Last week we saw two great cases at IEI where “data exhaust” — data created incidentally as part of a particular data management process — turned out to hold great value for our customers. In both cases we took simple telephone verification processes designed just to confirm that an executive’s contact information was still correct (in one case their email address, in the other their direct dial telephone number) and we re-engineered them. After doing so, we were able to identify substantial amounts of new contact information, dead URLs, changed job titles, defunct businesses, new business names, name changes, and deceased or changed contacts. And the beautiful part of the gold hidden in this data exhaust? It was all virtually free. We delivered the same core information to the customer at the same cost, and our customers got a wealth of additional valuable information to boot.
In speaking to folks at Infochimps, EDR, and Databridge about these experiences I realized that we aren’t the only ones gleaning through data “slag” in order to find gold. Metadata generated from various data processing routines and simply from usage patterns is being used by data analysts everywhere to make information services more valuable and even to drive completely new sets of valuable information.
Of course, turning data analysis into improved functionality has actually gone on for quite some time. A few of the more prominent examples are:
- NewsEdge’s customer-generated list of misspelled company name variants: This was used to direct users to the correct information even when they couldn’t spell a company’s name correctly. A lot better than a “404 – No Info Found” error and all for the cost of a little elbow grease.
- Google’s “popularity engine”: Request frequency data was just ancillary info until it was used to prioritize search results.
- Hoover’s took search popularity data and used it to drive the update frequency for the profiles of popular companies. “Not found” results also drove the creation of new profiles as well.
If your firm isn’t wading through your data effluvium looking for value, maybe you should be! One process’s garbage can easily be another’s titanium.