 Okay, we're back live here inside the cube and rounding out day one of exclusive coverage of IBM information on demand I'm John Furrier the founder Silicon angle and joined my co-host Dave Vellante. We're here in heat You saw who's the vice president. I said SPP because I think you always get promoted You've been on the cube so many times you're doing so well. It's all your presentation was so amazing I always like SPP. Good things happen. That's exactly why IBM VP is a big deal Unlike some of the startups where everyone gets EVP all these other titles, but welcome back. Thank you The storytelling has been phenomenal here. Although Mer's a little bit critical of some of the presentations early Mer from Gardner But the story is hard IBM just from last year take us through what's changed from IOD last year to this year The story has gotten tighter Yes, yes comprehensive Give us the quick. Okay quick view Okay, here's the point of view. Here's the point of view first You got to invest in a platform which we've all talked about and I will tell you it's not just us saying it I would say other vendors are now copying what we're saying because if you went to strata Yes, which you were there. We were there probably heard some of the messages right platform. Everybody wants to be a platform player. Okay one two Elevated risk Uncertainty governance. I think privacy privacy security risk. This is what people are talking about. They want to invest into more Why because you know what the decisions matter. They want to make bigger bets They want to do more things around customer experience. They want to improve products. They want to improve pricing The third area is really a cultural statement like applying analytics in the organization Because the people and the skills I would say the culture Conversation is happening a lot more this year than it was a year ago not just at IOD but in the industry So I think what you're seeing here at IOD is actually a reflection of what the conversations are happening So our organizations culturally ready for this I mean you guys are gonna say yes, and everybody comes on says oh, yes We're seeing it all over the place, but are they really ready? Depends. I think some are some are absolutely ready some are not and probably the best examples are And it really depends on the industry. So I'll give you a few examples. So in the government area I think people see the power of applying things like real-time contextual insight leveraging stream computing why because National security matters a lot of fraudulent activity because that's measurable. You can drive revenue or savings Healthcare people know that a lot of decision-making is being made without a comprehensive view of the analytics and the data Now the other area that's interesting is most people like to talk about text analytics Unstructured data a lot of social media data But the bulk of the data that's actually being used currently in terms of big data analytics is really transactional data Why because that's what's Maintained at most operational systems warehouse systems So you're going to see a lot more data warehouse augmentation use cases leveraging Hadoop on the front end or the back end You're going to see kind of more in terms of comprehensive view of the customer, right? augmenting like an existing customer loyalty or segmentation data with Additional let's say activity data that they're interacting with and that was the USTA kind of demo showing social data cell phone metadata Is that considered transactional? You know it it well call data record right CDR call detail records for the real time is important to you mentioned the US Open just For the folks out there was a demo on stage with the US Open data Yeah, and all the trend sentiment data or social data, but that's people's thoughts, right? So you can see what people are doing now. That's big you know what's amazing about that just one second which is What we were doing was we were predicting it based on the past But then we were modifying it based on real-time activity and conversation So let's say something hot happened and all of a sudden it was interesting when Brian told me this He was like, oh, yeah, Serena's average Twitter score was like 20 200 tweets a day and then if some activity were to happen, let's say, I don't know she wrote she had got into a romance or let's say she decided to launch a new product then all of a sudden You'd see a huge spike right in activity social activity that would then predict how they wanted to operate that environment That's amazing. And you know, we you know, we we love data You've seen our our crowd spots viewfinder We have the new crowd chat tool and and this idea of connecting consumers is loose data. It's ephemeral data It's transient data, but it's now captureable so people can have a have fun at the tennis tournament And then it's over they go back home to work You still have that metadata. We do that's very kind of it's transient and ephemeral That's value. So, you know, Merv was saying also that your group's doing a lot of value creation Let's talk about that for a second business outcomes. What do you what? It's the top conversation when you walk into a Customer that says, hey, you know, here's point a point B B's my outcome. What are those conversations like? I mean, what are they? What are some of the outcomes you just talk use case you need to talk customers But like what are some of the examples? Yeah, you know, what's I'll tell you one use case So and this was actually in the health care I'll tell you one health care use case and one financial services use case both conversations happened actually in the last two weeks So in the health care use case There's already let's say a model that's happening for this particular hospital now They have a workflow process typically in a workflow process you you're applying Capabilities where you've modeled out your steps, right? You do a before B before C and you automate this leveraging BPM type capabilities In a data context, you don't actually start necessarily with knowing what the workflow is you kind of let the data Determine what the workflow should be so in the this was in an ICU Arena historically if you wanted to decide who was the Healthiest of the patients in the ICU because you had another trauma coming in There was a workflow that said you had to go check the nurses the patients profile and say who gets kicked out Of what bed or moved because they're the most likely to be in a healthy state That's a predefined workflow But if you're applying streams for example all of a sudden you could have real-time visibility without necessarily a nurse Calling a doctor who that calls the local staff who then calls the cleaning crew, right? You could actually have a dashboard that says with 80% confidence beds two and eight those patients because of the following conditions could be The ones that you are proactive and in saying oh, you know what not only can they be released But we have this degree of confidence around them being because of the day that it's coming out the Changes then potentially, you know the way you're kind of setting your rules and policies around your workflow another example Which was really a government use case was Think about in government security so in security scenarios and national security scenarios You never quite know exactly what people are intending to do other than you know They're intending something bad, right and they're intentionally trying not to be found so human trafficking It's an ugly topic, but I want to bring it up for a second here What you're doing is you're actually looking at data compositions and and different patterns and resolving entities and based on that That'll dictate kind of potentially a whole new flow or a treatment or remediation or activity or savior Which is not the predefined workflow. It's you're letting the data Actually, all of a sudden connect to other data points that then you're arriving at the insight to take the action So we're completely different. I want to go back to clarify question on the health care examples. Yeah, so so Where are we today? Is that something that's actually being implemented? Is that something that's sort of a proof of concept? Well, that's actually being done at It's being done in a couple different hospitals one of which is actually in Hospital in Canada and then we're also leveraging streams in the Emory University Intensive care unit Timothy Buckman on you did earlier. Oh, yeah, I see you with the future, right? Absolutely brilliant example brings up, you know, obviously, that's the underbelly of the world and society But like data can usually Jeff Jonas has been on the queue as you know many times and he talks about his puzzle pieces In a way that the data is traveling on a network a network that's just attributed Essentially, that's network computing. I mean, it's a management. So if you look at network management, you can look at patterns, right? So so that's an interesting example. So that begs the next question What is the craziest most interesting use case you've seen? Oh my gosh, okay now I gotta think about oh Yes, all right that I can talk about that creates business value or society value. Oh, you know, I okay Boy, you are putting me on the spot the craziest one So three it could be great. It could be g-rated You know what I participated three weeks ago TIA Cref actually hosted a fraud summit where it was all Investigators like they were doing crime investigation. So more than 60% of the guys in the room Carried weapons because they were security intelligence. They were police. They were DA's they I was not Oh gosh, oh Anyway, and there was about so 60 plus percent were those right and then only about 30 percent in the room were What I would consider the data scientists in the room like these are the guys are trying to decide which claims are not true or false so forth There were at least like three or four use cases in that discussion that came out. They were unbelievable So one is in the fraud area in particular and in crime. They're layering the data there What does layering the data? They're taking location-based data For a geographic region. They're putting crime data on top of that right historical like drug rinks and Even like data sets in Miami-Dade County. The DA told me they were doing things where Rather than looking at people that are Dealing the drugs they they realized people that had possession of a drug Typically purchased within a certain location and they had these abandoned properties and were able to identify Entire rings based on that Another one. This is also semi-drug related is in the energy utility space. There was in the middle part of the United States Houses in nice urban areas where they were completely torn apart on the interior and built into marijuana houses and so of course they're utilizing high levels of gas and Electricity in order to maintain the water fertilization and everything else Well, what happens is it drives peaks in the way that the energy utility? Looks on a given-day pattern. So based on that they're able to detect how Inappropriate activities are happening and whether it's a single opportunistic type activity whether it's a crime activity or Well, you know what's interesting about electricity too is especially if someone's using electricity, but no one's like using any of the gas You're like, oh, but no one's cooking, you know, something's a little off, but it was fascinating I mean really fascinating. There were like several other crime Scenarios in terms of speed. I actually did not know the US Postal Service is like the longest running federal Institution that actually Tracked like mail fraud and one of the use cases. I'm sure Jeff has talked about here on The Cube is probably a money-gram use case, but we talked about that we talked I mean the stories were unreal because I was spending time with forensic scientists as well as forensic Investigators and that's a completely. So we're getting we're getting a few minutes. That's the need for a platform to handle all this diversity That's the security risk the governance everything you got to go because you're a star for the analyst me But yeah, I can't believe we're having this conversation One of the best yet We got drugs in there. We got other things packing guns guns and drugs, you know Knowledge worker all right final question for I know you got to go This big data applications where you know the guys in the mail room The guys who work for the post office are now Enabled to actually do this kind of high-level kind of date basically data science Yeah, if you will or being an analyst So that what I want you to share the folks your vision of the definition of the knowledge worker Overused word that's been kicked around from the PC generation But now with handheld with analytics real time with streaming all this stuff happening at the edge How is it not going to change that the knowledge worker the person in the trenches It could be first in the cubicle the person on the go the mobile salesperson or anyone, you know some people feel threatened when they hear that you're gonna Apply data and analytics everywhere because you're it implies that you're automating things But that's actually not the value the real value is the insight so that you can double down on the decisions you want to make so if you're more confident you're going to take bigger bets right and Decision-making historically has been I think Reserve for a very elite few and what we're talking about now is a democratization of that Insight and with that comes a lot of empowerment a lot empowerment for everyone and you don't have to be a data scientist be able To be able to make decisions and informed decisions if anything, you know Actually Tim Buckman, I had a good conversation about them as a professional, you know what I if I was a physician I'd want to work at the hospital that has the advanced capabilities Why because it allows me as a professional physician to then be able to do what I was trained to do not to Detect and have to pay attention to all these alarms going off, you know I want to work at the institutions and organizations that are investing appropriately because it pushes the caliber of the work I get to do so I think it just changes the dynamics for everyone Yeah, it was like a high-priced logistics manager People want to work with leaders and now we're in a modern era and this new wave is upon us People care and they want to improve and this is about a continuing to improve Dave and I always talk about the open-source world that those that those principles are going mainstream to every aspect of business collaboration openness transparency not controlled Absolutely in he thanks so much for coming on the queue. I know you're busy. Thank you for your time We are here live in the Cube getting all the signal from the noise and some good commentary at the end day one We have one more guest Ray Wang right up next Stay tuned right back