There are two common sources of data grouped under the banner of big data. First, we have a fair amount of data within the corporation that, thanks to automation and access, is increasingly shared. This includes emails; mainframe logs; blogs; PDF documents; business process events; and any other structured, unstructured, or semistructured data available inside the organization. Second, we are seeing a lot more data outside the organization, some available publicly, free of charge, some based on paid subscription, and the rest available selectively for specific business partners or customers. This includes information available on social media sites, product literature freely distributed by competitors, corporate customers’ organization hierarchies, helpful hints available from third parties, and customer complaints posted on regulatory sites.

Sales and marketing have received their biggest boost in instrumentation from Internet-driven automation over the past ten years. Browsing, shopping, ordering, and receiving customer service on the Web have not only provided tremendous control to users but also created an enormous flood of big data into the marketing, product, and sales organization, enabling an improved understanding of buyer behavior. Each sequence of web clicks can be collected, collated, and analyzed for customer delight, puzzlement, dysphoria, or outright defection. More information can also be obtained about sequences leading up to a decision.

Self-service has crept in through a variety of means: Interactive Voice Responses (IVRs), kiosks, handheld devices, and many others. Each of these electronic means of communication acts like a gigantic pool of time-and-motion studies. We have data available on how many steps customers took, how many products they compared, and what attributes they focused on such as price, features, brand comparisons, recommendations, defects, and so on. Suppliers have gained enormous amounts of data from self-service and electronic sensors connected to products. If I use a two-way set-top box to watch television, the supplier has instant access to my channel-surfing behavior. Did I change the channel when an advertisement started? Did I turn the volume up or down when the jingle started to play? If I use the Internet to shop for a product, my click stream can be analyzed and used to study shopping behavior. How many products did I look at? Did I view the product description or the price when looking at the product? This enriched set of data allows us to analyze customer experience in the minutest detail.

Products are increasingly becoming digital. We read books and magazines on tablets, and listen to music on a variety of electronic media. These devices are capable of collecting a lot of statistics on consumer viewing or listening patterns. Apple iTunes keeps track of when I play each song and how often I play it. It can use this information to formulate sophisticated graphs showing how the various published music products are used and their affinity based on usage patterns. iTunes Radio or Pandora can offer a stream of new songs that are similar to the ones you just heard. Pandora lets you decide whether you like the new selection, and changes its selection based on your response. Unlike a typical radio station, which must rely on listener responses painstakingly collected through traditional means, these electronic radio stations can be rapidly tuned to custom music preferences. Amazon does a similar customization to book reading. Since the time I started to write my books and read a large number of books on my Kindle reader, Amazon has made my research easier by offering me new analytics books based on the books I am currently reading and publishing. Unlike my physical library, the Kindle library is providing me with valuable ways to share my books, and is offering Kindle ways to achieve a better understanding of collaboration among readers.

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