They weren’t called discretionary bonuses for nothing. When banks decided how much to pay in the past, there was always a finger-in-the-air component – an estimation from a manager on how much was needed to make someone stay and a reflection of how much he or she valued you. But what if those nebulous components can be accurately measured? Similarly, when decisions are being taken about cutting costs, what if corporate politics, ‘visibility’ and favouritism are irrelevant? What if performance is absolutely and objectively measurable?
Welcome to the new world.
“People analytics has taken off and banking is one of the industries where its use is the most advanced,” says Morten Kamp Andersen, a former Deutsche Bank VP who consults on the use of human analytics at organisations across Europe. “Banks are monitoring and tracking employee performance much more closely. It’s an evidenced-based approach to managing people.”
People analytics is data. It’s big data used to identify trends and correlations that drive performance across the organization. It’s little data used to identify how well individuals are doing. It’s no coincidence that Goldman Sachs’ Human Capital Management (HCM) division is now underpinned by its own team of “strats” working out of Warsaw, Bangalore and Jersey City. Nor is it any coincidence that J.P. Morgan hired Google’s head of people analytics, Ian O’Keefe, to head its workforce analytics function last June.
O’Keefe didn’t respond to a request to comment for this article. Google, however, is well known for its use of analytics to underpin HR decision-making. At Google, data and metrics replace opinion. Google has tools that predict whether you’re going to resign, that analyze whether you’re a good leader, that assess the value of its top performers. J.P. Morgan is almost certainly after something similar.
Credit Suisse already has it. In finance, Credit Suisse’s people analytics team has been the most vocal about its achievements. Credit Suisse has discovered that graduate students with “sustained accomplishment” in music make better hires, and that students who’ve earned leadership positions through sustained effort (e.g. as head of sporting teams) make better performers than those who’ve earned leadership positions as a result of a popular vote (e.g. as head of a student body). It’s also used analytics to predict which employees might quit (and discovered that it’s not those who’ve swapped jobs within the bank). Two years ago, Credit Suisse said it had promoted 300 people after the data indicated they were at risk at leaving.
It’s here that people analytics becomes interesting. What if the data knows more about your motivations than you do? What if the data indicates when you’re likely to slack off? Or when it makes sense to promote you, or get rid of you, or move you to another team? Kamp Andersen says banks are already using data to match high-performing salespeople to top clients. Anecdotally, data already plays a big role in banks’ cost-cutting decisions. “Transparency on performance is greater than ever,” says one strategy consultant who helps banks restructure. “Data is used to take out fixed costs and drive automation.”
In this sense, the likelihood of you keeping your job in finance – and getting paid – now has everything to do with data and a lot less to do with how well you get on with your boss or play internal politics. This has its advantages. “Data is a great way for organizations to stay on top of unintentional bias that may crop up in decisions about people,” says Dave Weisbeck, chief strategy officer at Visier, a workforce analytics company with banking clients. “It allows people to move past, ‘I think’ and to have a conversation that starts with the facts.” Kamp Andersen says data could help eliminate ageism in banking by highlighting the value of managing directors aged 50+, or by showing that women make better leaders than previously acknowledged.
But what if data also shows that women aged 36 are less productive? Or that M&A bankers who commute for more than an hour a day achieve less? Or that male salespeople with more than two children generate higher revenues than those with one or none at all, whereas those with more than three bring in less? At which point does data become too intrusive? And at which point does data stray into ethical and legal black spots?
It’s something that O’Keefe at J.P. Morgan is alert to. Three years ago he noted that data can be used to create “highly sensitive” and “highly personal” profiles at an individual level and that people analytics professionals are, “potentially influencing livelihoods and careers.” Because of this, O’Keefe said, “the ethical handling and interpretation of data is absolutely paramount.” Kamp Andersen agrees: “There’s a big ethical dimension to this – a huge risk that data is misused or used to make false assumptions.”
For this reason, David Green, global director of people analytics at IBM, says analytics are best applied to data relating to performance or engagement. Performance is most easily measured in front office roles with clear outputs. Engagement can be measured by interaction with internal social media, like – say – Goldman’s new Symphony system, or even wearable devices that measure how much you walk about and interact with other teams. “The power of analytics is that they can help firms hire and develop the right people,” says Green. “Employees are naturally reticent, but analytics can help them too.”
As analytics take over, the implication is that staying employed in banking will be all about tangible results and sending lots of positive messages on internal chat systems rather than chatting up your boss or your boss’s boss. For the moment, though, not everyone in banking concurs. “Data will never take over in banking unless there’s no such thing as an “old boys network”,” says a director at one European bank. “It’s still all about how well your personality fits with the group. These analytics are just something applied by HR after managers have already made the decisions.”