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The Most Practical Big Data Use Cases of 2016
August 25, 2016 News


Big data is sexy.  Data scientists are the unicorns of the job market right now. Some days, it feels as though we are living right on the edge of some science fiction utopian future.

But unicorns and sci-fi aside, for businesses, implementing something like a big data strategy has to be more than sexy: it has to be practical.

In my book, Big Data in Practice, I outline 45 different practical use cases in which companies have successfully used analytics to deliver extraordinary results.

These are some of my favorites.

How big data is used to drive supermarket performance

Wal Mart is the largest retailer in the world and the world’s largest company by revenue, with more than two million employees and 20,000 stores in 28 countries.

With operations on this scale it’s no surprise that they have long seen the value in data analytics. In 2004, when Hurricane Sandy hit the US, they found that unexpected insights could come to light when data was studied as a whole, rather than as isolated individual sets.

Attempting to forecast demand for emergency supplies in the face of the approaching Hurricane Sandy, CIO Linda Dillman turned up some surprising statistics. As well as flashlights and emergency equipment, expected bad weather had led to an upsurge in sales of strawberry Pop Tarts in several other locations. Extra supplies of these were dispatched to stores in Hurricane Frances’s path in 2012, and sold extremely well.

Timely analysis of real-time data is seen as key to driving business performance – as Walmart Senior Statistical Analyst Naveen Peddamail runs Wal Mart’s Data Cafe and tells me: “If you can’t get insights until you’ve analysed your sales for a week or a month, then you’ve lost sales within that time. Our goal is always to get information to our business partners as fast as we can, so they can take action and cut down the turnaround time. It is proactive and reactive analytics.”

Peddamail gives an example of a grocery team struggling to understand why sales of a particular produce were unexpectedly declining. Once their data was in the hands of the Cafe analysts, it was established very quickly that the decline was directly attributable to a pricing error. The error was immediately rectified and sales recovered within days.

Sales across different stores in different geographical areas can also be monitored in real-time. One Halloween, Peddamail recalls, sales figures of novelty cookies were being monitored, when analysts saw that there were several locations where they weren’t selling at all. This enabled them to trigger an alert to the merchandising teams responsible for those stores, who quickly realized that the products hadn’t even been put on the shelves. Not exactly a complex algorithm, but it wouldn’t have been possible without real-time analytics.

Wal Mart tell me that the Data Café system has led to a reduction in the time it takes from a problem being spotted in the numbers to a solution being proposed from an average of two to three weeks down to around 20 minutes.

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