Procter & Gamble CEO Bob McDonald is very clear about the critical role for technology at P&G: He thinks better data analytics is at the heart of how P&G will improve productivity and make more innovative products.
To McDonald, the change that's coming is a "cultural revolution" -- instead of reacting to historical business results, P&G is trying to use more real-time data and predictive analysis to make better decisions. "We have to move business intelligence from the periphery of operations to the center of how business gets done," McDonald said.
But to do that, companies need better analytics software. And that's why McDonald spoke Wednesday night at P&G's Cincinnati headquarters to a gathering of a few dozen top IT leaders from companies including Boeing, BP, Disney, FedEx, GE and Goldman Sachs.
P&G's executives, as evidenced in an effort led by CIO Filippo Passerini, think analytics software can get better, faster, if companies together push for software that's more relevant to companies' real business problems. That idea prompted P&G to get IT leaders from different industries together to discuss how they're using analytics. "We want to build a learning consortium of companies who are making business intelligence and analytics a strategic choice, and therefore transforming their business operations," McDonald said.
Any such cross-company cooperation is difficult to sustain. But a number of factors are coming together today to fuel the intense interest in analytics by top executives -- soaring data volumes, new techniques for managing that data, new visualization tools for making sense of data, and more. Through open collaboration among companies, "we have a real opportunity to drive a step change in business intelligence," McDonald said.
P&G has credibility in part because the company has made such a big bet on analytics. McDonald has told his executives they need to "digitize" all P&G's business processes -- think of that as getting rid of paper-based processes across the company, an effort that provides a lot of raw data for use in analytics.
From there, McDonald laid out three broad steps that a company needs to take to put analytics at the center of how a business runs:
1. Get The Right Tools And Technology
Don't expect this technology to come off the shelf, McDonald warned. It may be off-the-shelf elements, but putting them together in a way that's exactly what employees need is the real tech innovation. He offered the example of P&G's Business Sphere, a meeting room that uses data analytics and visualization with videoconferencing to let managers hammer out decisions while looking at the same data and drilling down as needed. That effort used components from many tech vendors, and P&G's team brought them together into what executives needed. P&G has more than 50 of those spheres around the world.
2. Put The Right People In The Right Places
P&G is pretty well known for grooming managers and training people for its future needs. Analytics is now a skills area of particular focus.
In addition to building skills, McDonald said companies need to put those people with data analysis skills close to business operations. An example is an analytical team that P&G charged with using analytics and data visualization to find new ways to tackle the timeless problem of unproductive inventory -- finding excess inventory, figuring out why it was made and looking for viable ways to get rid of it. The effort has saved about $250 million so far, he said.
McDonald calls this more analytical approach to business a "cultural revolution." Tools like Business Sphere can support that culture, since it puts data at the center of discussions. But external forces such as social networking also drive the need to make more real-time, data-driven decisions.
McDonald pointed to P&G's greater use of social media to spread its tear-jerker "thank you Mom" ads that it created for the Olympics. Understanding the ads' impact meant monitoring which videos were taking off on YouTube in which countries, and measuring the sharing of the ads on Facebook, which are new analytical challenges. Then employees needed to decide what action to take based on how often people liked, tweeted, discussed or posted the ads. "I hear a lot about big data," McDonald said. "It's not about the data. It's about how we're able to use the data."
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