The "People Lie, Numbers Don't" Approach to HR Analytics SUMEET VARGHESE, SPHR, SHRM-SCP

A few workshops and a couple of speaking engagements later, I find it immoral not to update what I have written earlier on the marriage between HR Analytics and Big Data.

So here goes:

1. What is the future of Datafication in HR? Does HR already have a lot of Data?

The idea that Data (of the wide and "wild" variety) is required to run any form of Analytics (Big or Small) has not really caught on with "some" HR professionals, atleast the ones I have spoken to. When I say "some" you may take it to mean "many", since I always keep statistical sampling requirements in mind whenever I strike a conversation with anyone in HR (a "few' therefore might just represent the "many" out there, If I am right about my sampling). This means that when I speak to you, I always consider you first as part of an important data-set - either by virtue of your title, your experience in the HR function or your HR caste/"gotra" (XLRI, TISS, IIM and so on). I know you may not like this statistical approach to meeting and conversing with people selectively and treating them as nothing more than "samples", but trust me, this approach is not as demeaning as the kind of classificatory or analytical exercises you perform by asking me which state I am from, which religion I practice or whether I love Modi as much as you do or not. You see we are always collecting data from each other, whether others like it or not, but what we do with that data in HR is something I will reserve for another post some other day. For the moment though, let me clarify: HR Departments are caught in a position where they may have too much of data and very poor analytics capabilities to leverage the data or strong analytics capabilities but hardly any worthwhile data to mine for insights. Frankly, I have no clue which one is better!Anyways, the sad/good news is that we need to sort out many serious data related issues before we can discuss the extent to which our neighbors in Marketing, Customer Service, Finance and Operations use Big Data Analytics:

2. Is the Data we capture of any value at all? If it is not valuable today, will it be valuable tomorrow?

Unfortunately, no one has an answer. In one case, I asked a group of HR professionals whether we should track the number of loo breaks that senior executives took during a workshop and whether it would serve any purpose. Obviously, there were blank stares. My question of course wasn't pointless. The loo like the office water cooler and the cofee/tea vending machine points in an organization is as much a space for exchanging workshop feedback as it is for updating each other on some juicy company gossip. While this may be a small unwanted detail to be avoided by the HR professional it might certainly be of interest to a Data Scientist, assuming it can offer some interesting clues - Elementary, my dear HR Professional, Elementary! Strangely, with a little bit of luck I was able to work out a correlation (not causation) between the pathetic condition of the loos used by the top management at one firm and the MD's constant refrain that the organization lacked "ownership". In fact, nobody (and this included the organization's top brass) bothered to complain about the stink because they thought it wasn't "their job".

3. Should the Data we capture be of the same type or of different types? Aren't we folks more qualitative rather than quantitative?

Frankly, HR professionals must closely study the kind of work being done by their Marketing and Customer Service Analytics teams to figure out that Big Data Tools have evolved to a point where they rarely ever care a byte if your data is structured and/or unstructured. So, if you have an employee's leave records in XL, the poor unsuspecting chap's FB Posts and Tweets for a full year in Word, the person's performance appraisal history in PDF, and his/her Compensation Data in any format that your ERP spits out, some meaningful analytics can still be derived even if this employee record is a gibberish amalgamation of data. For instance some recruiters have long studied behavior patterns of candidates before, during and after the various stages of a screening process that they have been subjected to. Thanks to these studies we now know that if you handle a particular stage of the recruitment process poorly, candidates are two times or three times more likely to badmouth the company's products and services on social media. Obviously, in this case process feedback (quantitative and qualitative data) at each stage of the screening cycle has been correlated with social media behavior (qualitative data) of the candidates.

4. Why should we capture Data?

Asking this question (considering the order in which it appears) is a bit like placing the cart before the horse (line managers will vouch this is precisely the image of HR they have). Unfortunately, many of us in the HR fraternity have fallen in love with the practice of hunting for data only when businesses want it. Also, quite understandably, businesses don't explain why they want the data? They know they can do the analytics themselves because we haven't been able to figure out why businesses ask for specific types of data in the first place. At some workshops, consultants have asked me why clients look for specific data to be pulled out from either an ERP and/or freely floating electronic and physical folders containing all types of files (some including data captured on papyrus - I am mildly exaggerating, here). My answer: that's just their way of checking whether we are busy or not.

5. What kind of new/niche Analytics are we going to see in the near or far future?

A. People Models

We all know Google has done some heavy number crunching over quite so many years to figure out 8 attributes they would love to see in Googlers who manage other Googlers (please do read the HBS Case Study: Google's Project Oxygen) and that this model feeds their recruitment and succession planning processes. Its quite possible this people model might be revised in the next 10 years as new challenges emerge with the business and the model (not necessarily in that order). I remember studying Van der Waal's equation in school - the final derivation of the equation, which obviously had more variables than the one initially proposed, was developed to fit the "reality" out there because tests/experiments revealed the equation had not quite nailed it. If People Models are "work in progress", People Analytics Departments can rub shoulders with their scientific peers - if not, such models run the risk of being exposed by a Copernican revolution (which obviously would happen on the business side first!). We do know for a fact that the famed/notorious 25 layered (rounds) screening process (possibly, state-of-the-art at that point in time) at Google gave way to a 4 layered (rounds) screening process partly because business managers wanted "good" people in "quickly". I am assuming, Van der Waal was under no such pressure.

B. Operational Experiments

Google did a great job of experimenting with plate size to figure out an optimal shape that could meet its target of kicking employees back into shape (guilt and shame worked powerfully to reduce the number of trips employees made to fill a small plate) and help them reduce their calorie intake. I have seen such experiments to control wastage of food during lunch breaks. At one manufacturing firm, the HR Department set up a Scoreboard to show how many kilos of food was wasted the day before and so on. Obviously, such loud displays helped control the menace to an extent. At another place, a young engineer decided to stick graphic photos of poor children dying of hunger right next to the serving area. Consequently, people got the message and while some folks attributed their loss of appetite to the pictures some said it made them more sensitive about the quantity of food they loaded on their plates. Unlike the operational experiments Google undertook, the examples I cite may not have been the result of any meticulous planning, rigorous measurement or even continuous experimentation. At the same time, I cannot help but point out that HR is expected to change employee behavior in numerous ways for a variety of reasons. That, to say the least, is exactly what HR is expected to do (if we hear our Line Managers correctly). Therefore, If earlier, HR did not have the tools to study, analyze and mold employee behavior, thanks to Big Data Analytics it now has a wide and bewildering array of tools that have the potential to predict and regulate employee behavior, on a mass scale.

While most of the examples here pertain to food, I am hopeful that Operational Experiments in HR will extend to other more promising areas of employee experience as well. I remember the case of a "desi" (no HR Degree / no Strategic HR Experience) HR Head who was asked to hire a Costing Manager. The company he works for has a lone manufacturing unit outside Delhi. Once the Costing Manager was on board, the company realized he had no job since he needed data on work in progress - timely data on finished and unfinished goods and inventory, almost on a daily basis. As the company did not have an MIS or any practice of tracking anything remotely called " operational and production data", the HR Head secured permission from the MD to circulate chits of paper to collect such data from the company's 300 odd employees (all semi-skilled) at the end of each working day. In exchange for 10 rupees every day, each employee was asked to accurately mention on the chit the quantum of stock they were sitting on. The scheme went down well with the workers and the Costing Manager discovered he had enough and more data to occupy himself for a full year. The MD was so pleased, he decided to increase the amount to Rs. 20 per day. If a Desi MIS can be generated on the fly through an operational experiment, I am sure HR can conduct many experiments to help businesses unlock value from Data.

C. Dashboards and Visualization

For HR Departments that continue to labor with PowerPoint and XL, software like Tableau and Sisense (not that I am in love with these) can appear to be the proverbial oasis in a desert formed by data. They can make data analytics visually stunning and beautiful and for a change, even make business leaders fall in love with HR. However, these are low hanging fruits on a long journey. The primary objective of an HR Analytics Department cannot be the creation and transmission of Dashboards and Data Visualization - although these can greatly help Line Managers to arrive at their own inferences and conclusions, especially where they doubt HR to offer some stellar insights.

D. HR Metrics

Dashboards are made up of various kinds of metrics. Thanks to the "proliferate or perish" treaty that HR Professionals became signatories to sometime in the past decade, various types of HR Metrics (in the order of 1000s) are available today with leading ERP vendors. Someone recently claimed they have developed 3000+ HR metrics to track - now that's taking this proliferation business a bit too far. Unfortunately businesses don't share HR's love of metrics. Moreover, what irks them the most are the totally different ways in which teams within the same organization measure the same metric. Recruitment alone throws up various ways to measure an important metric like "time to hire" depending on how exactly you identify the base line. Worried probably by the confusing signals the HR fraternity was sending out to the business community, SHRM instituted standard ways of measuring some common metrics like Cost of Hire and so on. However, I really wonder how these standards can be applied across geographies or even industries.

6. What kind of skill-sets will HR professionals of the future need thanks to Big Data?

If Big Data Analytics is taken to its logical conclusion by "illogical" (I'll explain this in a while) Departments (Whether, they be IT or Operations or even HR), HR professionals won't be around and the best part, HR skills won't be required. I and a senior friend facilitated a workshop recently for a group of finance professionals. Everything from our travel and stay onwards to getting the participants to the venue from various regions was seamlessly managed by the Finance team. We were personally shocked (truth be told, we had mixed feelings and didn't know whether to laugh or cry) to not find a single HR professional play a role anywhere from need identification to vendor shortlisting and screening to trainee coordination to venue booking to feedback collection. When we left, the chaps said they have more work lined up for us - just that we would have to re-title the entire intervention to avoid detection by the company's HR Department and to prevent generating the impression that Finance is stepping into HR territory. Already, many traditional HR processes (requiring what was traditionally termed "HR skill-sets") are either being outsourced or perhaps (in the case I recounted) taken over by line functions. Now if the rest (which is not much - though some serious-minded folks might give it some meat and call it "strategy" or "business partnering" or "talent management") is to be carefully considered, automation will slowly catch up. Google's recruitment algorithms have done away with the need to have a hiring manager for some positions. At the same time its retention algorithms help it to predict who is likely to leave. While I do not immediately foresee job-destroying algorithms to entirely replace a generation of HR professionals, I am hoping a new breed of HR professionals with algorithm-dismissing/refining skills will be able to find their feet in the Big Data landscape. It is quite possible, that the HR Professional of the future will be more analytical, a wee bit statistical, certainly programming friendly as well as a domain expert having a more integrated view of HR.

7. Whither Psychometric Testing?

Some time back a well-known Psychometric Testing company that offers various types of IT platforms to conduct millions of tests online came up with an exceptional (and I must add "sensational") claim about the poor quality of sales "talent" in leading B-Schools in India. They claimed they had used tests that were rigorously developed. As an old hand at psychometric testing, I knew more than to believe such claims. Interestingly, companies the world over are looking to IT to bring them platforms that "shorten" the process of screening thousands. I don't care, a gentleman told me once, what you do with my people, as long as there are numbers and reports. I am hopeful Big Data Analytics will take on these charlatans and their acolytes in interesting ways. There is already encouraging research to show that your FB behavior, if analyzed well, can successfully predict your Big 5 personality traits - so the good news is we may not need psychometric tests in the future. But the sad news is our data footprints will be stored somewhere to be analyzed someday. I won't be surprised if my Google Calendar and Map are combined and analyzed someday (they already have, by the way) to tell you how I handle projects (how many meetings), travel, stay, and money (based on bookings data probably integrated from some other source). However, I must concede that those data-sets along with others should provide more data points to take a judicious people decision than simple answers to a set of questions hosted on an IT platform designed by an IT team that hasn't the faintest understanding of scoring and interpretation.

8. What are some common "heuristics" Line Managers are known to use for hiring/firing and everything in between?

Across organizations of all types, you will bump across various types of people heuristics (unexamined People Models) at work - rules of thumb that may have served line managers well and which play a very large role in several people decisions at a firm (contrary to what we HR professionals think). This is the human version of Big Data Analytics at work, perhaps! From among the few that I have been able to identify, I find the one involving a senior finance manager at a large Indian firm, quite interesting. This gentleman hires juniors who meet one criteria: they should have cleared their CA (Chartered Accountancy qualifying exams) in the 3rd or 4th attempt. The way he sees it, such candidates are ready to stretch more than those who have cleared it in the first attempt. Now, anyone who has taken the exam thrice will confess that the pain of preparing for the exam three times over and clearing it can be excruciating indeed. Whether that preparation makes them more industrious and persevering (atleast, in the eyes of this finance professional) is a matter of debate for statisticians and behaviorists alike. While there is no independent study out there that can establish whether these 3-timers are more persevering and industrious than the first-timers, our finance manager continues to operate on the basis of this heuristic and what is more, over time, has been able to build and retain a team of productive professionals using the same logic.

9. How can HR Analytics explore such "heuristics" that drive people decisions in a firm?

Every people heuristic is a fit subject of research for a budding HR Analytics professional. Armed with statistical tools, behavioral analysis models and an understanding of how people form perceptions about groups and individuals, an HR Analytics Department should statistically examine those "notions" or "assumptions" about people that might be actually preventing organizations from attracting, hiring, promoting and retaining talented people.One gentleman at a leading telecom company confessed using a particular heuristic to screen out candidates: he would ask the candidate to share his/her contact numbers during the interview. If the candidate used the services of a rival telecom operator (as would be evident from the number he/she provided), he/she would be dismissed from the interview. My friend's logic (based obviously on years of experience - Big Data Analytics) for the summary rejection is based on the idea that such individuals are never loyal to the brands they work for. If they were, they would avail their company's services and not that of a rival. In his scheme of things, people lied but the numbers didn't. If our Big Data Analytics program operates on a similar premise: people lie but numbers don't, we risk repeating the same mistake that my friend from the telecom sector makes. You see - my friend never asks where the candidate lives and more importantly, whether this place has adequate network coverage or not or whether the area in which the candidate usually operates has a good number of telecom towers for his customers or not.

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