The next step, as Perez sees it, is to tie everything together and create a graphic picture of UPS's various big data systems so the company can uncover new uses for the data -- and thereby derive more business value from it.
"It starts with process improvements, but once you start tying all of this together, it can mean very big changes in the business," Perez says. "That's what we're getting at."
The Win: Millions in Added Sales
Traditional business intelligence is alive and well at Intel, but big data mining and predictive analytics are the forces driving design and manufacturing efficiencies, and uncovering new revenue sources that added up to tens of millions of dollars in 2012 alone.
"It starts with believing that you can change outcomes," says CIO Kim Stevenson of the chip manufacturer's massive success with analytics. That, she says, requires less time spent on historical questions, which is the purview of traditional BI, and more focus on the future, which is what predictive analytics is all about.
Predicting the future at $53 billion Intel requires analyzing massive amounts of data to discern patterns and then applying predictive algorithms to solve high-value business problems.
In 2012, for example, Intel IT created a new reseller sales tool that worked to increase the chip maker's revenue by enabling its sales team to identify, then strategically focus on, larger-volume resellers. The new software engine mines large sets of internal and external data, then applies a predictive algorithm to pinpoint the most promising resellers. So far, it has helped identify three times as many high-potential resellers in the Asia-Pacific region as manual methods typically would have uncovered, according to Stevenson. That translates to about $20 million in potential new and incremental sales. More gains are expected as the tools are rolled out to other geographies.
On the manufacturing front, Intel is using a predictive analytics tool to reduce microprocessor testing time. The company saved about $3 million in testing during a proof-of-concept period. By 2014, as the tool is implemented more widely, Stevenson expects it to rack up another $30 million in savings companywide.
Intel's analytics success has been fast-tracked, to say the least. The key, Stevenson says, is tackling big-money problems with relatively small and swift-acting teams.
"To get the business to focus on the future and ask better questions that would lead to better outcomes, we knew we would have to do things quickly," she explains. "We were coming out of a traditional BI environment where solving master data is the unsolvable problem. People work on it forever and the business doesn't necessarily see the value."
So Stevenson came up with the "six months and $10 million" rule. "A $10 million problem solved in six months is important. Any general manager would say they'd invest six months if we could save them $10 million," she says. (At Intel, business managers must support and fund IT projects.)
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