The excellent film ‘Moneyball’ - based on a book by Michael Lewis - demonstrated the power of #Data in sports decisions, illustrated with the following quote: ‘People in both fields operate with beliefs and biases. To the extent you can eliminate both and replace them with data, you gain a clear advantage’.
With an algorithm developed by a Harvard Graduate, the Oakland A’s Baseball team achieved an unprecedented record of 103 wins in a season, resulting in most sports teams replacing decisions based on human intuition, with #Data and #Formulas. So although people think they know what they are doing based on intuition, tradition and industry norms, it is only by using evidence through #Data can organisations understand the real problems and their root causes; how can it be meaningfully fixed and see the resulting improvements.
Back in 2007, Laszlo Bock, Google’s Head of HR discovered through close analysis of both qualitative and quantitive data, that young mothers were twice as likely to quit as the average Google employee. He subsequently increased the Google ‘industry-standard’ maternity leave from 3 months to 5 months and introduced a paternity leave of 7-weeks. As a result, new mothers are now no more likely to leave than average employees.
Additionally, research from the 2 largest New York investment firms in 2012, showed that female stockbrokers earned about 60% the level of commissions when compared to their male colleagues. The tradition and understanding of industry norms would assume therefore that the women were not as effective salespeople as the men. However, by analysing both qualitative and quantitative data of personal histories; trading and asset records; and each broker’s management of accounts; closer examination showed that the women were being given inferior accounts - resulting in reduced sales opportunities, which explained the commission differential. However, when women were given more valuable accounts, they performed equally as well as their male colleagues.
It’s just the same with #DiversityAndInclusion.
Whilst intuition tells us what might be the right things to do, we advise @Predixa clients to establish the baseline qualitative and quantitive #Data, to understand first what needs fixing and the associated root cause of the problem, and then keep measuring and evaluating to manage the improvements.
How do you manage and measure your qualitative and quantitive D&I data?
