The New York Times ran an excellent primer on the subject of Big Data last weekend. If you missed it, I strongly recommend tapping into your 10 free stories a month (even if you’re equally as offended as I am that the Timeswould give front page online billing to a story covering/giving credibility to the “Occupy” movement as it reflects on what it should do next, rather than covering real news). But back to the subject at hand, Big Data, and a primer you should not miss on the subject. It makes the most sense to first examine what “Big Data” is.
The NYTquotes IDC, noting that the general amount of data generated is growing at 50% per year. This takes the form of both existing and new data types, including those from “countless digital sensors worldwide in industrial equipment, automobiles, electrical meters and shipping crates. They can measure and communicate location, movement, vibration, temperature, humidity, even chemical changes in the air.” That’s about as far as the article gets in hinting at the power of big data to transform procurement and supply chain analysis, but it nevertheless sets the stage for a play that’s not even in the first act.
The article also introduces the topic of unstructured big data, including the mass of information being generated in the form of photographs, videos, social media sites and the like. Some of the more creative forms of data enrichment we’ve heard about in supply chain and procurement include mashing this information up against enterprise data. For example, an organization might combine different data structured enterprise types/forms including supplier volume and quality metrics, raw material demand consumption and inventory at different points in the supply chain with a combination of structured and unstructured external information (weather predictions/patterns, social media commentary like Twitter, news sentiment analysis, YouTube video post types, etc.) to understand the potential magnitude and scenarios of an unfolding weather related incident in the supply chain.
Stay tuned as our primer on big data continues (before we get into the really cool stuff in more detail in a series of posts and research papers this year).
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