Businesses Must Look At Data’s Big Picture to Take Advantage of Analytics
Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills.
No matter the size of your organization, you’ve got big data. Kat Lind, analyst and chief systems engineer for Solitaire Interglobal Ltd., says big data isn’t so much about the quantity of data, rather, it’s a “big-picture look” at the structured data you’ve already captured and classified, along with the unstructured data that hasn’t been filtered or quantified.
One company saved $1.7 million in hardware acquisition costs using analytics
That big-picture look requires much time and effort, so many companies wonder if it’s worth the investment. Some of the biggest benefits Lind has seen in the past few decades include reduced costs, increased collaboration, boosted efficiency, risk reduction and organizational viability.
Save Money With the Big Picture
If you’re looking to increase your bottom line—and who isn’t?—analyzing your data is a great place to start. But to reap the financial rewards, your company must consider the big picture and not stop at surface data.
To explain, Lind points to one case where a major auto manufacturer was trying to figure out a way to charge teenagers more for their cars. Why? The company had concluded that teenage drivers caused paint delamination (or peeling paint). This issue is covered under the carmaker’s warranty for the first three to five years, so it can cost manufacturers a lot of money.
“We were totally aghast, and asked how they came to that conclusion,” Lind recalls. “They said there was a correlation factor of 72 percent between teenagers and paint delamination. But the problem was, the company had stopped at the correlation and didn’t look at the root cause.”
While the initial analysis was technically correct, the pool of data wasn’t big enough. By expanding that pool, they discovered that the causation was the red and black paint itself—and teenagers tend to buy red and black cars.
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