www.humancapitalonline.com ■42 ◆ August 2015

T

he idea that Data (of the wideand "wild" variety) is required torun any form of Analytics (Big orSmall) has not really caught onwith "some" HR professionals, at

TECHNOLOGY

BY SUMEET VARGHESE

The “people lie,

numbers don't”

approach to HR analytics

least the ones I have spoken to. When Isay "some" you may take it to mean"many", since I always keep statisticalsampling requirements in mind wheneverI strike a conversation with anyone in HR(a "few' therefore might just representthe "many" out there, If I am right about

HR professionals must closely study the kind ofwork being done by their Marketing and

Customer Service Analytics teams to figure outthat Big Data Tools have evolved to a point

where they rarely ever care a byte if your datais structured and/or unstructured.

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my sampling). This means that whenI speak to you, I always consider youfirst as part of an important data-set- either by virtue of your title, yourexperience in the HR function.

You see we are always collectingdata from each other, whetherothers like it or not, but what we dowith that data in HR is something Iwill reserve for another post someother day. For the moment though,let me clarify: HR Departments arecaught in a position where they mayhave too much of data and very pooranalytics capabilities to leverage thedata or strong analytics capabilitiesbut hardly any worthwhile data tomine for insights. Frankly, I have noclue which one is better!

Anyways, the sad/good news isthat we need to sort out many seriousdata related issues before we candiscuss the extent to which ourneighbours in Marketing, CustomerService, Finance and Operations useBig Data Analytics:

Is the Data we capture of

any value at all?

Unfortunately, no one has an answer.In one case, I asked a group of HRprofessionals whether we shouldtrack the number of loo breaks thatsenior executives took during aworkshop and whether it wouldserve any purpose. Obviously, therewere blank stares. My question ofcourse wasn't pointless. The loo likethe office water cooler and thecofee/tea vending machine points inan organization is as much a spacefor exchanging workshop feedbackas it is for updating each other onsome juicy company gossip. Whilethis may be a small unwanted detailto be avoided by the HR professionalit might certainly be of interest to aData Scientist, assuming it can offersome interesting clues - Elementary,my dear HR Professional,Elementary! Strangely, with a littlebit of luck I was able to work out acorrelation (not causation) betweenthe pathetic condition of the loosused by the top management at onefirm and the MD's constant refrainthat the organization lacked"ownership". In fact, nobody (andthis included the organization's top

brass) bothered to complain aboutthe stink because they thought itwasn't "their job".

Frankly, HR professionals mustclosely study the kind of work beingdone by their Marketing andCustomer Service Analytics teams tofigure out that Big Data Tools haveevolved to a point where they rarelyever care a byte ifyour data isstructured and/orunstructured. So, ifyou have anemployee's leaverecords in XL, thepoor unsuspectingchap's FB Posts andTweets for a full yearin Word, the person'sperformance appraisalhistory in PDF, and his/her Compensation Datain any format that yourERP spits out, somemeaningful analytics can stillbe derived even if thisemployee record is a gibberishamalgamation of data. For instance,some recruiters have long studiedbehaviour patterns of candidatesbefore, during and after the variousstages of a screening process thatthey have been subjected to. Thanksto these studies we now know that ifyou handle a particular stage of therecruitment process poorly,candidates are two times or threetimes more likely to badmouth thecompany's products and services onsocial media. Obviously, in this caseprocess feedback (quantitative andqualitative data) at each stage of thescreening cycle has been correlatedwith social media behaviour(qualitative data) of the candidates.

A. People models

I remember studying Van der Waal'sequation in school - the finalderivation of the equation, whichobviously had more variables thanthe one initially proposed, wasdeveloped to fit the "reality" outthere because tests/experimentsrevealed the equation had not quitenailed it. If People Models are "workin progress", People AnalyticsDepartments can rub shoulders with

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their scientific peers - if not, suchmodels run the risk of being exposedby a Copernican revolution (whichobviously would happen on thebusiness side first!). We do know fora fact that the

famed/notorious 25 layered (rounds)screening process (possibly, state-of-the-art at that point in time) at Googlegave way to a four layered (rounds)screening process partly becausebusiness managers wanted "good"people in "quickly". I am assuming,Van der Waal was under no suchpressure.

B. Operational experiments

Google did a great job ofexperimenting with plate size tofigure out an optimal shape thatcould meet its target of kickingemployees back into shape (guilt andshame worked powerfully to reducethe number of trips employees madeto fill a small plate) and help themreduce their calorie intake. I haveseen such experiments to controlwastage of food during lunch breaks.At one manufacturing firm, the HRDepartment set up a Scoreboard toshow how many kilos of food waswasted the day before and so on.Obviously, such loud displays helpedcontrol the menace to an extent. Atanother place, a young engineerdecided to stick graphic photos ofpoor children dying of hunger rightnext to the serving area.

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Consequently, people got themessage and while some folksattributed their loss of appetite tothe pictures some said it made themmore sensitive about the quantity offood they loaded on their plates.Unlike the operational experimentsGoogle undertook, the examples Icite may not have been the result ofany meticulous planning, rigorousmeasurement or even continuousexperimentation. At the same time,I cannot help but point out that HRis expected to change employeebehaviour in numerous ways for avariety of reasons. That, to say theleast, is exactly what HR is expectedto do (if we hear our line managerscorrectly). Therefore, if earlier, HRdid not have the tools to study,analyze and mould employeebehaviour, thanks to Big DataAnalytics it now has a wide andbewildering array of tools that have

the potential to predict and regulateemployee behaviour, on a mass scale.

While most of the examples herepertain to food, I am hopeful thatOperational Experiments in HR willextend to other more promisingareas of employee experience as well.I remember the case of a "desi" (noHR Degree / no Strategic HRExperience) HR Head who was askedto hire a Costing Manager. Thecompany he works for has a lonemanufacturing unit outside Delhi.Once the Costing Manager was onboard, the company realized he hadno job since he needed data on workin progress - timely data on finishedand unfinished goods and inventory,almost on a daily basis. As thecompany did not have an MIS orany practice of tracking anythingremotely called " operational andproduction data", the HR Headsecured permission from the MD tocirculate chits of paper to collectsuch data from the company's 300odd employees (all semi-skilled) atthe end of each working day. Inexchange for 10 rupees every day,each employee was asked toaccurately mention on the chit thequantum of stock they were sittingon. The scheme went down well withthe workers and the Costing Managerdiscovered he had enough and moredata to occupy himself for a full year.The MD was so pleased that hedecided to increase the amount toRs 20 per day. If a desi MIS can begenerated on the fly through anoperational experiment, I am sureHR can conduct many experimentsto help businesses unlock value fromData.

C. Dashboards and

visualization

For HR Departments that continueto labour with PowerPoint and XL,software like Tableau and Sisense canappear to be the proverbial oasis ina desert formed by data. They canmake data analytics visually stunningand beautiful and for a change, evenmake business leaders fall in lovewith HR. However, these are lowhanging fruits on a long journey. Theprimary objective of an HR AnalyticsDepartment cannot be the creation

and transmission of Dashboards andData Visualization - although thesecan greatly help Line Managers toarrive at their own inferences andconclusions, especially where theydoubt HR to offer some stellarinsights.

D. HR metrics

Dashboards are made up of variouskinds of metrics. Thanks to the"proliferate or perish" treaty that HRProfessionals became signatories tosometime in the past decade, varioustypes of HR Metrics (in the order of1000s) are available today withleading ERP vendors. Someonerecently claimed they havedeveloped 3000+ HR metrics to track- now that's taking this proliferationbusiness a bit too far. Unfortunatelybusinesses don't share HR's love ofmetrics. Moreover, what irks themthe most are the totally differentways in which teams within the sameorganization measure the samemetric? Recruitment alone throws upvarious ways to measure animportant metric like "time to hire"depending on how exactly youidentify the base line. Worriedprobably by the confusing signals theHR fraternity was sending out to thebusiness community, SHRMinstituted standard ways of measuringsome common metrics like Cost ofHire and so on. However, I reallywonder how these standards can beapplied across geographies or evenindustries.

Skill-sets HR professionals of

the future will need

If Big Data Analytics is taken to itslogical conclusion by "illogical",departments (Whether, they be ITor Operations or even HR), HRprofessionals won't be around andthe best part, HR skills won't berequired. I and a senior friendfacilitated a workshop recently for agroup of finance professionals.Everything from our travel and stayonwards to getting the participantsto the venue from various regionswas seamlessly managed by theFinance team. We were personallyshocked (truth be told, we had mixedfeelings and didn't know whether to

In a career spanning 15+ years,

Sumeet Varghese has had various

avatars - initially serving as Country

Manager for the Human Capital

Institute – an HR Think-tank, and

later, as Associate Director at T.V. Rao

Learning Systems Pvt. Ltd., before

founding Your HR Buddy. He has

consulted more than 100 firms (in

India, the Middle East and South East

Asia), trained over 1000 executives

(including MDs and CEOs) and

published over 75 position papers

covering key areas of HR and OD

practice.

Sumeet Varghese

Founding Partner, Your HR Buddy

TECHNOLOGY

www.humancapitalonline.com■ August 2015 ◆ 45

laugh or cry) to not find a single HRprofessional play a role anywherefrom need identification to vendorshortlisting and screening to traineecoordination to venue booking tofeedback collection. When we left,the chaps said they have more worklined up for us - just that we wouldhave to re-title the entire interventionto avoid detection by the company'sHR Department and to preventgenerating the impression thatFinance is stepping into HR territory.Already, many traditional HRprocesses (requiring what was

traditionally termed "HR skill-sets")are either being outsourced orperhaps (in the case I recounted)taken over by line functions. Now ifthe rest (which is not much - thoughsome serious-minded folks mightgive it some meat and call it"strategy" or "business partnering"or "talent management") is to becarefully considered, automation willslowly catch up. Google'srecruitment algorithms have doneaway with the need to have a hiringmanager for some positions. At thesame time its retention algorithmshelp it to predict who is likely toleave. While I do not immediatelyforesee job-destroying algorithms toentirely replace a generation of HRprofessionals, I am hoping a newbreed of HR professionals withalgorithm-dismissing/refining skillswill be able to find their feet in theBig Data landscape. It is quitepossible, that the HR professional ofthe future will be more analytical; a

wee bit statistical, certainlyprogramming friendly as well as adomain expert having a moreintegrated view of HR.

Common "heuristics" line

managers are known to

use for hiring/firing

Across organizations of all types, youwill bump across various types ofpeople heuristics (unexaminedPeople Models) at work - rules ofthumb that may have served linemanagers well and which play a verylarge role in several people decisions

at a firm (contrary to what we HRprofessionals think). This is thehuman version of Big Data Analyticsat work, perhaps! From among thefew that I have been able to identify,I find the one involving a seniorfinance manager at a large Indianfirm, quite interesting. Thisgentleman hires juniors who meetone criterion: they should havecleared their CA (CharteredAccountancy qualifying exams) inthe 3rd or 4th attempt. The way hesees it, such candidates are ready tostretch more than those who havecleared it in the first attempt. Now,anyone who has taken the examthrice will confess that the pain ofpreparing for the exam three timesover and clearing it can beexcruciating indeed. Whether thatpreparation makes them moreindustrious and persevering (at least,in the eyes of this financeprofessional) is a matter of debatefor statisticians and behaviourists

alike. While there is no independentstudy out there that can establishwhether these three-timers are morepersevering and industrious than thefirst-timers, our finance managercontinues to operate on the basis ofthis heuristic and what is more, overtime, has been able to build andretain a team of productiveprofessionals using the same logic.

Every people heuristic is a fitsubject of research for a buddingHR Analytics professional. Armedwith statistical tools, behaviouralanalysis models and anunderstanding of how people formperceptions about groups andindividuals, an HR AnalyticsDepartment should statisticallyexamine those "notions" or"assumptions" about people thatmight be actually preventingorganizations from attracting, hiring,promoting and retaining talentedpeople.

One gentleman at a leadingtelecom company confessed using aparticular heuristic to screen outcandidates: he would ask thecandidate to share his/her contactnumbers during the interview. If thecandidate used the services of a rivaltelecom operator (as would beevident from the number he/sheprovided), he/she would bedismissed from the interview. Myfriend's logic (based obviously onyears of experience - Big DataAnalytics) for the summary rejectionis based on the idea that suchindividuals are never loyal to thebrands they work for. If they were,they would avail their company'sservices and not that of a rival. In hisscheme of things, people lied butthe numbers didn't. If our Big DataAnalytics program operates on asimilar premise: people lie butnumbers don't, we risk repeating thesame mistake that my friend fromthe telecom sector makes. You see -my friend never asks where thecandidate lives and moreimportantly, whether this place hasadequate network coverage or notor whether the area in which thecandidate usually operates has a goodnumber of telecom towers for hiscustomers or not. HC

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