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In the article Big Data Analytics: Eating An Elephant, the author explores what big data currently means to supply chain management and some of the ambiguity big data brings to business.  The metaphor the author first introduces is that big data is like an elephant – it can’t be swallowed in one bite, and no matter where you start, you will be eating for a while.  In this sense, big data solutions can be daunting to begin, and once started, must be continuously evaluated and updated to be efficient.  A recent survey of executives found that 81% thought big data was “disruptive and important,” which is a curious way to describe big data.  As seems to be the trend through many articles and current opinions about big data, many think it’s useful, some are willing to try it, and a few are actually reeking the benefits from it.

One common assumption in the supply chain world is that big data has the potential to solve complicated problems.  An example illustrated in the article is that of Dell’s customer support function.  Dell notes that service information for one customer can encompass 25,000 log files and up to 400 GB of data.  Through identifying the important variables and the use of big data analytics, Dell has seen an 84% improvement is helping solve customer issues faster.  Its noted that Dell had a specific purpose in mind as well as a good understanding of the data it already had.  Dell wasn’t fishing for answers it wasn’t sure existed.  This lesson in big data is simple: have a problem and purpose in mind when analyzing the data.

The author further drives this point home as the article goes on.  As he points out, there’s no reason to create a giant data repository and then hire individuals to analyze the data, looking for hidden trends.  It’s about asking the right questions to begin with.  An example of this type of corporate behavior can be found at Nestle.  Nestle has created a hierarchy for its use of big data to understand demand.  First, baselines are generated, then casual modeling is done, then advanced modeling for decision making.  Each successive step uses more data and more modeling.  This structured approach is allowing Nestle to create value from the big data is has.


Do you think the hindrance many companies see to using big data is that they believe big data analytics involves data scientists examines huge data sets?

Based on the feeling that big data is “disruptive and important,” do you think there is some frustration with the digital revolution?

When will we or are we already seeing the lack of adoption of big data techniques hurting companies?