DC Velocity - April 2015

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1Big data is not just for the IT department. Although widely used, the term
“big data” is poorly defined. This
lack of a clear definition may be the
reason why some supply chain man-
agers make the mistake of thinking
that big data has little relevancy to
their work and is only the concern
of the information technology (IT)
department. If they limit themselves
to viewing big data as just an IT
storage issue, supply chain managers
may overlook the economic value of
the data they are collecting. As it is,
many are not taking full advantage
of the data they already have at hand, nor
are they thinking creatively about how
they could use it.

There are actually three definitions of big data currently in use. Each one is valuable, in different ways, to supply chain managers.

The first, and most “IT-centric,” definition refers to an amount of data that is too large or complex for current mainstream data storage and retrieval systems. In other words, you can’t simply store “big data” in a standard database and run a query against it. Common examples of big data as defined this way include the massive amounts of data generated by sensors (which fill up databases quickly) or free-form text or video data (which doesn’t fit into a relational database very well). This is an IT-centric view because it focuses on the technology of storing and retrieving data, and on the need to use new types of servers and software, such as Hadoop. Hadoop is a file storage system that goes beyond standard relation-al-database technology, allowing users to store and retrieve unstructured data over hundreds or thousands of distributed machines. Facebook, for example, runs with Hadoop.

Supply chain managers don’t need to know the technical details of Hadoop and other such technologies. But they do need to realize that they can and should capture and analyze data if there are business reasons for doing so. For example, if you have thousands of sensors on your manufacturing, warehousing, or trucking equipment, there are now ways for you to analyze the data those sensors collect. You could, for example, use that information to better predict when machines will fail or to improve the fuel efficiency of trucks. Or if your customer service team records customers’ voicemails and e-mails, you can analyze this data to help your company provide better service.

The IT community tends to think this is where the definition of big data ends. Actually, from a manager’s point of view, the following two definitions are just as interesting.