By Steve Blais

Let’s face it, the majority of HR departments simply record and store employee data.  What else is there to do with the information?  HR departments have to answer questions about the employees – how long have they worked for the company, when were they first hired, how much leave do they have on the books, when was their last pay increase, what proficiency levels have they achieved, and so forth.  Since the typical company does not employ tens of thousands of employees at one time, searching a database of employees or even going through paper files is not that onerous.

But there are larger questions:  questions that are asked about employees in aggregate that may require views of all employees over time which means that a company that has 500 employees at any one time but has been in existence for 150 years now has a large amount of data on hand in some format.

The typical questions HR is called on to answer today include:

  • How long does it take to find the right new hire?
  • What is our current employee turnover rate?
  • Which employees are eligible for a promotion to that specific position?
  • How should we handle an employee discrimination or harassment complaint?

But what if we could go a step further and answer some really /more significant questions that would help the organization’s mission? The important factor in all of the benefits that can accrue from a data analytics program for HR is the initiating question.  Many questions might flow naturally but have been suppressed over time because there is no way to obtain answers to the questions.  They become rhetorical in nature and the curious person with the question realizes that the answer to the question might be extremely beneficial, but in reality, there is no way to get that answer.  For the most part, however, with today’s analytics there is a way to get those answers, and what we need to do is to bring back those questions.

Questions like:

  • Of those employees who left, how many might be considered regretted loss?
  • Which employees are most likely to leave within the next year?
  • Where can we find the best candidates to fit our staffing requests?
  • How can we speed up the recruitment and hiring process?
  • What constitutes the most likely candidate profile for success in our software development department?
  • Which of our new hires will become top performers in the next two years? And, conversely, which of our new hires will move on to other companies after two years, or be asked to leave?
  • Can you relate time connected remotely to the organization (from home during the pandemic) to productivity – either increase or decrease?
  • How can you monitor staff productivity while working from home without violating privacy policies?

Many of these and other questions asked by and of HR require access to multiple sources of data both within and outside of the organization.  For example, to determine whether employee engagement actually increases productivity, one would need to access and analyze the employee engagement surveys and the financial productivity statistics, likely located in two widely divergent areas of the organization’s databases.

What areas will these questions come from?

  • Measuring performance
  • Assisting management in making promotion and salary decisions
  • Getting a better handle on attrition and increasing retention and organizational loyalty
  • Analyzing employee engagement
  • Accurately measuring staff development and learning results especially against investment

In other words, HR already has much of the information to answer many of the questions being put to it, including the media enflamed issue of the so-called “Great Resignation”.

The “Great Resignation”

If, for example, you addressed retention through a bonus system and other financial rewards, you might find that the program is not working sufficiently.  Applying data analytics might expose trends such as employees who are on small teams are less likely to leave, or those working for poor managers are more likely to seek employment elsewhere.

How does this work?  Someone from HR defines what factors constitute success for individuals – the success that would normally earn a bonus or financial reward.  Then the data scientist creates an algorithm to search through all the data available on employee performance to determine which employees demonstrate that level of success and then filter through all the factors that affect the employee distinguishing between those employees who stay in the organization and those who leave.  The end result is a list of the factors, such as working in small teams or a clearer technical advancement path that contribute to an employee’s retention.  HR can then use that information to make changes in the organization to support those factors that keep good employees around and reduce the factors that drive good employees away.

Suppose you wanted to know what factors to emphasize at the organization to attract potential new staff to join.  A similar algorithm could be created to use data extracted from social media (Facebook, Twitter, and especially LinkedIn) to analyze the comments that people make about the organizations they are working for and their feelings about that organization – whether they are planning to leave or not.  This effort could give you a list of factors for which potential employees are really searching.

Data analytics, artificial intelligence, machine learning and the rest of the data-based technologies are not really magic even though the tools seem to provide information that was not available before.  The technologies seem to magically have answers to questions that we previously did not believe we could answer.

So, the secret underlying successful application of analytics in HR is simple:  ask more questions.  Be more curious.  You have the information available and means to mine that information for just about any answer you need.  Now all you need are the questions.