Narendra Mulani, senior managing director of Accenture Analytics, part of Accenture Digital – an arm of the consulting firm that offers digital business and technology services – has a focus on data science and big data.

Accenture recently completed a survey to gain a broader, more enhanced perspective of big data successes. The firm surveyed executives from across seven industries in 19 countries to get a clear picture on whether companies are engaging with big data, what their experiences are like and what type of lessons they’re learning.

Mulani talked with Information Management about the survey results, the success factors that were revealed and where the big data market is heading.

Accenture’s research showed areas where big data is driving results. For instance, companies use big data moderately or extensively to identify new sources of revenue (94 percent), retain and acquire customers (90 percent), and develop new products and services (89 percent). Are there examples or use cases that especially impress you?

There are multiple. For example, a telecommunications provider in Japan is helping consumer companies target promising customers with mobile ads in real time. And they’re using anonymized customer attributes and geolocation data from Wi-Fi or access points [to be able to] serve up the right ads. This is a classic case of using big data technologies, mobility and data science to come up with better recommendations.

Another example is with the Internet of Things. We’re working with a water utility in Britain that is capturing [big] data pretty much in real-time from the sensors in their reservoirs, in their pipes in their dumping stations, etc. to give them the complete view – the live view – of their value chain and how the whole system’s acting in real time. It helps them better anticipate equipment failures and how to deal with wastewater. A number of things that utilities did manually, they’re now doing in real-time. So those are a couple of very clear examples of how big data technologies are essentially taking stuff that was in the “I cannot even think of how I’d do this” pile to implementation mode.

One area of Accenture’s study looks into challenges with big data, with the top results being security, budget and perceived lack of talent. Are these challenges game-stoppers for big data initiatives?

I don’t think these are game-stoppers, and I’ll tell you why. Security is getting embedded into big data in a bigger way. At the same time the [application programming interface] providers are also really focused on security. The big data ecosystem understands the fact that if they’re going to become a core piece of the enterprise architecture, they’re going to have to solve the security problem. And they’re all investing heavily in it. So I think if we do the same survey 12 months from now, we’d probably get a different view on security than we have today.

The second [biggest challenge] is budget. I thought about that one, and I’m also trying to correlate it with some of the other data you see on where CIOs are with respect to big data. Most CIOs are starting to understand how big data fits into an enterprise architecture, both from a utility perspective and from a governance perspective. And so my view is that when you look at a lot of the big data initiatives today, many of them have been started [out in] the business, with the CIO or IT helping out. Therefore, right now someone [is seeking approval for] a [business] initiative budget rather than big data becoming part of the enterprise architecture. As big data capabilities become more of an enterprise and an operating model, I think those budget issues will become part of the general budget cycle for IT.

The third [challenge] is talent, and there are two parts to that.

One is just the generation of talent, and I think talent generation will definitely need to scale with the solutions. But this isn’t much different than what we saw in the beginning of the e-commerce revolution. And more talent is being generated. For example, Accenture has a big data academy that is helping us generate our own data technologists and data scientists of the future. But our clients, too, are leveraging our academies and other academies to create the talent. So there’s a tremendous focus on talent.

The second thing that’s happening is that big data is becoming more accessible. More and more of the query languages that are getting added to big data are much more traditional than the original approaches in the sense that they’re getting more SQL-like or they’re getting more object-programming-oriented.

And so when you look at the convergence of those three things – the investment in security, the fact that [big data] is becoming part of the IT architecture … as well as the investment in talent … I believe, over time, these will not be the issues we will be talking about. In fact, maybe in a year or year-and-a-half the issues we’ll be talking about will have more to do with scalability and applicability to specific business models.

What recommendations do you have for organizations looking to ensure big data success?

The first thing is that you need to engage [with big data]. One of the big dichotomies we saw in the survey [in terms of perceived success] is that if you engage and you work through to finish your project, you are extremely positive about what comes out in the end. We sensed some hesitancy in engagement because these are the early days of big data. But once you get past that, and once you engage, there are a series of steps you can take if you want to pursue big data.

The first: Because data sources and technologies are in a constant state of flux and movement, you need to spend a little time learning. Understand what your choices are, both in technology as well as business application. And then once you find [a suitable option], go for it. Learn from going after a specific issue with a specific technology, and see how you succeed in that.

The second: Don’t attempt to do all the heavy lifting at once. Start small and grow. Whenever we’ve seen clients start small and grow, they’ve felt very comfortable and, given the nature of this technology and the use cases, they’ve grown very fast. So starting small doesn’t limit your growth, it just ensures that you get comfortable with the new technology, the culture and the real integrative process that it drives.

The third: Focus on building skills. Talent is still one of the biggest challenges, and so building skills of employees through training as well as hands-on development is important. You can always tap outside expertise, and that’s important, too, but skills development has been very important. Very telling is that about 5 percent of the executives we surveyed said their companies have been able to use only internal resources to [complete their big data initiative]. So expect to leverage the external ecosystem to get going.

Were there any results from the research that surprised you?

Once clients engaged and actually finished a project, however small or big, they were extremely happy with how that came about. So we saw this very clear dichotomy between those who’ve taken the jump and those who haven’t.

The second … security jumped up to the top. I’m pleasantly surprised with that, because it’s a sign that people seriously want to understand how to integrate big data into their enterprise. It’s an indicator of the fact that clients are looking to truly engage in this.

How do you think big data will transform business in the years to come?

Fundamentally. It will move business from being something that’s driven mostly from an inside-out perspective, where internal data guides most of the thinking as well as decision processes, to be much more an outside-in process, where data outside the enterprise has a tremendous effect on strategy, tactics and day-to-day operations. I think that’s the big shift that we’re going to see.

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