will learn enough of the basic terms of art and the relationships among the various components to live our lives and when the stuff breaks down we call the likes of IBM to come and fix it or better yet they just buy our products and they just work magically all the time without fail conversing and comfortable with the concepts to the point which you can leverage them and what about visualization where does that fit visualization visualization is where the rubber meets the road of analytics is it's where human beings how human beings extract meaning insight fundamentally maybe that's like yeah you extracted inside a lots of different ways you do searches and so forth but to play around it to actually see you know a heat map or a geospatial map or or or you know a pie chart or whatever you see things with your eyes that you may not have realized we're there and if you can play around and play with different visualizations against the same data set things will pop out that you know the statistical model just seek the raw output of a data mining our predictive model or statistical analysis those patterns may not suggest themselves and rows of numbers that would pop out to an average human being or to a data scientist they need the visualizations to see things that you know because in other words when you think about analytics it's all about the algorithms that are drilling through the data to find those patterns but it's also about the visualizations the algorithms and you need the visualizations and of course you need the data to really enable human beings of all levels of expertise to find meaning and fundamentally visualizations are a lingua franca between non-expert human beings and expert eamon beings between data scientists visualizations are a lingua franca Hey look what I saw what do you think you know that's the whole promise of tools like concert for example we demonstrated this this morning it's a collaborative environment as sharing of visualizations and data sets and so forth among business analysts and the normal knowledge worker you know it with it you know like what do you see here's what I see what do you think I don't see that here's another visualization what do you see there oh yeah I think I see what you mean and here's my annotation about what I have broader context I've you know here's what I oh this is great that's the whole notion of humans deriving insight we derive it in socials we derive it in teams of that some Dave might be adept at seeing things that Jim is just absolutely blind to or you know Nancy might see things that both of us are applying to but we're all looking at the same pictures and we're all working with the same data part art yeah it's all so let's talk about some plumbing conversations you know one of the things that we noticed we were at the splunk conference this year's blown came out of nowhere taking log files making them manageable saving time for people so the thing that comes out of the splunk conversation is that it's just so easy to use that their customer testimonials are overwhelmingly positive around the area hey I just dumped my data into this the splunk box and it grid good stuffs happening I can search it it can give me insight save me time so that's the kind of ease of use so so how does IBM getting to that scenario because you guys have some good products we've got on the platform side but you also have some older products legacy Lotus other environments collaborative software that's all coming together in converging so how do we get to that environment where it's just that he just dumped your data in and let it do its magic well Odin go that's the very proposition that we provide with our puresystems puredata systems portfolio tree data system and big insights right for Hadoop so forth big in size you know we have an appliance now yeah we have pdh so that's the whole create load and go scenario that because Bob pidgeotto unless wretched and others demonstrated on the main stage yesterday and today so we did we do that and we are simple and straight being easy to use and so forth that's our value prop that's the whole value prop of an appliance you know simple you don't need a ton of expertise we pre build all the expert in a expertise patterns that you can use to derive quick value from this deployment we provide industry solution accelerates from machine data analytics on top of big insights to do the kinds of things you're talking about with splunk offerings so fundamentally you know that's scenario we all we and we're you know we have many fine competitors we offer that capability now in terms of the broader context you're describing we're a well-established provider of solutions we go back more than a hundred years we have many different product portfolios we have lots and lots of customers who would invested in IBM for a long time they might have our older products our newer products in various combinations we support the older generations we strive to migrate our customers to the newer releases when they're ready we don't force them to migrate so we make very we're very careful in our row maps to provide them with a migration path and to make it worth their while to upgrade when the time comes to the newer feature ok so I got it don't change gears to the to the shiny new toy conversation which is you know you know we love that in Silicon Valley what's a shiny new toy there's always an emerging markets when you have see changes like this where there's a whole the new whole new wave comes in creates new wealth old gets destructed new tags over whatever the conversation goes but I got to ask you okay well Elsa to the IBM landscape that you that you're over overlooking with big data and under the under the hood with cloud etc there's always that one thing that kind of breaks out as the leader the leading toy a shiny object that that people gravitate to as as I'm honest I won't say lost later because you got you know it's not not about giving away free it's it's the product that goes well we this is the lead horse you know and in this game right yeah so what is that what is the IBM thing right now that you're doubling down on is it blu acceleration is it incites is it point2 with a few highlights right now that's really cutting through the new the new the new soil of yeah we're developing our own rip off version of google glass thank you know I'm saying it's always I mean I'm gonna say shiny too but there's always that sexy product well I want that I want L customers name I want that product which leads more you know how she lifts for other products is there one is there a few you can talk about that you've noticed anecdotally is going to be specific data but just observational a shiny toy for the consumer market or for the business business business mark okay yeah yeah is it Watson is Watson the draw is it what's the headline looking for the lead lead dog here what's the attack there's always one an emerging market well you can put your the spot here well you could say that the funny thing is the whole notion of a shiny new toy implies something tangible when the world is gone more and more intangible in the cloud so we are moving our entire portfolio beginning links the big data analytics solutions into the cloud cloud first development going forward our other core principles for the pure data systems portfolio and the light for the shiny the shiny new thing the new cons could be shiny new concept or new paradigm yeah but the shiny new thing is the cloud the cloud is something pervasive and the cloud is something that it really multi form factors that's not very sexy but customers want flexibility you know they want to acquire the same functionality either as a licensed software package and running on commodity hardware we offer that for our big data analytics offerings or as an appliance and one sort or another that specialized particular occurrence or as a SAS cloud offering or as a capability that they can deploy in a virtualization layer on top of IBM or non-ibm hardware or they want the abilities you can mix and match those various deployment form factors so in many ways the whole notion of multi form factor flexibility is the shiny new thing it's the hybrid model for deployment of these capabilities on Prem in the cloud combination thereof that's not terribly sexy because it's totally it's totally abstract but it's totally real I mean demand wise people can see them that drives my business because when you go to the cloud I mean that's where you can really begin to scale seriously beyond the petabytes the whole notion of big media it will exist entirely in the cloud big media I like to think is the next sexy thing because streaming is coming into every aspect of human existence where stream computing a lot of people who focus on Big Data think of volume as being like big headline oh god we'd go to petabytes and exabytes and all that yeah it's important some really fixate on variety all these disparate sources of data and now we have all the sensor data and that's very important we have all the social media and everything all those new sources that's extremely important but look at the velocity everybody is expecting real-time instantaneous continuous streaming you know everything we do all of our entertainment all of our education surveillance you know everything is completely streaming I think ubiquitous streaming to every device and everybody themselves continue to continuing to stream their very lives everywhere all the time is the sexy new thing Dave and I talk about running data we coined that term running data what four years ago so I got to get you got to get kind of a thought leader they're watching us and we're watching streaming data right now from these said these are your guys are streaming this is big media give us some wanna get your thought leader perspective here some thought leader mojo around um the hashtag data economy you know you need now you're moving into a conversation with c-level folks and they said James tell me what the hell is this data economy thing right so what is the data economy in your words kind of like I mean I'll say it's a mindset I'll everything else what's your take on that we've been discussing that internally and externally at IBM we're trying to get our heads around what that means here's my take as one IBM are one thought Leigh right by the way the trick of being a thought leader is just to let your own thoughts lead you where they will turn around where all my followers yeah hopefully they want to lead you to far astray where you're out in the wilderness too long that's an important type of people are talking about because people are trying to put the definition around at economy can you actually have a business construct around yeah data here is my taken on the layers of the meaning of data economy it's monetizing your data the whole notion of monetization of your data data becomes a product that you generate internally or that you source from externally but you repackage it up and then resell with value add the whole notion of data monetization and you know implies a marketplace for data based products you know when I say data I'm using it in the broader context of it could be streaming media as the kind of one is a very valuable category of you know data like you know whatever kollywood provides so there's a whole notion of monetizing your data or providing a marketplace for others to monetize their data and you take a transaction fee from that or it also means in more of a traditional big data or data warehousing bi sense it means that you drive superior outcomes for your your own business from your own data you know through the usual method of better decision if better decisions on trustworthy data and the like so if you look at data monetization in terms of those layers including the marketplace including you know data-driven okay in many ways the whole notion of a data economy hinges on everybody's realization now that the chief resource for betterment of humanity one of the chief resources going forward for us to get smarter as a species on this planet is to continue to harness the data that we ourselves generate you know people stop what data is being the new oil what oil was there before we ever evolved but data wasn't there before we we landed on earth or before we evolved we generate that so it's our own exhaust your own exhaust that's actually a renewable resource data exhaust from data from exhausted gold that's what we say data is the data exhaust it's good if you can harness it and put it together as Jeff Jones says the puzzle piece is the picture the big picture at the smarter picture the smarter planet so on the final question I want to wrap up here to our next guest but what's going on with you these days talk about what's up with you you know you're very active on Facebook will you give a good following I'll be coming up what's happening you know I'll make sure I said big birthday for you on your Facebook page what's going on in your life I'll see you're working at IBM one of the things are interesting what's on your mind these days when you're at leisure are you hanging out you think what are you thinking about the most what are you doing with your you know things with your family's cherith let's see what's going on well I hang out at home with my wife and drink beer and listen to music and tweet about it everybody knows that stuff kind of beer do you drink whatever is on sale I'm not going to say where we buy it but it's a very nice place that whose initials are TJ but fundamentally you know my my mind is an open book because I evangelize I put my thoughts and my work thoughts and love my personal thoughts out there on socials I lived completely ons but I completely unsocial I self-edit but fundamentally the thought leadership I produce that the blogs and whatnot I produce all the time I put them out there for general discussion and I get a lot of good sort of feedback the world and including from inside of IBM I just try to stretch people's minds what's going on with me I'm just enjoying what I'm doing for a living now people save Jim you're with IBM why aren't you an analyst I'm still doing very analyst style work in in a vendor context I'm a thought leader I was a thought leader as I try to be being a thought leader is like being a humorist it's like it's a statement of your ambition not your outcome or your results yeah you can write jokes too you're blue in the face but if nobody laughs then you're not a successful comedian likewise i can write thought leadership pieces till I'm blue in the face but if nobody responds that I'm not leaving anybody anywhere i'm just going around in circles so my my ambition and every single day is to say at least one thing that might stretch somebody's box a little bit wider yeah yeah I think I think IBM smart they've been in social for a while the content markings about you know marketing to individuals yeah with credibility so I love analysts I love all my buds like like Merv and everybody else and I'm you know sort of a similar cat but you know there's a role for X analysts inside of solution providers and we have any number John Hegarty we have we have Brian Hill another X forest to write you know it's it's a you know it's a big industry but it's a small industry we have smart people on both sides of the equation solution provider and influencer my line um under people 99 seats and you know I I suck up to my superiors at IBM i suck up to any analyst who says nice things about me and hosts be on their show and i was going out of my life i'm just a big suck up well we like we like to have been looking forward to doing some crowd chats with you our new crouch an application with you guys lock you into that immediately it's a thought leader haven that the Crouch as as it turns out Dave what's your take on the analyst role at IBM just do a little analysis of the analyst at IBM which you're taken well I think it's under situation I think that the role that they that IBM's put James in is precisely the way in which corporations vendors should use former analysts they should give you a wide latitude a platform and and not try to filter you you know and you're good like that and so guess what I do the usual marketing stuff to the traditional but I do the new generation of thought leadership marketing and there's a role for both of those to me marketing have said this is if I said it was I said a hundred times marketing should be a source of value to people and it's so easy to make marketing a source of value by writing great content or producing great content so yeah that's my take on a jonathan your your marketing is a great explainer you explain the value to the market and thereby hopefully for your company generate demand hopefully in the direction of your cut your customers buying your things but that's what analysts the influencers should be explainers it's you know probably Dave I mean has influenced as influences that we are with with a qu here's my take on it when you have social media of direct full transparency there's no you can't head fake anyone anymore that all those days are gone so analyst bloggers people who are head faking a journalist's head faking the house the audiences will find out everything so to me it's like it's the metaphor of when someone knocks on your door your house and you open it up and they want to sell you something you shut the door in their face when you come in there and they say hey I want to hang out I got you know I got some free beer and a big-screen TV you want to watch some football maybe you invite him in the living room so the idea of communities and direct marketing's about when if you let them into your living room yeah you're not selling right you are creating value see what i do i drop smart i try to drop smart ideas into every conversational contacts throughout socials and also at events like i od so you know a big part of what I do is I thought leadership marketer is not just right you know you're clever blogs and all that but I simply participate in all the relevant conversations where I want I want ideas to be introduced and oh by they want way I definitely want people to be aware that I am an IBM employee and my company's provides really good products and services and support you know that's really a chief role of an evangelist in a high-tech slider that's one of the reasons why we started crouched at because the hashtag get so difficult to go deep into so creates crowd chatter let's go deeper and have a conversation and add some value to it you know it's you thinking about earned media as parents been kicked around but in communities the endorsement of trust earning a position whether you work at IBM people don't care a he works at IBM or whatever if you're creating value and you maybe have some free beer you get an entry but you win on your own merits you know I'm saying at the end of the day the content is the own merits and I think that's the open source paradigm that is hitting the content business which is community marketing if your pain-in-the-ass think you're going to get bounced out right out of the community or if you're selling something you're on so you guys do a great job really am i awesome you thank you James I really love what you add to the iod experience here with this corner and all the interviews is great great material well thanks for having us here really appreciate it I learned a lot it's been great you guys are great to work with very professional the products got great great-looking luqman portfolio hidden all hitting all the buttons there so hitting all the Gulf box so this is the cube we'll be right back with our last interview coming up shortly with Jeff Jonas he's got some surprises for us so we'll we'll see what he brings brings to his a game apparently he told me last night is bring his a-game to the cube so I'm a huge Jeff Jonas fan he's a rock star we love them on the cube iza teka athlete like yourself we write back with our next guest after this short break