Doing Big Data Analytics Right

By Samuel Greengard

Over the last few years, the term “big data” has evolved from a vague concept into a mainstream business strategy. But somewhere at the intersection of conceptual promise and real-world implementation, many business leaders, including CIOs, have faced a tough realization: putting the concept to work requires different tools, technologies and strategies than in the past. What’s more, the task isn’t becoming any easier as mobile devices, social media, and other tools and technologies add to an already enormous stream of both structured and unstructured data.

A June 2014 Accenture study found that 89 percent of 1,000 respondents from 19 countries recognize that big data is “very important” to their digital transformation, but 62 percent indicated that they had no idea how difficult it would be to implement it.

There are plenty of inherent challenges to big data analytics, but there’s also a lot of “noise” and confusion in the marketplace, says Vincent Dell’Anno, a leader in Accenture’s Big Data Practice. “Every vendor and product claims to offer a big data solution and, unfortunately, everyone seems to have a different definition of what big data is and what it does,” Dell’Anno says. “It’s critical to filter the signal from the noise.”

Tapping the Right Data

Big data isn’t merely an extension of BI and it isn’t necessarily about sifting through huge volumes of data. It’s crucial to tap the right data in the right situation at the right time. This may translate into large data sets or relatively small and defined data sets. It may mean combining structured, unstructured and semi-structured data in different ways. “Big data drives value only when the right data is put into motion,” says Vijitha Kaduwela, founder and CEO of Kavi Associates, a consulting firm that specializes in big data and analytics. Scott Schlesinger, senior vice president and head of North America Business Information Management at Capgemini, says that results come down to one fundamental thing: “What insights and value can I derive from the data?”

A starting point is to identify the business problem or business case that the organization is looking to address and map it to the right benchmarks, metrics and key performance indicators (KPIs). Schlesinger says that efficiency, profitability and competitiveness are typically at the center of the equation. “What issues keep executives awake at night?” he asks. “What factor are the true measures of business success?”

Schlesinger argues that it’s necessary to focus on a handful of key factors: framing the primary issue, identifying the right data, and then acquiring, organizing, storing, and cleansing the data. Along the way, it’s often essential to rethink data governance as well as data-sharing policies and relationships with partners.

“You have to do the basic blocking and tackling,” Schlesinger says, “before you can get to the Holy Grail of better insight.”

Taking a Creative Approach

Yet success also revolves around approaching problems more creatively. An organization may require an infusion of data scientists but also individuals—business analysts and others—who can think about problems and challenges in far broader and more three-dimensional ways than in the past. With talent difficult to find (41 percent of the Accenture survey respondents said that the needed skills are in short supply), organizations may benefit by retraining or cross-training employees from line of business functions. What’s more, best-practice companies source talent workers wherever they can find them, Accenture notes. Some organizations also benefit from cloud-based tools, including analytics as a service and data science as a service.

Accenture found that the most successful organizations start with focused initiatives in practical areas, such as customer relations, product development and operations, rather than trying to do everything at once. And CIOs, of course, play a critical role in the transition to big data analytics. “Over the next 10 years we will see a revolution in how organizations leverage big data for business benefits,” says Kaduwela of Kavi Associates. “It’s critical that business leaders devote the necessary attention and resources to making it a success.”

About the Author

Samuel Greengard is a contributing writer for CIO Insight. To read his previous CIO Insight article, “Five Ways to Make Flextime a Win-Win,” click here.

Samuel Greengard
Samuel Greengard
Samuel Greengard writes about business, technology and other topics. His book, The Internet of Things (MIT Press) was released in the spring of 2015.

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