HR Analytics: To a hammer, everything is a nailSpoiler alert: I’m a skeptic.
HR Analytics: What is it?HR Analytics is also known as People Analytics, Workforce Analytics or Talent Analytics. (I’m already nervous about this because the only group of people who think we can accurately quantify “talent” are statistical analysts. AND “People” by and large are unpredictable. “Analytics” is the consolidation of a lot of data to make predictions. …but people aren’t predictable…okay?…so I’m skeptical. Are you skeptical too?
Definition: CornerstoneOnDemand defines HR Analytics as gathering data on employee efficiency and using the data to make relevant decisions for improvement.
According to techopedia, HR Analytics is correlating business data and people data to improve business outcomes.
What data?… and where do they get it?Employee Engagement is a key factor in HR Analytics. Now we have to figure out what is meant by Employee Engagement. And I’m not doubly skeptical because, once again, “people engagement” is also unpredictable.
The Business Dictionary tells us that, at the heart of employee engagement is the emotional connection an employee feels toward his or her employer .
Who believes that we can measure an “emotional connection?” Lots of people, to a point. What point. Well it’s fairly easy to differentiate between someone who hates their job and someone who loves their job. But HR Analytics takes this to a new level of quantification…down to the smallest detail.
Here are some considerations that “measure” employee engagement. My comments follow the bullets.
- Does the employee “feel” mentally stimulated?
- Is there trust and communication between employees and management?
- Do employees understand how their work contributes to the company performance?
- Is there growth potential?
- Does the employee have a high sense of pride about his or her association with the company?
How do companies get this data?Did you guess? It’s from the infamous Employee Engagement Survey that companies send to all their employees. I bet you remember some of these questions.
- On a scale of 1 to 10, how happy are you at work?
- Would you refer someone to work here?
- Do you feel valued at work?
- Do you feel the management team is transparent?
- If you were given a chance, would you reapply to your current job?
- How frequently do you receive recognition from your manager?
- With eyes closed, can you recite our organization’s values?
- Do you have fun at work?
(Compliments to Sabrina Son on her article.)
- It is easy to become absorbed in my job.
- I would recommend the company as a great place to work.
- Most days, I look forward to coming to work.
- I feel like I belong here.
- I feel challenged and stretched in my job in a way that results in personal growth.
What’s my problem?
I’m sure you “feel” my skepticism, and perhaps I’m missing something, but it “seems” to me that for every question asked, it depends on the day. It depends on when the person is asked. It depends on what has happened in their recent communications with their management, their colleagues, and their family or close relationships.
Garbage in –> garbage out.
Nevertheless, the data from these responses is used to make business decisions. This sounds more like a deal with the devil as Neil Patrick so clearly describes in his post: HR data and analytics drives profits but at what cost?
Why do we need HR Analytics?Data technologists say we need HR Analytics because people can’t make good decisions. (Actually, one person said we need it because people make “stupid decisions.” (Yes, that’s a quote.)
The basic premise is that people are bias.
Bias: a preference or an inclination, especially one that inhibits impartial judgment [emphasis mine].
To get rid of bias, the HR Analytics Process does the following:
- Identify the appropriate databases where information can be obtained.
- Collect the data.
- Cleanse the data. (This could take another blog to discuss…)
- Analyze the data.
- Aggregate the data to define meaningful outcomes.
WOW…a lot of work goes into this process! All of this to remove bias so we can stop making “stupid” decisions.
Do we need or really want to get rid of bias?
Is “impartial judgment” always best? I’m not so sure. Here are a few case studies from HR Analytics “experts.” Let’s see if “bias” is removed, and whether it is helpful to do so, or not.
Case Studies:Pilots leave the company:
Question: Why were so many pilots leaving a law enforcement agency?
Process: HR Analytics.
Situation: Pilots fell into one of two groups: those who were commercially trained prior to being hired and those who were hired and then trained on the job.
Conclusion: Following the detailed HR Analytics process, it was determined that the commercial pilots left the company and returned to their former employment in the commercial airline industry because the compensation was much higher.
Really? All of that work to figure that out? Was this over-engineered?
Bias check: If the agency, moving forward, dismissed any candidate that came from the commercial airline industry, aren’t they being biased? Or are they making a smart business decision?
Ethnic Prejudice on Résumés:
Question: Did ethnic names on résumés draw a bias from hiring professionals as they evaluated candidates?
Process: HR Analytics.
Situation: A stack or résumés included “white” names and names from other ethnicities was passed around to the selection committee.
Conclusion: It was found that if “white” people reviewed the stack of resumes, they favored “white” names. It was found that people from other ethnicities did the same.
Bias check: Obviously, there are times when candidates are overlooked for the wrong reasons and less capable candidates are selected and candidates that are more qualified are dismissed. However, are there times when a less qualified candidate should be selected?
Example: Would we expect the National Black MBA Association (NBMBAA) to choose a non-black candidate as the Director of Marketing because his or her MBA was from a more prestigious university? Or perhaps the non-black candidate had 12 years of experience as compared to 8 years by a black candidate.
If the selection excluded people they thought were white (and looked it on LinkedIn?), aren’t they being biased?
Can we escape Bias? Should we? Do we want to?
Is it fair to say that there are consequences to making the decisions “by the numbers” when it comes to people? Did you know that HR Analytics doesn’t belong in HR?
Uh huh. A particular company made the case that for the Analytics process to be interpreted accurately, the “interpreters” should be trained in analytics. And HR people are fluffy, shoot-from-the-hip types and not capable of this kind of analysis. …of course that company also offers courses in HR Analytics for …wait for it… those fluffy, shoot-from-the-hip HR professionals.
I’m still skeptical…and now I’m even cynical… and concerned
When I started this blog I was definitely skeptical. Now that I’ve spent a few hours with the material and wrestled with it, paced up and down in my living room, and walked four miles with my pooch, I’m cynical as well.
Skeptical: doubtful.Skeptical: first of all, people are fickle, inconsistent, and unpredictable. If a company is committed to finding the right candidate, then they will need to spend some time to actually get to know their top candidates. (Oh dear, wouldn’t that be costly? As costly as implementing an HR Analytics program?)
Cynical: distrusting the motives.
Concerned: disturbed, troubled.
Cynical: Am I cynical in thinking that companies really want their employees to enjoy coming to work? (Aw…that’s sweet.)
Is it that the leadership wants higher revenue and this is one way they think they can get it?
Am I cynical in thinking that this approach is disingenuous? Does motive matter? Is it possible that some people, perhaps many, hear the right words, (e.g. “Morgan, your work here is valued!”), and if the words feel disingenuous, or manipulative, that Morgan will not perform at peak.
Concerned: The marketing glitz surrounding this movement is like a loose tiger in a playground of toddlers. The marketing glitz is compelling companies to make many or most decisions based on data-analysis, predictive analytics, machine learning and Artificial Intelligence. At its core, it is inherently bias! The toddlers are smiling and saying, “Here kitty-kitty.”
NOTE: I use the term “toddlers” because machine learning and Artificial Intelligence are in their infancy and therefore, so are its users. Should I have used the term “infants?”
This does sounds more like a deal with the devil. Check out Neil Patricks blog, here it is again: HR data and analytics drives profits but at what cost?
To a hammer, everything is a nail.To an Analytics engineer, everything can be solved by analytics.
What do you think?
HR Analytics: articles of interest
Engage Employees By Actually Listening To Them by James E Smith
HR Analytics should not be located in HR …wrong skill sets there.
Engagement Survey Blogs:
Misconceptions about Employee Engagement Surveys
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