Volume 21, Number 10
October 2015

To leverage the impact of Big Data, you need the power of analytics, says Professor Peter Bell.

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Peter Bell has a message for the ever-growing number of true believers in the power of Big Data. When it is not combined with Big Data analytics, says the Ivey Business School Professor of Management Science, all you get is just a very large mess of useless data. And even if you invest in analytics looking to gain competitive advantage, you may not achieve the intended impact. 

“People think they can work magic with Big Data,” Bell says. “But how and where a business goes about deploying analytics is just as important as having senior management jump on the Big Data bandwagon. Senior management often has little appreciation of the difficulties of Big Analytics problem solving, not to mention the attention required to manage a successful analytics team.”

 Simply put, Bell’s research suggests analytics is far riskier than people realize because firms often end up with no advantage for their efforts. In fact, in many cases, investing in analytics with the intent of being the industry leader fails because the fruits of early-adopter investments often quickly spread around the industry. And when that happens, the early movers pay all the costs, but everyone benefits.

 “So if you can’t figure out a route to creating a sustainable competitive advantage while keeping key elements of the knowledge created confidential,” Bell notes, “it is probably better to be a follower than an industry leader.”

First and foremost, Bell says organizations have to think long and hard about whether to do analytics alone in-house or to work with a business process outsourcing (BPO) partner. But either way, any company deploying analytics today must understand that its competitors are attempting to gain the upper hand via Big Data strategies as well.

“The potential of analytics is now a topic du jour at industry conferences,” says Bell, who joined Ivey in 1977 after earning BA and MA degrees in Chemistry from Oxford University and MBA and PhD degrees in business from the University of Chicago. “But I have been studying this area for four decades, and despite the hype, the evidence suggests the deployment of contemporary analytics is now often just the table stakes required to be competitive in today’s markets.”

Bell’s research indicates the act of deploying analytics alone can no longer be relied upon as an automatic source of competitive advantage because most of the analytic strategies now being deployed can be easily replicated. American Airlines, Bell points out, successfully deployed analytics to gain a significant competitive advantage in seat pricing between the mid-1980s and mid-1990s. But then other airlines caught up. And while airlines still spend large sums on pricing analytics, the money spent today is “just the price of survival, not a source of significant advantage,” says Bell.

Using analytics to create and maintain competitive advantage today requires learning from early adopters. In “Sustaining an Analytics Advantage ,” an article recently published by MIT’s Sloan Management Review, Bell notes speed of deployment and senior-level support are a must if you want to win the analytics game. But where you apply analytics is equally important. Keep in mind that many companies make the mistake of thinking they can get a significant leg up on the competition by targeting small and easy-to-identify problems that tend to be common among industry players. But to gain and sustain competitive advantage via analytics, Bell says the evidence suggests that targeting major, complex, and critical issues offers more potential, especially if the issue in question involves a constantly changing landscape because this allows valuable knowledge to accumulate over time via feedback loops, making your analytics advantage harder for new entrants to quickly replicate.

Controlling all created data and keeping it secret are other common traits of successful early adopters. Wal-Mart, for example, has been playing the analytics game for a long time, and it has done a good job of keeping its cards very close to the corporate chest despite using BPO partners. And that isn’t easy to do. As Bell and University of Liverpool instructor David Fogarty point out in “Should You Outsource Analytics ,” another Sloan Management Review article, while outsourcing can put intellectual property at risk, companies can’t always do everything they want to do with analytics internally, especially with rising demand for the tight supply of talented analysts. Keeping secrets, of course, is also difficult with the many opportunities to jump ship that are always open to internal talent.

As a result, in addition to targeting a Big Analytics problem, building relationships is another key to sustaining a competitive advantage.

“Going with external partners is not for everyone,” says Bell. “But if you choose to outsource some of your analytics, then my research shows gaining and sustaining ROI requires that all concerned are crystal clear on who does what, who owns what, and who can do what with the information and knowledge created, not to mention what happens if the external team is involved in any M&A activity. Meanwhile, you must also ensure that your in-house team is engaged and not looking to jump ship.”

So what’s the next big thing in analytics? According to Bell, the success of innovative companies like Google at using analytics to conduct data-based people management is certainly an area worth watching.

“I think collecting and analyzing data on what people actually do at work is going to be a huge game changer,” he says.

Bell, of course, doesn’t spend all his time researching analytics. After all, he has a very research intensive hobby: rebuilding classic motorcycles with his brother. To date, they have restored more than 40 bikes that can be seen in the United Kingdom’s National Motorcycle museum, which holds the world's largest collection of British motorcycles.

“You often can’t find the documents you need to restore old bikes to their original condition. We are working on a bike right now, a 1961 Matchless, and finding parts and information is a real challenge. Some Big Data on old bikes would be really helpful.”

 

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