Control Yourselves, Big Data Dudes

According to a recent piece in the New York Times, we live in an era of “Big Data.” It’s a time of a “data flood” and “data-driven discovery and decision-making.” Some even call it a revolution.

“A report by the forum, ‘Big Data, Big Impact,’ declared data a new class of economic asset, like currency or gold,” the Times noted.

Interesting stuff, to be sure. But we were also taken by the published comment of a Times reader, Danny P. of Warrensburg, Mo. As he wrote: “‘Big Data’ that the article refers to doesn’t have any controls in place, and adequate controls are what allow quantitative study to determine cause-and-effect relationships with any real degree of accuracy. Without those controls, big data becomes nothing more than the bag of letters in Scrabble.”

This was a very Druckerian point to make. For as Peter Drucker saw it, measurements, information systems, feedback loops and the like–what he called, in general, “controls”—need to be carefully constructed if they’re to be effective. In fact, as Drucker explained in Management: Tasks, Responsibilities, Practices, controls should meet seven specific criteria:

1.     They must be economical.  “More controls does not give better control. All it does is create confusion. … The capacity of the computer to spew out huge masses of data does not make for better controls.”

2.     They must be meaningful. As we’ve discussed before, “the events to be measured must be significant either in themselves . . . or as symptoms of at least potentially significant developments.”

3.     They must be appropriate to the character and nature of the phenomenon measured.  This was supremely important. The controls must “bring out clearly what the real structure of events is.” Getting 50 employee complaints a month might seem better than getting 100 employee complaints a month. But if the 100 complaints are dispersed throughout the company and the 50 complaints are targeted against one abusive supervisor, then just looking at numbers can be fatally misleading.

4.     They must be congruent with the events measured. “A measurement does not become more accurate by being worked out to the sixth decimal when the phenomenon is only capable of being verified within a range of 50 to 70%.” (Economists, take note of that one.)

5.     They must be timely. “The time dimension of controls has to correspond to the time span of the event measured.” Don’t do a daily temperature update to gauge global warming.

6.     They must be simple. As we’ve explored in another context, “complicated controls do not work. They confuse.”

7.     They must be operational. Someone has to be able to use the information to do something. “It should never just say, ‘Here is something you might find interesting.’”

Does all the data coursing through your organization meet these seven specifications?