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Big Data Analytics: How to Do it Like the Big Boys

There is probably no more well-known example of the power of analyzing large reams of data to improve a business model than Amazon’s recommendation algorithm.

big boys Big Data Analytics: How to Do it Like the Big BoysAmazon and a few other large online companies like Netflix and eBay have a leg up on the competition in the world of big data analytics because they were born digital. But these companies are not the only ones seeing gains from the effective use of big data.

Companies making large investments in big data analytics are generating returns and obtaining significant competitive advantage over companies that have not made similar investments, according to a survey by Tata Consultancy Services.

“Specifically, our study found that the companies estimating the greatest returns last year on big data outspent those with much smaller ROI by a factor of more than three – a median spend of $24 million vs. $7 million,” notes Satya Ramaswamy, vice president and global head of Tata Consultancy Services’ Digital Enterprise group, in a Harvard Business Review blog post.

Other findings include:

  • Of the 53% of the survey respondents that had big data initiatives in 2012, median spending per company was $10 million
  • While 7% of companies with big data initiatives in 2012 invested at least $500 million each on software, hardware, data scientists, consultants and other related expenses, 24% spent less than $2.5 million each on expenses related to big data last year

The companies projecting the greatest ROI from big data have four key characteristics compared to the companies projecting a lower ROI: they’re Internet-centric; they use analytics in a number of different areas; they make unstructured data part of their analytics mix; and they centralize their data analysts, according to the survey. Tata characterizes companies that estimated a return of 50% or greater on their big data investments last year as leaders.

“ROI leaders see greater potential from big data to improve a number of marketing, sales, R&D and service activities,” Ramaswamy adds. “Leaders also believe big data holds much greater potential than do underperformers for improving four marketing activities: monitoring and improving customers’ experience in offline channels (such as stores); discerning competitors’ moves beyond pricing; monitoring external perceptions of the brand; and marketing based on customers’ physical location.”

For ROI leaders, 55% of their digital data is unstructured or semi-structured vs. 46% for the ROI laggards, Tata finds. In addition, 79% of the ROI leaders put their analysts in a dedicated big data group or in IT vs. 68% of the laggards.

“The manager of a large analytics team at a big Internet company believes that removing analysts from business units and functions and centralizing them was critical to success,” according to the Tata survey. “When they reported to business unit managers, ‘our analysts got heavy pressure to confirm what those unit managers were already doing,’ the manager said. Centralizing the analysts also helped them share analytics methods, which he termed the ‘special sauce.’”

EBay, which uses data analysis to get closer to its customers and identify their pain points quickly, underscores the importance of many of the Tata findings.

In fact, all eBay employees need to be data driven, notes Neel Sundaresan, senior director of research at eBay, in an interview with MIT Sloan Management Review .

“Now, not everybody has to look at data, but everybody has to understand data at some level,” he notes. “A lot of data is coming from the behavior of millions of users on our site. So, being able to understand and kind of get your head around that data and the analysis is really important. You can think of it as an attitude change in all grades of people.”

Sundaresan also underscores the importance of analyzing unstructured data flowing in from smartphones, such as images and video data, location data and other sensor data.

“Being able to deal with new kinds of data and understand these new kinds of data – understand what they mean – is a challenge,” he says. “As personal devices get smarter, as we augment ourselves with more and more devices, be it the smartphone or watch or eye-wear, new kinds of data [are] going to be everywhere. Suddenly we are seeing new forms of data, and we need to be prepared to process this data really well and in a near-real-time manner.”

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