In one example of a successful big data implementation at Intel, Stevenson discussed a pilot program that the company ran that identified customers that were more likely to purchase than others based on the heaps of information generated at Intel.
“We looked at that and we examined how our sales coverage model was against those customers, and we took our inside sales force and made calls based on what the predictive analytics said who the customers more likely to purchase were,” explained Stevenson. “In a short amount of time, we were able to cover customers that weren’t previously covered and generate millions of dollars in incremental revenue.”
Another example that Stevenson gave was in retroactive analysis of a failed program that she said cost the silicon giant $700 million when the dust settled. Using the massive amounts of manufacturing data available to them during the die process, Stevenson says that they are now able to see problems sooner using big data analytics to assist in the debugging process.
When asked what advice she would give to others, Stevenson said that she advises fellow CIOs to partner with their business units to identify where the hidden potential is and let that become the guiding light in terms of what problems are focused on – and then stay focused.
“There’s a lot of questions you can answer about any given business, but if you stay focused on a small set of business problems, they you’ll create some early wins and you’re able to grow based on your successful track record.”
Stevenson says Intel has set rules about how to operate predictive analytics in their company, which include small teams of roughly 5 people, and problems that can be solved within a 6 month period of time. Ultimately, says Stevenson, these projects are tied to ROI, which Intel has set a target of $10 million dollars for their first initial deployment. “That helps us narrow and prioritize the problems to higher value problems for the company.”
Stevenson also advises that enterprises start to build the skills that they need. “You need data scientists, visualization experts, data curators, and those types of skills – they’re rare today,” she comments. “It’s harder to learn the business knowledge that is needed to make the data into valuable information than to learn the IT technical skills,” she says in advising people to grow the skills internally. “It will be a focus diligent progression of taking the people that understand your business process today and complimenting them with the technical skills required in building big data management systems or predictive analytic models.”