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Using Big Data to Tackle Supply-Chain Demands

How Capgemini's UK financial services unit helps clients manage risk using big data analysis

When Capgemini's business information management (BIM) practices unit needed to provide big data capabilities to its insurance company customers, it needed to deliver the right information to businesses much faster from the very bottom up.

That means an improved technical design and an architectural way of delivering information through business intelligence (BI) and analytics. The ability to bring together structured and unstructured data -- and be able to slice and dice that data in a rapid fashion; not only deploy it, but also execute rapidly for organizations out there -- was critical for CapGemini.

And that's because Capgemini's Financial Services Global Business Unit, based in the United Kingdom, must drive better value to its principal-level and senior-level consultants as they work with group-level CEOs in the financial services, insurance, and capital markets arenas. Their main focus is to drive a strategy and roadmap, consulting work, enterprise information architecture, and enterprise information strategy with a lot of those COO- and CFO-level customers.

Our next innovation case study interview therefore highlights how Capgemini is using big data and analysis to help its organization clients better manage risk.

BriefingsDirect had an opportunity to learn first-hand how big data and analysis help its Global 500 clients identify the most pressing analysis from huge data volumes we interviewed Ernie Martinez, Business Information Management Head at the Capgemini Financial Services Global Business Unit in London. The discussion, at the HP Discover conference in Barcelona, is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Risk has always been with us. But is there anything new, pressing, or different about the types of risks that your clients are trying to reduce and understand?

Martinez

Martinez: I don't think it's as much about what's new within the risk world, as much as it's about the time it takes to provision the data so companies can make the right decisions faster, therefore limiting the amount of risk they may take on in issuing policies or taking on policies with new clients.

Gardner: In addition to the risk issue, of course, there is competition. The speed of business is picking up, and we’re still seeing difficult economic climates in many markets. How do you step into this environment and find a technology that can improve things? What have you found?

Martinez: There is the technology aspect of delivering the right information to business faster. There is also the business-driven way of delivering that information faster to business.

Bottom up

The BIM practice is a global practice. We’re ranked in the top upper right-hand quadrant in Gartner as one of the best BIM practices out there with about 7,000 BIM resources worldwide.

Our focus is on driving better value to the customer. So we have principal-level and senior-level consultants that work with group-level CEOs in the financial services, insurance, and capital markets arenas. Their main focus is to drive a strategy and roadmap, consulting work, enterprise information architecture, and enterprise information strategy with a lot of those, the COO- and CFO-level customers.

We then drive more business into the technical design and architectural way of delivering information in business intelligence (BI) and analytics. Once we define what the road to good looks like for an organization, when you talk about integrating information across the enterprise, it's about what is that path to good looks like and what are the key initiatives that an organization must do to be able to get there.

This is where our technical design, business analysis, and data analysis consultants fit in. They’re actually going in to work with business to define what do they need to see out of their information to help them make better decisions.

Gardner: Of course, the very basis of this is to identify the information, find the information, and put the information in a format that can be analyzed. Then, do the analysis, speed this all up, and manage it at scale and at the lowest possible cost. It’s a piece of cake, right? Tell us about the process you go through and how you decide what solutions to use and where the best bang for the buck comes from?

Martinez: Our approach is to take that senior-level expertise in big data and analytics, bring that into our practice, put that together with our business needs across financial services, insurance, and capital markets, and begin to define valid use cases that solve real business problems out there.

We’re a consulting organization, and I expect our teams to be able to be subject matter experts on what's happening in the space and also have a good handle on what the business problems are that our customers are facing. If that’s true, then we should be able to outline some valid use cases that are going to solve some specific problems for business customers out there.

In doing so, we’ll define that use case. We’ll do the research to validate that indeed it is a business problem that's real. Then we’ll build the business case that outlines that if we do build this piece of intellectual property (IP), we believe we can go out and proactively affect the marketplace and help customers out there. This is exactly what we did with HP and the HAVEn platform.

Why Capgemini and our BIM practices jumped in with a partnership with HP and Vertica in the HAVEn platform is really about the ability to deliver the right information to business faster from the bottom up. That means the infrastructure and the middleware by which we serve that data to business. From the top down, we work with business in a more iterative fashion in delivering value quickly out of the data that they are trying to harvest.

Wide applicability

Gardner: So we’re talking about a situation where you want to have wide applicability of the technology across many aspects of what you are doing, that make sense economically, but of course it also has to be the right tool for the job, that's to go deep and wide. You’re in a proof-of-concept (POC) stage. How did you come to that? What were some of the chief requirements you had for doing this at that right balance of deep and wide?

Martinez: We, as an organization, believe that our goal as BI and analytics professionals is to deliver the right information faster to business. In doing so, you look at the technologies that are out there that are positioned to do that. You look at the business partners that have that mentality to actually execute in that manner. And then you look at the organization, like ours, whose sole purpose is to mobilize quickly and deliver value to customer.

I think it was a natural fit. When you look at HP Vertica in the HAVEn platform, the ability to integrate social media data through Autonomy and then of course through Vertica and Hadoop -- the integration of the entire architecture -- gives us the ability to do many things.

But number one, it's the ability to bring in structured and unstructured data, and be able to slice and dice that data in a rapid fashion; not only deploy it, but also execute rapidly for organizations out there.

Being here at HP Discover this week has certainly solidified in my mind that we’re betting on the right horse.

Over the course of the last six months of 2013, that conversation began to blossom into a relationship. We all work together as a team and we think we can mobilize not just the application or the solution that we’re thinking about, but the entire infrastructure derivatives to our customers quickly. That's where we’re at.

What that means is that once we partnered and got the go ahead with HP Vertica to move forward with the POC, we mobilized a solution in less than 45 days, which I think shows the value of the relationship from the HP side as well as from Capgemini.

Gardner: Down the road, after some period of implementation, there are general concerns about scale when you’re dealing with big data. Because you’re near the beginning of this, how do you feel about the ability for the platform to work to whatever degree you may need?

Martinez: Absolutely no concern at all. Being here at HP Discover has certainly solidified in my mind that we’re betting on the right horse with their ability to scale. If you heard some of the announcements coming out, they’re talking about the ability to take on big data. They’re using Vertica and the HAVEn network.

There’s absolutely zero question in my mind that organizations out there can leverage this platform and grow with it over time. Also, it gives us the ability to be able to do some things that we couldn’t do a few years back.

Business value

Gardner: Ernie, let's get back to the business value here. Perhaps you can identify some of the types of companies that you think would be in the best position to use this. How will this hit the road? What are the sweet spots in the market, the applications you think would be the most urgently that make a right fit for this?

Martinez: When you talk about the largest insurers around the world, whether from Zurich to Farmers in the US to Liberty Mutual, you name it, these are some of our friendly customers that we are talking to that are providing feedback to us on this solution.

We’ll incorporate that feedback. We’ll then take that to some targeted customers in North America, UK, and across Europe, that are primed and in need of a solution that will give them the ability to not only assess risk more effectively, but reduce the time to be able to make these type of decisions.

Reducing the time to provision data reduces costs by integrating data across multiple sources, whether it be customer sentiment from the Internet, from Twitter and other areas, to what they are doing around their current policies. It allows them to identify customers that they might want to go after. It will increase their market share and reduce their costs. It gives them the ability to do many more things than they were able to do in the past.

It allows them to identify customers that they might want to go after. It will increase their market share and reduce their costs.

Gardner: And Capgemini is in the position of mastering this platform and being able to extend the value of that platform across multiple clients and business units. Therefore, that reduces the total cost of that technology, but at the same time, you’re going to have access to data across industries, and perhaps across boundaries that individual organizations might not be able to attain.

So there's a value-add here in terms of your penetration into the industry and then being able to come up with the inferences. Tell me a little bit about how the access-to-data benefit works for you?

Martinez: If you take a look at the POC or the use case that he POC was built on, it was built on a commercial insurance risk assessment. If you take a look at the underlying architecture around commercial insurance risk, our goal was to be able to build an architecture that will serve the uses case that HP bought into, but at the same time, flatten out that data model and that architecture to also bring in better customer analytics for commercial insurance risk.

So we’ve flattened out that model and we’ve built the architecture so we could go after additional business, instead of more clients, across not just commercial insurance, but also general insurance. Then, you start building in the customer analytics capability within that underlying architecture and it gives us the ability to go from the insurance market over to the financial services market, as well as into the capital markets area.

Gardner: All the data in one place makes a big difference.

Martinez: It makes a huge difference, absolutely.

Future plans

Gardner: Tell us a bit about the future. We’ve talked about a couple of aspects of the HAVEn suite. Autonomy, Vertica, and Hadoop seem to be on everyone's horizon at some point or another due to scale and efficiencies. Have you already been using Hadoop, or how do expect to get there?

Martinez: We haven’t used Hadoop, but certainly, with its capability, we plan to. I’ve done a number of different strategies and roadmaps in engaging with larger organizations, from American Express to the largest retailer in the world. In every case, they have a lot of issues around how they’re processing the massive amounts of data that are coming into their organization.

When you look at the extract, transform, load (ETL) processes by which they are taking data from systems of record, trying to massage that data and move it into their large databases, they are having issues around load and meeting load windows.

The HAVEn platform, in itself, gives us the ability to leverage Hadoop, maybe take out some of that processing pre-ETL, and then, before we go into the Vertica environment, be able to take out some of that load and make the Vertica even more efficient than it is today, which is one of the biggest selling points of Vertica. It certainly is in our plans.

This is a culture that organizations absolutely have to adopt if they are going to be able to manage the amount of data at the speed at which that data is coming to their organizations.

Gardner: Another announcement here at Discover has been around converged infrastructure, where they’re trying to make the hardware-software efficiency and integration factor come to bear on some of these big-data issues. Have you thought about the deployment platform as well as the software platform?

Martinez: You bet. At the beginning of this interview, we talked about the ability to deliver the right information faster to business. This is a culture that organizations absolutely have to adopt if they are going to be able to manage the amount of data at the speed at which that data is coming to their organizations. To be able to have a partner like HP who is talking about the convergence of software and infrastructure all at the same time to help companies manage this better, is one of the biggest reasons why we're here.

We, as a consulting organization, can provide the consulting services and solutions that are going to help deliver the right information, but without that infrastructure, without that ability to be able to integrate faster and then be able to analyze what's happening out there, it’s a moot point. This is where this partnership is blossoming for us.

Gardner: Before we sign off, Ernie, now that you have gone through this understanding and have developed some insights into the available technologies and made some choices, is there any food for thought for others who might just be beginning to examine how to enter big data, how to create a common platform across multiple types of business activities? What did you not think of before that you wish you had known?

Lessons learned

Martinez: If I look back at lessons learned over the last 60 to 90 days for us within this process, it’s one thing to say that you're mobilizing the team right from the bottom up, meaning from the infrastructure and the partnership with HP, and as well as the top-down with your business needs to finding the right business requirements and then actually building to that solution.

In most cases, we’re dealing with individuals. While we might talk about an entrepreneurial way of delivering solutions into the marketplace, we need to challenge ourselves, and all of the resources that we bring into the organization, to actually have that mentality.

What I’ve learned is that while we have some very good tactical individuals, having that entrepreneurial way of thinking and actually delivering that information is a different mindset altogether. It's about mentoring our resources that we currently have, bringing in that talent that has more of an entrepreneurial way of delivering, and trying to build solutions to go to market into our organization.

Learn more about the HP+Capgemini CIRA Solution
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I didn’t really think about the impact of our current resources and how it would affect them. We were a little slow as we started the POC. Granted, we did this in 45 days, so that’s the perfectionist coming out in me, but I’d say it did highlight a couple of areas within our own team that we can improve on.

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At Interarbor Solutions, we create the analysis and in-depth podcasts on enterprise software and cloud trends that help fuel the social media revolution. As a veteran IT analyst, Dana Gardner moderates discussions and interviews get to the meat of the hottest technology topics. We define and forecast the business productivity effects of enterprise infrastructure, SOA and cloud advances. Our social media vehicles become conversational platforms, powerfully distributed via the BriefingsDirect Network of online media partners like ZDNet and IT-Director.com. As founder and principal analyst at Interarbor Solutions, Dana Gardner created BriefingsDirect to give online readers and listeners in-depth and direct access to the brightest thought leaders on IT. Our twice-monthly BriefingsDirect Analyst Insights Edition podcasts examine the latest IT news with a panel of analysts and guests. Our sponsored discussions provide a unique, deep-dive focus on specific industry problems and the latest solutions. This podcast equivalent of an analyst briefing session -- made available as a podcast/transcript/blog to any interested viewer and search engine seeker -- breaks the mold on closed knowledge. These informational podcasts jump-start conversational evangelism, drive traffic to lead generation campaigns, and produce strong SEO returns. Interarbor Solutions provides fresh and creative thinking on IT, SOA, cloud and social media strategies based on the power of thoughtful content, made freely and easily available to proactive seekers of insights and information. As a result, marketers and branding professionals can communicate inexpensively with self-qualifiying readers/listeners in discreet market segments. BriefingsDirect podcasts hosted by Dana Gardner: Full turnkey planning, moderatiing, producing, hosting, and distribution via blogs and IT media partners of essential IT knowledge and understanding.

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