 It's theCUBE, covering HPE Big Data Conference 2016. Now, here are your hosts, Dave Vellante and Paul Gillan. We're back, Amores Tripathi is here. He's the partner and the practice lead of the Big Data Analytics business at PWC, one of the, of course, world class consultancies. Amores, welcome to theCUBE, thanks for coming on. Thank you so much for having me here. You're welcome. So we're here at Analytics Conference. Analytics is your wheelhouse. What are you seeing so far? I know it's sort of day one, but what's the vibe at the show? It's very interesting. It's a great mix of use cases and technology coming together and great to see HP bringing up a suite of new ideas and products and like the entire stack, building up the stack in a lot of ways and a lot of use cases that I constantly learn from. So fantastic. So tell us the PWC analytics story. Massive organization, obviously. Your role and sort of how it, PWC got started in that analytics business, you've been in it, maybe it wasn't called analytics or maybe it was. Yeah, interesting, but if you think about it, we've been in the analytics business for 150 years, right? I mean, all we, whatever we do is to drive insight with data and build trust around data so that others can act on it. If you fundamentally look at PWC from that angle. Analytics as in the big data space as we are thinking about, that's probably a journey that's close to 20 years old in some ways with big data with different definitions and we do a lot of, we traditionally used to do a lot of work in forensics and financial analysis. So we've been in this business for a long time. We have organized the business analytics aspect of it. How do you use data to make better business decisions? That practice is probably around 13 years, 13, 14 years old now. And the way we are organized is we are, essentially it's a center of excellence model. We call it the analytics hub where we incubate a lot of the analytics offerings and our services. And our vision is it's not like analytics is a standalone business or standalone thing. It is integrated into each and every of our service line. So we have this hub and spoke model where we incubate the services and we work with our various industry service offerings and integrate analytics into it. And it's been very successful for us. I think it's a differentiator to think about not as analytics as a standalone thing, but something that's part of everything we do. What are the challenges that you have, some of these companies you work with are very old line companies and operate by seat of the France management principles. How do you get them to think analytics first? It's a great point because I mean, if you ask what we do, there are essentially two things we do. We actually help in building the analytics operating model and helping companies take benefit of analytics from an organization perspective and the second is actually through tools and solutions perspective. On the first one, if you think about how would you institute a culture of analytics in an organization, that's the most fundamental issue companies run into. And there's no good answer for it. The right answer is, I've never seen a company embrace analytics without a top-down leadership support. So someone has to be a believer in analytics in the organization pretty high up to go and institute analytics. And then that sets a tone for the rest of the organization to follow. Everything else, the investment, the people, the talent, the culture, the processes and governance. I mean, we have an entire process around how we start thinking about analytics, how to build an analytics-driven organization. But that fundamentally aligns, has to align with someone's vision around an organization that they need to do that. So how do we adapt and enforce the change? I think part of it is they have to be a little bit of there is someone has to believe in it and be ready for it. And then you support it with the right set of strategy to make the change happen. And thinking about initiatives that you're involved in, you had to break us, simplify it from my mind. If you had to break them into running the business, growing the business, and transforming the business, specifically as it relates to the analytics practice, how much of it is, run the business better versus grow the business versus really transform the business? It's a mix of all three. And even mix, or is it weighted? I think it's a mix of all three depending on the industry sectors we go in. So as part of the strategy we focus on, so healthcare, healthcare, for example, I spend a ton of time on, it is an industry in transformation. So it's a great opportunity to embed analytics in the transformation process, which we are doing. We are helping hospitals, financially struggling hospitals, sometimes very well run hospitals, fundamentally change how they think about customers, how they think about their physicians and the relationships between physicians and patients and the payers, cost and quality aspects of it and embed analytics right into it because they're anyway going through a transformation. Embedding analytics is the way to go. Compared to other industry, which probably, let's say, it's not kind of, it's more stable and there's not a lot of regulatory pressure as in healthcare, there I think the focus is around growth. How do you kind of- Retail would be an example. Retail would be a great example, where the demographics and everything, it's a 2% growth industry. How do you make it more efficient and at the same time look for micro growth opportunities? That's a great example. And I think the key is to balance where the industry is and what kind of analytic solutions you put in there. Can you give a couple of examples of projects you've worked on recently, where you've worked with customers, who thought you thought we're doing something really revolutionary? So many, many examples like that. Let me just choose a couple from two different industries. One is in healthcare, we have working with a very large specialty hospital, specialty cancer hospital, one of the finest in the world. They have decided that their future growth in this entire transformation is not going to be based on building brick and mortar hospitals and serving patients in a traditional way. They want to be an IP based company because their key asset is the data that they generate for when they see 100,000, 200, like 100,000 patients a month. And there is a data with the interaction between the physician and the patient. How do you capture the data? How do you store the data and draw insights from the data to create a decision support engine for other oncologists around the world, right? And export that software as a service offering. And it's an entire new business line for that. Very, I mean, we are partnering with HP on putting a lot of the solution together, but very challenging if you think in terms of EMR data, the clinical expertise that is required, the analytics that goes in that from an NLP perspective. Governance. Governance around the data and the security around the data. But fundamentally, a doctor has to change behavior because now they are not going to only rely on them. It's about augmenting the doctor and the physician in how they approach it. And that's not only here. It's applies to a hospital in Texas, but at the same time, it could apply to a hospital in Malaysia. That's fascinating. Well, you talked about the importance of top-down earlier in that example. It's sort of top-down and middle-in, I guess, is the doctors have so much influence in that environment, right? Yes, and the top-down is because most of these organizations are not physicians. Yeah, right. That's right. The second example, I think we all could relate to who are always on the road. We are working for a large airline. And I mean, we hear a lot about predictive maintenance in its various forms. Now it is a hot topic, but what was interesting if you think about predictive maintenance is, I mean, airlines have been in the sensor space and IoT space for a long, long time. They have all kinds of sensors, like showing all kinds of data, like they generate terabytes and teras of data second. It's mission critical. It's mission critical, exactly. And they've been doing it for years. And then at the same time, they also have been generating a lot of regulatory requirements, like pilot logs. Pilot have this essentially every before every flight and after every flight pilot just fills out a log. It's a handwritten document. And we have been doing this project with this airline where we have been able to blend both the sensor data and the pilot log data to predict delays and cancellations, avoidable delays and cancellations. I mean, that's a different view of predictive maintenance, but you essentially don't want to cancel a delay of flight because you have to pay for a hotel. You have to kind of put them on the flights. It kind of creates havoc in the network. How can you avoid that, but rather than going from a scheduled maintenance to a predictive maintenance kind of a model, but taking the sensor data, which is I would say high volume, low signal, a lot of noise, low signal, with an unstructured data from pilot log, which is actually a very high signal because it's an expert who's writing it, but it's just hard to analyze. But how do you blend it together and create machine learning models around it? I mean, and we've been able to do it and reduce the delays and cancellations by 30%. Wow. We're very tight on time. My last question is we, you know, we studied the so-called big data market and we noticed early on that it's very services heavy because it's so complicated and there are so few resources. Certainly at the time, there are more now, but still the expertise is lacking. Do you see that changing? You know, people have predicted, oh, no, software is going to sort of eat the world and what do you see still? This analytics business is a very services-led, continue to be complicated, moving fast, so you need the expertise of a partner like yours for your perpetually. It's going to be a blend of both. I think technology companies will have partnerships like HP and R is a great example where we are having partnerships. You need strong technology and strong services. I think it's a general evolution of technology. Things will get simpler to the end user over time, so which essentially means you have to have more and more solutions. Industry folks, vertical specific solutions that will come out and you see in the market, even the software that's coming out has become much more vertically focused right now, industry focused and we are investing in a lot of technology also. I mean, not only just the services, we are building a lot of accelerators. We call something App Hub, which we are collaborating with HP on it also, around building industry specific solutions that make it easier. So I think there will be a convergence of solutions that will accelerate and make it the analytics simpler and at the same time, you see the talent pool getting upgraded with all the programs, masters of analytics programs or even data science or even in business. So I think the talent is getting upgraded, solutions are becoming simpler. I think that's how the true cycle will start in terms of productivity with cycle. And the data keeps growing to add complexity. Amarash, thanks very much for coming on theCUBE, I'll leave it there. Thank you so much for having me, it was a pleasure. All right, keep it right there, everybody. We'll be back with our next guest. We're live from Boston. This is the HP Big Data Conference. Hashtag, seize the data, right back.