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Predictive Analytics in HR

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Nikhil Abhishek Mishra - Freelance Strategic HR Consultant, Noida.

26-Oct-2020

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The new branch of management which has been touched by the data is Human Resource Department. And the term used for the usage of data in decision making is called HR Analytics. The curious minds will now ask this: what is HR Analytics and how HR Analytics will help in decision making?

With the onset of the data age we have already witnessed the role of data in every aspect of human life that we lead in a modern age. The iconic term “DATA IS THE NEW OIL” is a self explanatory statement put out by industry leaders to show the importance of data in the day to day activities/decision making of an organization.

This shows why most off the leaders are always hungry for data to figure out a way to take business decisions. When huge amount of money is at stake the managers and decision makers are hungry for more relevant data to take right decisions at the right time. Every aspect of business has been influenced by data in decision making.

The new branch of management which has been touched by the data is Human Resource Department. And the term used for the usage of data in decision making is called HR Analytics. The curious minds will now ask this: what is HR Analytics and how HR Analytics will help in decision making?

HR analytics is the application of statistical modeling on the data collected by the HR department of an organization from an employee and analyzing the data for employee-related factors. It presents the outcome in a simplified form to improve the decision making of the concerned leaders in regards to various aspects of business to propel the business growth.

Some of the basic questions that are always looked for are as follows:

  • What is the churn ratio of organization?
  • What is the ROI ratio per employee?
  • Which employee is most likely to leave within a year and what is the cost of employee turnover?
  • What are the differences in parameters of an employee who left within a year as compared to the employees who are working for more than one year?

Now, these are some of the basic questions that can be found out by yearly survey and a basic interview as well to find out where is the impact? The real impact of analytics comes when we do it in real time and to do that in real time we need technology because no matter how much amount of HRBP’s (Human Resource Business Partner) time (40% of the HRBP’s time is spent in collecting+ analyzing+ feedback) is devoted in this process. There are certain limitations to this process that you cannot just do away with like collecting and collating the entire set of data painfully collected over a period of time which is usually done in the form of annual survey.

The foremost being data quality, which is the biggest hurdle in the path of implementing any analytics project. For example in a survey conducted by NTMN in 2017 it was found that around 65% reported that data quality hinders the project success which means they have faced the quality issue  in their projects. This even gets clear because in another survey in which only 9% of the senior executives showed confidence in their HR data (Rosslyn, 2017).

Another hurdle is that of a Data Integration, The survey conducted by NTMN in 2016, 77% of the respondents said that the dispersion of data in different HR systems is their main problem. In the year 2017 the situation has changed when in the survey conducted by Bersin 69% of the companies said they are in the process of integrating their data.

The impact of the analytics on Human Capital is felt on the workforce but also on the financial statements as well. The findings of the survey conducted by the different marketing research teams on HR analytics are as follows:

  1. On every dollar spent by on analytics, the return is of 13.01 dollar (Nucleus Research, 2014).
  2. 81% of the organizations which have used HR analytics for at least 1 project say they have seen its impact in their business growth (Bright, 2016).

Looking at some of the industry data some key aspects are clearly visible like annual regrettable attrition is $49 Mil., which leads to backfill whose cost, goes up by $ 4000/hire. This shows the level of disengagement that is there in the industry and the total cost of this pegs around $605 bn./year.

Some of the Traditional HR mindset may say that to increase their employee engagement they conduct annual surveys as well as they really put forward a perspective towards human to human interaction where they are building employee trust and giving a one to one attention towards their problems.

To all those business leaders who think they are doing it correctly let this sink in their traditional mindset, as per HBR (Harvard Business Review) report, employees may think that annual surveys are inhuman, boring and questions are repetitive which have no resemblance to the real situation they are facing in the real world of work. Imagine that after spending 5 plus years in an organization, an employee still gets a question asking does he has a best friend in this organization? Don’t we think this question is a little out of place or in a bad taste? On top of this there are other factors like people dynamics and other such personal rapport which again filter out the kind of insight due to dangers of anonymity that can come from these surveys. Even if they come through these methods it will take a lot of time to find these problems or insight and that will at the end of the year when the employee would have already moved on. This in turn will make the entire exercise futile.

The answer to these problems is given by predictive analytics by gathering the data from the employees in real time, giving the mood of the entire organization in a real time data gives us a organization wide Life temperature check, changes in attitude of the employees over a period of time can be mapped effectively. To save the costs the intervention must be made at the earliest to get optimum results.

For this to happen they must gather data and analyze them in a single day. A responsive mechanism  can only be built through artificial intelligence or machine learning can be used which drives them in a guided manner to get the responses from the employees in a customized manner and not repetitive questions which do not serve any purpose. Only then can the actual employee engagement can be achieved.