Big Data for Retail
- by David Dorf
Right up there with mobile, social, and cloud is the term "big data," which seems to be popping up lots in the press these days. Companies like Google, Yahoo, and Facebook have popularized a new class of data technologies meant to solve the problem of processing large amounts of data quickly. I first mentioned this in a posting back in March 2009. Put simply, big data implies datasets so large they can't normally be processed using a standard transactional database. The term "noSQL" is often used in this context as well.
Actually, using parallel processing within the Oracle database combined with Exadata can achieve impressive results. Look for more from Oracle at OpenWorld as hinted by Jean-Pierre Dijcks.
McKinsey recently released a report on big data in which retail was specifically mentioned as an industry that can benefit from the new technologies. I won't rehash that report because my friend Rama already did such a good job in his posting, Impact of "Big Data" on Retail.
The presentation below does a pretty good job of framing the problem, although it doesn't really get into the available technologies (e.g. Exadata, Hadoop, Cassandra, etc.) and isn't retail specific.
Determine the Right Analytic Database: A Survey of New Data Technologies
So when a retailer asks me about big data, here's what I say: Big data refers to a set of technologies for processing large volumes of structured and unstructured data. Imagine collecting everything uttered by your customers on Facebook and Twitter and combining it with all the data you can find about the products you sell (e.g. reviews, images, demonstration videos), including competitive data. Assuming you could process all that data, you could then personalize offers to specific customers based on their tastes, ensure prices are competitive, and implement better local assortments. It's really not that far off.
