Big data is a powerful tool – but only in the right hands.
Asking Kevin Spacey to play the ruthless Frank Underwood in House of Cards was an inspired piece of casting.
Or was it?
Rewind to the appointment of Ted Sarandos as Chief Content Officer of Netflix – who commissioned the blockbuster series – and the decision starts to look a bit less inspirational – and a bit more, well….big data.
Under Ted’s leadership Netflix had begun investing heavily in data-driven programming, creating advanced algorithms that could more accurately predict the desires and behaviors of viewers.
It meant that when Netflix approached Kevin Spacey to star in the show, which went on to win several awards, company executives were able to tell him that they had run the data, viewers would watch the series and a pilot show was simply not necessary.
Before Ted’s arrival at Netflix, the company had been plagued by limited data and out-of-touch executives. The results hadn’t been good.
The point here is all about the use of big data. With more and more companies relying on contingent employees, the race is on to find ever more effective ways of managing that workforce.
Many believe big data is the answer. It’s become the go-to resource for many staffing professionals. But there is a big difference between simply collating data and applying it effectively. Without solid interpretation, raw numbers have limited use when it comes to making predictions.
And despite the highly effective use of big data in the Netflix case, Ted Sarandos himself was quick to point out the real secret behind all those algorithms – people.
“It is important to know which data to ignore,” he confided to journalist Tim Wu of The New Yorker. “In practice, it’s probably a seventy-thirty mix. Seventy is the data, and thirty is judgment… But the thirty needs to be on top, if that makes sense.”
And that’s where companies like CXC have a vital role to play. You have to have the right people to track, process and report the data.
Successful predictive analysis means learning over time, sustained practice, teams that work closely together, domain expertise, employees with mathematical and statistical experience, and, of course, open minds and unbiased analysis.
Without this sort of mindset, companies looking to build and improve their contingent workforce programs are destined to struggle – much like Frank Underwood’s opponents really…