Big data has been lauded as a market developer, a revenue builder, and a premier analyzer of consumer behavior. Now big data is steadily making its way into the back office and operational cost cutting, too.

Since 2012, Intel has used big data and predictive analytics as a means of bringing chips to market faster. During manufacturing, each chip that Intel makes goes through a quality check that includes an extensive battery of 19,000 tests. To reduce test time, Intel began analyzing historical data that was gathered from pre-release chips. “We’re taking some of the information that’s coming out of the manufacturing process for those pre-release chips, and looking at it at the wafer level,” Intel’s Ron Kasabian is quoted as saying in a 2013 InformationWeek article. “Instead of running every single chip through 19,000 tests, we can focus tests on specific chips to cut down test time.” This predictive analytics process, implemented on a single line of Intel Core processors, saved Intel $3 million in manufacturing costs in 2012.

The supermarket chain Tesco used big data to cut its annual refrigeration cooling costs by 20% across 3,000 stores in the UK and Ireland. Tesco collaborated with IBM’s research laboratories in Dublin to analyze gigabytes of refrigeration data, which revealed that many of Tesco’s stores in Ireland were running their refrigerators at lower temperatures than needed. “We developed a set of key performance indicators [KPIs] that looked at data aggregated every month over a year,” said Niall Brady, IBM’s research engineer for intelligent buildings and energy analytics, as quoted in a 2013 ComputerWeekly article. “Without knowing anything about how the refrigerators should perform, we could identify when they were behaving in an anomalous way.”

Focusing on sustainability and savings in building energy consumption, the Massachusetts Institute of Technology (MIT) monitors energy use data that streams in from sensors located in dozens of buildings across the campus. Buildings are color-coded based upon their energy consumption status; for instance, if a building is color-coded red, that indicates a water leak or an energy leak in one of its lighting, climate-control, or ventilation systems. The energy monitoring system can also use big data analysis to predict where problems are likely to surface next. “It makes us more efficient, because we know what to look for,” said Balby Etienne, an MIT buildings-systems analyst, in a 2014 Wall Street Journal article.

Success stories like these tend to float under the radar, when instead they could lead to an enormous and relatively untapped opportunity for organizations to capitalize on big data and analytics. Here are several reasons why the corporate radar usually doesn’t detect these initiatives.

1: Saving money isn’t rewarded in most organizations

Yes, there is the employees’ suggestion box on how to save money and do things better, but when it comes to what corporate boards care about and how stock incentives and raises are determined for C-level executives, the big money goes to those who drive revenue, not the cost cutter in the back room.

2: There isn’t enough time to “tune up” support systems

Everyone understands that the power needs to stay on to run the facilities and equipment required by the business, but a majority of employees are hired for other reasons. For most employees, there simply isn’t enough time to make formal studies of how buildings, equipment, and processes can run more economically — unless they are mandated by someone to do so.

3: Most businesses do a poor job of cost cutting

When revenue streams cool down, the first things the CFO looks at cutting are the “discretionary” costs — like laying off employees or cancelling contracts. Buildings or equipment (and the energy they consume) are viewed as “fixed costs” that can’t be helped. The net of it is that the buildings stay running, even though the occupant counts go down — and that’s an expensive way to cut costs.

The bottom line

Big data and analytics can productively be used as automated decision systems that send alerts to managers about cost-cutting opportunities, without requiring the managers to perform extensive research. If cutting unnecessary refrigeration can save one company 20% of its refrigeration energy costs and analyzing test results can save another company millions each year in its chip testing process, there are certainly opportunities for other organizations to obtain similar results.

After all, there are really two ways for companies to build strong balance sheets: earn money or save money.

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