PWC Develops Analytics Platform to Improve Retail, Media, eCommerce Insights on Road to Deep Predictive Modeling


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Transcript of a BriefingsDirect podcast on how HP tools are changing the face of data analytics in the era of big data.

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PWC Develops Analytics Platform to Improve Retail, Media, eCommerce Insights on Road to Deep Predictive Modeling

  1. 1. PWC Develops Analytics Platform to Improve Retail, Media, eCommerce Insights on Road to Deep Predictive Modeling Transcript of a BriefingsDirect podcast on how HP tools are changing the face of data analytics in the era of big data. Listen to the podcast. Find it on iTunes. Sponsor: HP Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on how data is analyzed and used to advance the way we all work and live. Once again, we’re showcasing how thought leaders and innovative companies worldwide are capturing myriad knowledge, gaining ever deeper analysis, and rapidly and securely making those insights available to more people on their own terms. Our next innovation case study focuses on the growing needs among business verticals for a powerful, fast, and targeted data-analysis capability, and we will explore how one technology solutions provider, PricewaterhouseCoopers LLC (PwC), is crafting just such a set of services. We’ll now learn how PwC has connected the dots between data access, transformation, integration, and analysis to provide rapid and recurring insights for such industries as retail, media, healthcare, and e-commerce. The goal is to develop deep predictive modeling capabilities for such business activities as dynamic real-time pricing and responsive consumer services. To learn more, about how high-performing and cost-effective big-data processing is coming to market as a vertical industry solution, please join me now in welcoming our guest. We’re here with Dhiraj Pathak, he is the Director of the CloudLab at PwC in New York. Welcome, Dhiraj. Dhiraj Pathak: Hi, Dana. Good to be with you. Gardner: Glad to have you with us. Let's start at a high level. How is big-data analysis changing to such a degree that we can now make it into something that’s appropriate for vertical industry solutions? How is it that things have changed so that we can now take what was basically a technology and make it into a business service? Gardner
  2. 2. Pathak: Well, Dana, a couple of things are happening. As clients are becoming more-and-more digital in nature and transacting with their users using digital channels, a lot of the business performance around these interactions is tied to an ability to process the data that’s underlying these interactions in meaningful ways, and in ways that can be turned into business insight, can be used for decision-making. We’re witnessing a shift from looking at big data as a technological or a technical capability to a business need, which then has to be addressed and resolved. Niche approach Gardner: What's interesting to me is that we’re not just taking a one-size-fits-all approach to this data capability. We’re looking at the business requirements vertical-by-vertical, perhaps even company-by-company and starting to adjust how we extract analysis in such a way that it's of greater value to that vertical. How are we able to do that granular or niche approach to analysis? Pathak: That’s a very critical observation, Dana. The core of that is that meaning from data cannot be derived unless you put the right context to it. To be successful with big-data analysis and turning that data into business insight, what's critical is to combine it with enough context of the business area that’s focused on honing in on aspects of the data and asking the right questions that allow for that value to come out of that data. Gardner: I suppose another big change nowadays is around velocity or speed. We've been able to take big chunks of data, do batch processing, and extract analysis and reports. But we’re now at the point where we can take activity in real-time, like a customer in a retail environment or someone on a website doing e- commerce, and deal with vast amounts of data and then react to them. How is this speed element impacting the market? Pathak: The speed element is essentially based having on this mobile device with you all the time, as all of us are now adopting. That’s leading to a need for an analysis capability. But offline analysis is not going away, because that's where the deep insights come from. That’s where the predictive analytics happen. What's also becoming extremely crucial is to serve up the results of those analyses and to apply those analyses in real-time. As you said, imagine a shopper on a e-commerce site around Christmas, making a last moment purchasing decision. They’re looking for insight. They don’t necessarily know what they might be what they might become interested in. To the extent that that e-com site is able, based on a knowledge of who that customer is and based on the behavior that the customer begins to show on the site, if an insight or a recommendation can be made in context, in real-time, that’s where the value is. That then leads Pathak
  3. 3. to a different set of requirements around how to process big data in order to be able to produce those insights in the moment. Gardner: How significant is this likely to be, this velocity increase, the amount of data being brought to bear on these businesses? Are we talking about incremental improvement, or is this something that could be rather dramatic, something catalytic even? Pathak: For a period of time it will have a fairly dramatic kind of an impact, before it's all settled down and then that becomes the new normal and that’s how businesses interact with their users. But for the next two to three years, I think that we’ll see dramatic shifts and dramatic examples of how businesses are able to inject a lot of momentum into their interactions with their customers. They’re able to process the underlying data factor and are able to turn that data into insights that get presented in the course of a transaction or in the course of that interaction that the customer is having with that business. To the marketplace Gardner: Well, you've certainly piqued my interest. Let's learn a bit more about PwC and how it's working in the market to exploit these technologies to make them into discrete business services. Tell us how we got to where we are in terms of you’re taking this out to the marketplace? Pathak: As I said before, one of the most dominant trends that we’re observing with our client base is that all of our clients, or at least most of our clients, are turning into digital businesses. If you look at our retail clients, more-and-more of the transactions that these businesses are having with their users are happening on digital channels, on e-commerce sites, but it's not restricted to just the retailers. If we look at hospitals, banks, or media companies, anywhere you look, businesses are creating capabilities and services that are digital, where people are interacting with these services using their smart devices, their phones, tablets, etc. In fact, in many cases, there is a whole new emerging area around variable technology, where people are monitoring themselves for different reasons. If that’s the market opportunity that we've become squarely focused on, the requirement around this is to be able to process the data that gets generated when customers and end users are interacting in a digital way. Increasingly, it's about how to turn that data into a couple of different things, and turn that into a better shopper experience. Whoever is interacting with business is getting a sense that they are being served as a category of one. To the extent that there is that personalization and a sense for a
  4. 4. white-glove treatment, that creates an enormous stickiness factor and an enormous competitive differentiation for that business. The other aspect of it is that all of this data that’s getting aggregated at an enormous rate and in very large quantities is allowing for analytics of a very different kind to get applied to this data. Those are the two requirements that many of our clients are having. What we've been doing is to create platform capability to help accelerate the business’s ability to look at its data and convert it into these kinds of responses that they’re looking to create. Gardner: Is PwC showing them the way to build their own data capabilities or are you focusing on a vertical industry, where you can bring your own data, analysis, and aggregation to bear? Are you teaching them to fish or are you going to be doing some of this analysis for them as a service, perhaps as a cloud service? Pathak: The way we’re going into market around this platform capability is to do what makes the best sense for our clients. In some cases, we’re working with clients who are viewing us as a service bureau, where we take on their data and turn it around, combine that data that comes from a particular enterprise with more data that’s available outside, and provide a result back as a service. But in other cases, there are a number of clients we’re working with for whom their interaction with PwC is more to bring us in to help create that capability more organically within their own enterprise. So we see the full spectrum of delivery methods around this kind of capability, but the common factor is that the need is emerging very rapidly in all of the different sectors we’re working with and with clients both big and small. Hurdles and roadblocks Gardner: Dhiraj, you mentioned earlier that there has to be a new level of technology capability in order to accommodate the speed, the scale, and the responsiveness. Tell us about the problem. What has been the hurdle or the roadblock to being able to do this data analysis at scale and at velocity up until now? Pathak: There are several problems. It is important to recognize that there has been a pivoting or a transition in the last two to three years, where big data has suddenly really become a reality. About 12 to 18 months ago, people said they were working with big data, but clearly they were not truly working in big data. They just had large data sets, and traditional analytics technologies, data management technologies, and infrastructure technologies were at least technically sufficient for that situation. However, there was still quite a bit of heavy lifting that was required to turn that data into insight.
  5. 5. Now, truly big data, where we’re seeing businesses really getting into the petabyte range, the problem is several fold. It's not that there is a first problem and then there is a second problem, but there is a whole bevy of problems here. Some of the things that make up this complexity are first getting a grip on how can you harness the data -- the what question, before getting into the how question. Many of our clients are now at the point where they can clearly see that they are creating this enormous data wealth, but they’re not able to see what to do with it. So there is that issue, and in many cases, we go in and we work very collaboratively with our clients and help work through what the opportunity might be around the data that they’ve got available to them. After that issue has been addressed, the next thing is a ground reality, which is that the available data, in most cases, is not ready to be mined. There is a big gap between the data as it exists and needing to be converted or transformed into a shape and form that analysis can be done. In many instances, if you look at the overall effort required from identifying what you want to do with a big data set and then ending up with some value from that big data set, 60 to 80 percent of the effort would go into just doing various things to pull all that data together and put it into a form that you can do analytics on. Then, when you come to that point, there is another an issue looming in front of you. In most instances, it would require the assembly of many different kinds of analytics engines. If it's structured data, then you have to approach it in a certain way. If it's unstructured data, you have to approach it in another way. Depending on the type of data, it could require a traditional data warehouse kind of an approach. If it's more unstructured, it might require some of the newer distributed file systems kind of approaches. All of that technology that’s required to process the data and convert it into value becomes the third bottleneck. What makes sense Those are some of the issues that we’re helping to resolve for our clients by first figuring out and coming forward with, in an industry specific way, a framework of what kind of questions make sense in specific industry sectors. Then, it’s following it up with a platform capability that does all of the heavy lifting around how to stream data into an infrastructure and how to apply different kinds of ways of analyzing the data. All of that tooling has gotten put together in a platform, and then that helps to accelerate the time to value.
  6. 6. Gardner: Given this very complex interrelated set of requirements, you also need to create a platform through which you can re-use that platform for a variety of different verticals with a lot of different business issues and requirements. When you looked for the right platform to suit your needs, where did you go and what did you find? Pathak: We had some very specific requirements. Having done big-data analysis work with our clients across industry sectors for a while, we had started to converge on some core requirements around the right technology building blocks. One of these core requirements was the need for openness and a platform or an architecture that would allow for a plug-and-play, where capabilities could be dropped in and taken out as needed. The second major requirement that we had was a platform that could scale. Big data, as I said before, is in that petabyte-plus range and that’s only the beginning. You have to have an architecture that’s designed for scale and scale in terms of the data itself, but also in terms of processing. As we talked about before, a lot of the big-data value proposition is now tied to being able to do it in real time and to effect, in the moment, context decision-making. So it's both about large- scale data storage, large scale computation. Then, the third requirement that we had established was an architecture that had a built-in support for connecting to different systems and different sources of data. If that support isn’t there, that typically translates into a lot of ad-hoc work that has to be done in marshaling the data, putting it together, and having it flow in the right-way. Then, even after you've achieved that objective, you’re stuck with the fairly daunting task of maintaining all of that connectivity to data over time. Gardner: Getting back to the use of this in the market, are you in the proof-of-concept (POC) phase? Do you have a steady set of examples? Can you give us some sense of when you do this well, building it on the right platform bringing it into the business vertical in a context that’s suitable to their real world needs, what paybacks we’re seeing and what efficiencies or innovation are gained? Early launch phase Pathak: We’re in the early cycle of this, an early launch phase. We’re working with a number of clients who have big-data problems that they want resolved. The early payback is our ability to do a POC and move quickly to the point where we’re having a real meaningful interaction with a client around their data and starting to show them what value is hidden in that data. We’re finding that we’re able to now do that proving out of the value from data much faster.
  7. 7. In the last 60 days or so since we've launched this solution, we’re now in front of at least six or eight clients who are looking at this capability. They’re seeing the value of having a platform that they can connect into, where the discussion is around what your data is and what you want out of it. It completely circumvents the discussion around what tools you need to buy, what infrastructure you need to have, how would this all be integrated, and how long of a system’s integration kind of an exercise would have to be undertaken before you can begin to see what your data can actually tell you. Early in the analytics lifecycle, that acceleration is very crucial and sets the stage for whatever comes next. That’s where we’re seeing the early value. We’re also projecting that, and this is a little bit more futuristic, based on real client feedback, the next logical step on this is to provide this platform as a service (PaaS), where the data can simply be streamed into the platform and end results be provided to the end customer. Right now, the value that we are seeing in this is accelerating our client’s ability to establish a value proposition around their big data sets and being able to provide them those early insights, which are then shaping their strategy around what to do next. Gardner: Terrific. I'm afraid we’ll have to leave it there. It's been a very enlightening discussion. We've been learning about how technology solutions provider PwC is crafting a powerful, fast, and targeted data analysis capability for such industries as retail, media, healthcare, and e- commerce. And we've learned how this will soon lead to deep predictive-modeling capabilities and a reduction in the time to value, while in a sense masking a lot of the technological hurdles along the way. So I want to extend a huge thank you to our guest, Dhiraj Pathak, he is the Director of the CloudLab at PwC in New York. Thanks so much, Dhiraj. Pathak: Thank you, Dana. Good to have talked to you. Gardner: And also, a huge thank you to our audience, for joining this HP Discover Podcast. I'm Dana Gardner, Principal Analyst at Interarbor Solutions; your host for this ongoing series of HP sponsored business innovation discussion. Thanks again for listening and do come back next time.   Listen to the podcast. Find it on iTunes. Sponsor: HP Transcript of a BriefingsDirect podcast on how HP tools are changing the face of data analytics in the era of big data. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved. You may also be interested in:
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