The buzziest term of 2013 is ‘big data’ which originally meant large amounts of formerly inaccessible data, like what people are doing on their cell phones and personal devices while watching TV. Increasingly, the fairly straight forward analysis of relatively modest levels of data is being called ‘big data’, like crunching employee data and comparing with survey results, as in this piece in the NY Times:
Steve Lohr, Big Data, Trying to Build Better Workers
Google, not surprisingly, is committed to applying data-driven decision-making to human resource management. For years, candidates were screened according to SAT scores and college grade-point averages, metrics favored by its founders. But numbers and grades alone did not prove to spell success at Google and are no longer used as important hiring criteria, says Prasad Setty, vice president for people analytics.
Since 2007, the company has conducted extensive surveys of its work force. Google has found that the most innovative workers — also the “happiest,” by its definition — are those who have a strong sense of mission about their work and who also feel that they have much personal autonomy. “Our people decisions are no less important than our product decisions,” Mr. Setty says. “And we’re trying to apply the same rigor to the people side as to the engineering side.”
I think these results are unsurprising. Personal autonomy, domain mastery, and the respect of those that you think highly of are the three considerations in work happiness, and clearly, these derive from doing a job well. The surprising thing is that Google’s earlier biases were believed in the first place.
My friend Rawn Shaw got similar heartburn with the piece, and suggested that ‘social analytics’ might be a better characterization, likening this analysis of employees to the traditional analysis that companies have been doing on their customers, for years:
The big picture people often miss out is that this in concept is no different than doing detailed analysis on customer behavior over their lifecycle. This is easier in some ways since you can more easily reach the data, for a longer period of time, and in greater detail that can be mapped to individuals (e.g., employees), than you typically can get from the limited transactions with an individual customer.
It is also more complicated because there tend more databases with employee and process information across more departments and business functions than with customer information. Plus, there are more issues in accessing this information while trying to maintain privacy of employees. It can be done on a practical level.
I think ‘process’ could be substituted with ‘work’ in the last paragraph, but otherwise I agree with Rawn, and also with his observation that companies have been willing to spend the time and money to analyze their customer data for insight. It is only now, when businesses want to get another tranche of productivity out of the workforce, and they have loads of social data streaming through social tools, only now, when it is becoming relatively cheap are they trying to mine that social exhaust for insights.