Getting Smart With Big Data
How smaller companies are becoming increasingly sophisticated about analyzing multiple forms of data.
May 10 2013 by William J. Holstein
In addition to raw computing power, the information technology industry has made rapid strides in improving its analytical software. Only two years ago, for example, IBM unveiled its Watson computer that was able to defeat human contestants on the television game show, “Jeopardy.” Now the same software is available in IBM’s new Power7+ servers for prices starting at about $6,000. “We’ve taken the intelligence of a Watson and put it in systems that are priced to the small and medium-sized company range,” says Ed Abrams, vice president of IBM’s Midmarket Business, based in Norwalk, Connecticut. “The small and medium-sized business, when armed with big data analytics, can drive true change and true innovation into the marketplace.”
Not surprisingly, providing analytic hardware and software to SMEs is one of the hottest spots in the entire IT industry. It’s a very fractured industry, but IBM says its sales of analytics to SMEs are growing at a faster rate than IBM’s sales as a whole. It is competing aggressively in the space by offering $4 billion in financing to buyers of its systems. SAS is a long-time big player in the space, but EMC, SAP, Oracle and every major IT company are targeting this sector.
Even decidedly low-tech companies are realizing gains from modest investments in data analytics. IBM’s Abrams tells the story of a California distributor of cantaloupes and honeydew melons that acts as a middleman between growers and wholesale and retail customers. “They’re using predictive analytics to look at heat indexes, the size of picking crews at various growing sites and other factors to get a very good sense of the inventory they will acquire,” he explains, noting that a single manager working with an IBM Power7 systems handles the process. “That helps them to know how to price the fruit and tells them how much they can provide to retailers and wholesalers.” The size of picking crews is considered unstructured data, while structured data includes the number of bruised or damaged pieces of fruit.
It seems that when an SME company combines structured and unstructured data, it is able to achieve higher levels of insights than it otherwise can. Fiona McNeill, global product marketing manager for text analytics at SAS, says her company had a customer that evaluated insurance claims in hopes of minimizing workplace injuries in police departments, among other workplaces. The system analyzed handwritten accounts of hundreds of police officer accidents and found a pattern. Officers, particularly women, were suffering wrist injuries when they were putting an offender in the rear seat of their cars. The insurer suggested that all officers wear wrist guards. Result? A major reduction in accidents.