Commentary: Tricky human resource questions best dealt with facts instead of feelings
Human Resource practitioners should use data to seek buy-in from company leaders and employees alike, says one observer from the National Council of Social Service.
SINGAPORE: Nobel prize-winning economist Ronald Coase once said:
If you torture the data long enough, it will confess!
This is a useful starting point for which to discuss the use of data. I find the use of data in human resources (HR) to have an especially huge potential.
The organisations that will win the war for talent, especially in manpower-lean economies like Singapore, will be those which are better at identifying and keeping key talent, motivating high performance, developing and promoting staff and predicting future people needs accurately.
But the well-worn phrases organisations have that “people are our greatest assets” and “HR is our ultimate competitive advantage” will finally ring true when HR focuses on solving business problems as opposed to people problems.
HR MUST USE FACTS TO SOLVE BUSINESS PROBLEMS
No professional entering the HR field can expect to succeed in his or her career without a solid mastery of how data can help organisations make recruitment, retention and progression decisions based on facts.
Linking pay-for-performance has been a dogma of management, but recent research shows that most incentive plans do not produce the desired behavior, and incentive pay models, in fact, have little correlation with future business results.
So it is imperative that HR provide data-driven answers and insights on how to manage and incentivise people in the organisation.
But HR analytics should not be left only to data scientists. Each HR practitioner needs to be armed with practical, hands-on approaches to connect HR policies and practices to business performance, which include the ability to:
- Review key statistical and financial concepts in a way that can track performance and link it to company profits, financial returns, efficiency, and descriptive or predictive statistics.
- Understand and apply basic concepts including data analytics, data management and modelling. Key among these is hypotheses building and testing.
- Have a good grasp of how data is to be collected, prepared for analysis and stored so it can be used with various commonly found analytics tools.
- Model HR questions on available data in areas including workforce planning, recruiting, training, career planning, pay and turnover rates.
HR professionals need to embrace analytics, and speak its business language, if they are truly to get the much coveted “seat at the table” among the company’s key decision makers.
Most business leaders speak in numbers, financial and otherwise. If HR wants to be heard, it needs to be able to put HR arguments in business language - meaning using data that links HR decisions to business outcomes.
Otherwise, discussions about HR matters becomes one’s “gut” versus somebody else’s “gut”. The worst outcome is that business leaders ignore the manpower recommendations HR offers, thinking that they know better.
Many HR professionals harbour a fear that “I am not a numbers person; how can I do analytics?” While it is true that basic knowledge of finance and statistics is required, analytics is a skill, not a talent, and can be learnt.
Interpreting the data is just as important, including what stories the numbers tell.
The HR professional that has a handle on analytics is better positioned to answer business questions from top management, for instance, what the profile of a company’s sales force that will best help it to increase sales revenue growth looks like.
He or she is also able to use HR analytical tools to improve employment outcomes for employees – for instance if he or she can evaluate which combination of employee benefits and work-life balance programmes delivers the highest staff engagement.
WHEN FACTS HELPED NAVIGATE A TRICKY ISSUE
A company was looking to determine which profile of sales force made most sense in its growth strategy. HR interviewed several sales managers and discovered each had their own preferences, and there was no consensus, nor evidence, that any of these approaches worked better.
The HR team collected data on sales results for each of the sales people in the last three years, and combined it with information from other internal systems. After months of analysis they had a great deal of insights and numbers to show.
They began their presentation on the results with a handful of slides which basically started with" “Do you want to increase sales results by X% over the next 12 to 18 months? If you do, you need to change the profile of your sales force so that it matches these attributes A, B, C and D.”
They expected pushback from veteran sales managers who had “always done it this way”. To their surprise, there were no questions whatsoever.
After a long silence, the CEO said, “Ok, it’s agreed, let’s do this!”, and moved on to the next topic on the agenda. After more than a year, the team went to evaluate the results and discovered that those regions that had changed the sales force to the indicated profile saw even better sales results than estimated.
So it seems starting with the right business question, as opposed to starting with the data available, was the key in this instance.
In fact, the most common misunderstanding about analytics is that, if you look at data hard enough, and take time to “torture it”, you will find insights.
Fermin Diez Is deputy CEO and group director, human capital and organisational development at the National Council for Social Service.