Outperform Rivals with Advanced Analytics

There is no dearth of fanfare surrounding the potential for big data to allow companies to gain better insight into their customers, markets and operations.

But there has been less evidence of the potential return on investment for companies that have embraced advanced analytics.

A recent Bain & Co. study, however, has tackled that issue by surveying more than 400 large companies. The study finds that the companies with the most advanced analytics capabilities are outperforming competitors by wide margins.

Bain notes that companies including Samsung and Progressive are tapping into the power of data analysis to boost business performance.

According to the survey, companies leading the charge for building advanced analytics are:

  • Twice as likely than others to be in the top quartile within their industries for financial performance.
  • Five times as likely to make decisions more quickly than their peer companies.
  • Three times as likely to execute decisions as intended.
  • Twice as likely to rely on data, using it very frequently when making decisions.

To succeed in data analysis, Bain notes that companies need easily accessible, large quantities of data; advanced analytical tools; and expertise.

“Big data isn’t just one more technology initiative,” the report notes. “In fact, it isn’t a technology initiative at all; it’s a business program that requires technical savvy. So you can’t just add more capacity and expertise, and expect your IT or marketing functions to begin generating data-based insights. Even if they did, the rest of the company would be unlikely to act on those insights.”

What companies can – and need – to do to exploit big data to boost business performance is to embed data analysis deeply into their organizations.

To do this, Bain advises companies take these steps:

1. Spell out their intents. Companies should lay out plans to embrace data analysis as a new way of doing business.

“A declaration like this from the senior leadership team is an essential precondition for the kind of behavior change this article will discuss,” according to the study. “But the senior team must also answer the question: ‘To what end? How is big data going to improve our performance as a business? What will the company focus on?'”

2. Take analytics horizontal. Bain found that leading companies develop horizontal analytics capabilities across their organizations.

“Organizations don’t change easily, and the value of analytics may not be apparent to everyone, so senior leaders may have to make the case for big data in one venue after another,” Bain notes. “They may need to help people change their everyday behaviors and then continue along the new path without backsliding. As with any major initiative, executives and managers have a variety of tools at their disposal. Leading companies typically define clear owners and sponsors for analytics initiatives.”

For example, Bain notes that Nordstrom shifted responsibly for analytics to a higher management level in the organization, and a global consumer electronics company tapped high-impact analytics projects for additional support to create visible positive results and prompt additional demand.

3. Find a home. Companies need to assign collection and ownership of data across business functions, plan how to generate insights and prioritize opportunities and allocation of data scientists’ time.

Typically, companies adopt one of four models to house analytics within their organizations:

  • A business unit lead, best used when business units have distinct data sets and scale isn’t an issue. In this model, each business unit can make its own data analysis decisions.
  • A business unit lead with central support where units can make their own decisions but collaborate on some initiatives.
  • A center of excellence, set up as an independent entity that oversees the company’s data analysis projects.
  • A fully centralized corporate center that takes direct responsibility for identifying and prioritizing initiatives.

“A good first step is to benchmark your industry and determine your company’s current position in big data analytics and capabilities, compared with that of your chief rivals,” Bain concludes. “If you are significantly behind the competition, you will have the kind of burning platform that is often required to create and sustain change. You can then begin experimenting, testing hypotheses to learn where and how advanced analytics is most likely to help your business.”