The Data Tipping Point

More and more data is now available to organizations about their operations, their customer interactions, and their Web sites.  With the arrival of radio frequency identification (RFID) chips on every palette, case, or box of products, and eventually on every individual product, organizations will have more data about their supply chain than ever before.  With mobile devices that are always on and fitted with global positioning system (GPS) chips, every vehicle and employee will be a source of a continuous stream of data.  Customers, too, as their mobile phones interact with organizations' systems, will deliver constantly updated information about their whereabouts and activities.  Growing sensor networks and the integration of massive external consumer databases with enterprise and government databases will only add to this increase in information.  We will, if we haven't already, reach the tipping point where the volume of data overwhelms current data reporting and analysis systems.

Two other factors complicate an organization's ability to take advantage of this embarrassment of riches.  First, most tools, techniques, and methods for managing data are largely for transactional, relational data.  Much of the new information that's available isn't.  It might be unstructured text, as in e-mail or blogs, or structured but not semantically understandable.  Social network software accounts for some of the most popular Web sites and can be a gold mine of information, if it can be extracted and understood.  Second, all these types of data require technology such as voice recognition, image recognition, and text analysis to turn previously unusable data into information.  Bigger volumes of unfiltered data, however, won't be valuable to organizations unless the data can be turned into useful insight.