Talk of talent analytics has taken over the recruiting blogosphere. This exciting new workforce science has quickly created a party of true believers, and along with it, a party of naysayers. It all comes down to what people truly believe to be performance factors and how that information can be used for hiring for the future.
Team Talent Analytics
Josh Bersin, corporate HR, talent management and leadership analyst, says that a lot of companies already have the information that it takes to conduct a talent analysis in order to assess performance factors. Companies have been storing this information for years: education, location, performance numbers, demographic information and a gamut of other factors. In the right hands, this information can be used to predict top performers for future hiring.
Organizations are finding that when implementing talent analytics in their workforce, the data is showing that what were once considered traditional, common sense factors in performance, are all wrong. Many companies believe in the hiring formula:
Good School + Good Grades = Good Performers
Talent analytics is contending that most companies don’t have a realistic grasp on what their performance factors actually are. Bersin said,
“This, to me, is the single biggest BigData opportunity in business. If we can apply science to improving the selection, management, and alignment of people, the returns can be tremendous.”
Talent analytics don’t only claim to predict, they can should also be able to problem solve. Clara Bryne reported in a VentureBeat article ,
“One Talent Analytics customer, a forklift operator firm, constantly received complaints about customer service. The operators were not friendly, but as it turned out, they were very focused on accuracy. So the company started to highlight accuracy as its main selling point rather than trying to transform the personalities of its staff.”
Those who are decidedly not on the talent analytics bandwagon have some pretty good points of their own. First, this is a new science. Everyone knows not to buy the first generation of the newest iPhone, because the next and improved generation is coming along shortly. Many organizations are letting the pioneers work out the kinks and prove the returns and validity before jumping aboard.
Additionally, we aren’t exactly looking at a DIY work project here. Unless companies are willing to go to the pros, talent analytic initiatives can be hard to get off the ground. In an InfoChimps survey of 300 IT professionals, 80% of respondents said that the top two reasons analytics projects fail are…
1) Managers lack the right expertise in house to “connect the dots” around data to form appropriate insights.
2) Those projects lack business context around data.
That is why, according to MIT Sloan Management Review, that 55% of big data analytics projects are abandoned.
While many companies might actually have what it takes to implement the use of talent analytics, there are probably a lot more whose existing data falls short of the requirements. Greta Roberts of Talent Analytics Corp., who is obviouslynot on Team Traditional, brings up a good point however,
“Only the business unit an deliver performance data; real business performance metrics like sales quota achieved, number of calls handles in a call center, customer service scores, ash drawer shortages for tellers, how many lines of code a software developer writes, numbers of orders processed, and other KPIs (key performance indicators) directly measured and valued by the business.”
We want to know what you think and what you’re doing. Are you planning on using your organization’s big data for talent analytics? Are you going to wait and see what happens in this emerging trend? Tweet us and let us know .