‘People analytics can now become a strategic competitive advantage’

Fredrick Tay­lor, an indus­tri­al engi­neer, start­ed this trend in 1911 when he pub­lished his report Sci­en­tif­ic Man­age­ment, which stud­ied the move­ment and behav­iour of fac­to­ry work­ers in steel mills. Since then com­pa­nies have deployed thou­sands of engage­ment sur­veys, stud­ied the char­ac­ter­is­tics of top lead­ers, done count­less reviews of reten­tion and turnover, and built mas­sive human resources data ware­hous­es. All in an effort to fig­ure out “what can we do to get more out of our peo­ple?”

Well now this domain is called peo­ple ana­lyt­ics and it has become a fast-grow­ing, core-busi­ness ini­tia­tive. A study, enti­tled High-Impact Peo­ple Ana­lyt­ics and com­plet­ed last Novem­ber by Bersin by Deloitte, found that 69 per cent of large organ­i­sa­tions have a peo­ple ana­lyt­ics team and are active­ly build­ing an inte­grat­ed store of peo­ple-relat­ed data.

Why the growth and why the busi­ness imper­a­tive? Sev­er­al tech­ni­cal and busi­ness fac­tors have col­lid­ed to make this top­ic so impor­tant.

First­ly, organ­i­sa­tions have more peo­ple-relat­ed data than ever before. Thanks to the pro­lif­er­a­tion of office pro­duc­tiv­i­ty tools, employ­ee badge read­ers, pulse sur­veys, inte­grat­ed enter­prise resource plan­ning sys­tems and mon­i­tor­ing devices at work, com­pa­nies have vast amounts of detailed data about their peo­ple.

Com­pa­nies now know who peo­ple are com­mu­ni­cat­ing with, their loca­tion and trav­el sched­ules, their salary, job his­to­ry and train­ing plans. New tools for organ­i­sa­tion­al net­work analy­sis, built into email plat­forms, can tell lead­ers who is com­mu­ni­cat­ing with whom, new tools for audio and facial recog­ni­tion iden­ti­fy who is under stress, and video cam­eras and heat sen­sors can even iden­ti­fy how much time peo­ple spend at their desks.

It could be argued that much of this infor­ma­tion is con­fi­den­tial and pri­vate, but most employ­ees don’t mind organ­i­sa­tions cap­tur­ing this data, as long as they know it is being done to improve their work expe­ri­ence, as shown in 2015 Con­fer­ence Board research, Big Data Doesn’t Mean Big Broth­er. While Euro­pean Union Gen­er­al Data Pro­tec­tion Reg­u­la­tion stan­dards, enforce­able from May 25, will put the bur­den of pri­va­cy and gov­er­nance on HR depart­ments, employ­ers are step­ping up to this and treat­ing such data with great care.

Sec­ond­ly, as a result of hav­ing access to all this data, com­pa­nies can now learn impor­tant and pow­er­ful things. Not only are exec­u­tives being forced to report on top­ics such as diver­si­ty, gen­der pay equi­ty and turnover, but they can also now use peo­ple ana­lyt­ics to under­stand pro­duc­tiv­i­ty, skills gaps and long-term trends that might threat­en or cre­ate risk in their busi­ness.

One organ­i­sa­tion, for exam­ple, found inci­dents of fraud and theft were “con­ta­gious”, caus­ing sim­i­lar bad behav­iour among oth­er employ­ees on the same floor with­in a cer­tain dis­tance. Anoth­er is using sen­ti­ment analy­sis soft­ware to mea­sure “mood” in the organ­i­sa­tion and can iden­ti­fy teams with high-risk projects just from the pat­terns of their com­mu­ni­ca­tion.

Many organ­i­sa­tions now study turnover and can even pre­dict it before it occurs by mon­i­tor­ing email and social net­work behav­iour, enabling man­agers to coach high per­form­ers before they resign. Organ­i­sa­tions now use ana­lyt­ics and arti­fi­cial intel­li­gence or AI to decode job descrip­tions, iden­ti­fy­ing words and phras­es that cre­ate biased recruit­ment pools and pre­vent gen­der and racial diver­si­ty. Man­u­fac­tur­ers use peo­ple ana­lyt­ics to iden­ti­fy work­ers who are like­ly to have acci­dents, while con­sult­ing firms can pre­dict who is like­ly to be burnt out from too much trav­el and auto­mo­tive com­pa­nies now know why cer­tain teams get projects done on time when oth­ers are always late.

AI is, there­fore, enter­ing the domain, giv­ing it even more pow­er and scale. A new AI-based peo­ple ana­lyt­ics tool sends anony­mous emails to a manager’s peers ask­ing sim­ple ques­tions to assess man­age­r­i­al skills. Through its care­ful­ly designed algo­rithms, it gives man­agers an unthreat­en­ing set of rec­om­men­da­tions and has improved man­age­r­i­al effec­tive­ness by 8 per cent in only three months.

For human resources depart­ments, peo­ple ana­lyt­ics is now the num­ber-one rea­son com­pa­nies want to replace or upgrade their HR soft­ware, accord­ing to the Sier­ra-Cedar 2017 HR Sys­tems Sur­vey.

But for chief exec­u­tives, chief finan­cial offi­cers and chief oper­at­ing offi­cers, it’s even more impor­tant. When a sales team is behind its quo­ta attain­ment or a store’s sales num­bers fall behind, why wouldn’t a leader ask “what’s dif­fer­ent about the peo­ple, prac­tices and man­agers at those teams that we may be able to address?” Or an even big­ger ques­tion is “if we want to grow our busi­ness by acquir­ing a giv­en com­pa­ny in Ger­many, what will the cul­tur­al and organ­i­sa­tion­al impact be?” These crit­i­cal strate­gic ques­tions can all be answered by peo­ple ana­lyt­ics.

The his­to­ry of this dis­ci­pline is tac­ti­cal and some­what arcane. For years indus­tri­al psy­chol­o­gists led the effort and focused pri­mar­i­ly on employ­ee engage­ment and turnover. Today, how­ev­er, the indus­try is tak­ing on a new light, refo­cus­ing its ener­gy on oper­a­tional, sales, risk and per­for­mance mea­sures. The tech­nol­o­gy tools are here and com­pa­nies have AI engi­neers ready to analyse the data in a pow­er­ful and pre­dic­tive way. And ana­lysts say this domain will grow for years to come; remem­ber that for most busi­ness­es, labour costs are the largest and most con­trol­lable expense on the bal­ance sheet.

The bot­tom line is clear: peo­ple ana­lyt­ics can now become a strate­gic com­pet­i­tive advan­tage. Com­pa­nies that focus in this area can out-hire, out-man­age and out-per­form their com­peti­tors.