Big Data @ Work
By Thomas H. Davenport
(Harvard Business Review Press, 228 pages, $34.50)
Big data is big these days. It’s a common catchphrase in management, even if many of us have no idea what it really means.
The confusion comes not just because it’s about something both abstract and mathematical, but because, according to analytics expert Thomas Davenport, it’s a bit misleading. “It’s a catchall term for data that doesn’t fit the usual containers. Big data refers to data that is too big to fit on a single server, too unstructured to fit into a row-and-column database, or too continuously flowing to fit into a static data warehouse. While its size receives all the attention, the most difficult aspect of big data really involves its lack of structure,” the professor of information technology at Wellesley, Mass.-based Babson College writes in Big Data @ Work.
Because the term is so imprecise, he says firms must deconstruct it to understand how it may apply to their operations. He cites some industries that have big data but aren’t making proper use of it. Banks have massive amounts of information about their customers but have been underachievers in helping them make sense of it all and presenting targeted marketing offers. Retailers have purchase behaviour information from their point-of-sales systems but, with the exception of Wal-Mart and Britain’s Tesco, haven’t done a lot until recently. Electric utilities have been talking of a “smart grid” for many years, but other than rolling out some smart metering systems have made few strides. Media and entertainment companies believe in intuition and gut feel, so fail to use data to help improve their batting averages in developing hits.
“The point is not to be dazzled by the volume of data, but rather to analyze it – convert it into insights, innovations, and business value,” he writes.
LinkedIn did that when it developed its popular People You May Know feature, which suggests to members some other folks they may want to connect with. Those possible connections come from analyzing data about shared schools, workplaces, connections and geography. Prof. Davenport says LinkedIn’s growth trajectory shifted significantly upward after developing this feature, and it is now being copied by other social media sites.
As sensors are increasingly put in devices from cars to refrigerators – one estimate suggests 50 billion sensors will be connected to the Internet in about 10 years time – that presents an avenue for corporate ingenuity. He notes that General Electric already monitors more than 1,500 turbines from a centralized facility. The company makes a lot of money servicing industrial products, and so any clues it can gain to spotting possible breakdowns could be beneficial.
He imagines a retail scenario that many employees and consumers might find Orwellian. It might include monitoring how often employees approach customers who seem to be struggling to find what they want. As well, the store might analyze what items customers looked at but didn’t buy, and send them follow-up offers to tempt them into a purchase.
Big data opens opportunities in all facets of a company’s operations, from marketing and sales, to manufacturing, to the supply chain and human resources. Strategy might be improved, as well as finance and information technology.
Big data might be used to cut costs, reduce the time needed for certain processes, develop new offerings, or support internal business decisions as banks do when they probe to understand aspects of the customer relationship. He also suggests you can use the information in one of two modes: discovery or production. Data discovery is essentially innovation – learning what’s in your records and figuring out how it might be used to leap ahead. Production involves putting a big data application you have developed into action.
From 1954, when United Parcel Service created the first corporate analytics group in the United States, until about 2005, management was in what he calls the Analytics 1.0 period. Data sources were relatively small and structured, and came from internal sources. Records were stored in enterprise warehouses or data marts, before analysis, and that analysis was primarily descriptive, for reporting. We entered the Analytics 2.0 era as online data were exploited by Internet-based firms like Google and eBay. The information often came from outside the company and was very large or unstructured, as the era of big data commenced. The fast flow of information meant it had to be stored and processed rapidly, often using parallel servers.
We’re now in Analytics 3.0, which combines the best of both worlds, blending big data with traditional analytics to yield insights with speed and impact. The defining change of this era, he says, is that it’s not just online firms, but virtually any type of company in any industry that can take advantage.
The book covers all aspects of the issue, from what big data means, to whom you must hire, to what technologies to follow. It’s surprisingly easy to read, given the topic, and offers good examples to ponder from startups and large firms.
Business coach Andrew Thorn looks at Leading with Your Legacy in Mind (McGraw-Hill, 229 pages, $28.95), where he discusses turning passion into purpose, change into growth, balance into focus, and listening into hearing.
Training-video developer G. Shawn Hunter explains how innovative leaders drive exceptional outcomes in Out Think (Jossey-Bass, 273 pages, $29.95)
In The Truth about Trust (Hudson Street Press, 266 pages, $28.95) psychologist David DeSteno explores how trust determines success in many facets of our life.
Harvey Schachter is a Battersea, Ont.-based writer specializing in management issues. He writes Monday Morning Manager and management book reviews for the print edition of Report on Business and an online work-life column Balance. E-mail Harvey SchachterReport Typo/Error
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