HOW HR CAN LEVERAGE ON DATA ANALYTICS TO DERIVE BUSINESS VALUE
The business environment is constantly changing and today there are current business trends which include digitalization, block-chain, crypto-currency, data analytics, internet based start-ups and artificial intelligence. These days, data and information has undeniably become a key source of competitive edge in the business world. He who has knowledge has power, those with data and ability to access and quickly interpret it are able to outsmart their competition to nothing.
Likewise, the HR discipline is always evolving and moving along with times. This on its own has an impact on HR strategies and the organisation of work. Previously HR used to be called personnel management and HR was largely an operational role. In the 1990s and part of the new millennium, we got to the strategic period. Right now we are talking of digital and data-driven HR!
What is HR analytics?
Human Resource analytics involves the use of historical data to guide in determining behaviour patterns, analyse trends and make assessments that relate to key decision making in people management. Such knowledge and data is aimed at enhancing the efficiency and effectiveness of the HR function and ensure that HR generates business value. Associated with such a field are terms such as HR metrics, predictive analysis, correlation, causation, surveys and indexes, measures of central tendency and graphs.
What is data-driven HR Management?
Data driven Human Resource management is an approach to HR decision making that involves the use of making decisions from the foundation of verifiable data. This is a key component of modern day management as it ensures that HR can quantify and measure its efforts and make sound decisions through the use of empirical evidence. We have quite a number of issues that we can track in HR such as employee turnover rates, quality of hire, time to fill a vacancy, profit per employee, headcount levels and quality of training. Once HR is able to assess reasons why employees are leaving through the use of statistical data, sound decisions can be made.
The value of data-driven HR and analytics
Assists in determine trends and patterns of behaviour
The use of HR analytics and data-driven HR enables HR practitioners to diagnose problems as well as determine trends of behaviour that relate to people. HR would be able to assess on the reason why people are leaving the organisation, attract the best talent, determine its employee engagement index patterns, calculate the Return on Investment (ROI) from HR interventions and being able to predict future trends.
Solving complex problems
There is quiet a large number of complex and sophisticated problems that HR historically found it difficult to explain and express. Most of these issues were traditionally regarded as difficult for HR to justify. These include employee engagement levels, organisational climate, conflict levels and employer brand. However with HR analytics such sacred areas can be documented and quantified so as to easily track and manage them. With HR analytics, HR has generally made strides in strategic alignment between HR management and business goals.
Make the right decision
HR used to make decisions basing on gut-feeling. HR could not justify the value that was being got from its interventions. In the end HR was seen as a cost centre to the business which was not deriving any meaningful value to businesses. Through the use of data analytics, HR is able to make the right decision as its efforts will be based on empirical evidence.
Swift and accurate decision making in as far as people are concerned
People analytics help HR practitioners in making swift and accurate decisions as they are able to quickly troubleshoot HR issues and make the right the decision. For example, with people analytics, HR is able to accurately forecast its manpower needs and know when to resource new employees. Through people analytics, HR is able to assess employee competencies and suggest possible talent development interventions.
Infusing objectivity
The use of HR analytics will definitely enable HR to be able to enhance objectivity in key decisions such as hiring costs, the cost of industrial relations, assessing employee potential and employee positioning. This is because the use of metrics and quantifiable data will trigger decision makers with the ability to remove the subjectivity that is often involved with HR issues such as assessing employee attitude levels and candidate suitability during interviews.
What HR needs to do
Efficient data management
I recommend that HR practitioners should invest more in their data management systems. They (HR practitioners) must ensure that their information is stored in electronic format for easy retrieval and analysis. They (HR practitioners) must record their activities and take due diligence to ensure that their data is clean and accurate. A data management software called Pangolin Information Management System is worth considering.
Information must reliable and credible
For HR to make enhance reliability and credibility of data, HR should take efforts in recording and inputting accurate and timeous data. This would aid in assisting HR with making sound decisions that are credible and more accurate.
Invest in the services of data scientists
The other option that can be available for HR is to seek the services of qualified and competent hard core data scientists. These would assist HR with analysing and interpreting data so they (HR) can make the right decisions.
HR should have Training and development courses on data science and data analytics.
HR should invest more in the training and development courses that relate to data analytics and data science. There are many online free and paid sites that offer HR analytics material. Locally in Zimbabwe, the Chinhoyi University of Technology now offers a Master of Science degree in Data Analytics. HR can thus leverage on that and ensure that its people are equipped with the right skills to deal with such huge volumes of data.
Simple steps to carry out HR analytics.
On this part, I shall discuss with you simple key systematic steps that I tried out and found them working.
The first step is to should classify your data and tabulate it. I have found Microsoft excel sheets working well with my general practice as an HR practitioner during my years of experience.
We shall use a case example of recruitment and selection which I have tabulated below in a data set.
|
Recruiting manager |
Job Position |
Job reference code |
Number of panel members |
Date when position became vacant |
Date position advertised |
Date interview held |
Date background checks held |
Date Offer accepted |
|
D. Orange |
HR Officer |
HR012A |
3 |
2-Jan-18 |
5-Jan-18 |
10-Jan-18 |
15-Jan-18 |
15-Jan-18 |
|
V. Potatoes |
Marketing Intern |
MKI10A |
4 |
5-Dec-17 |
4-Nov-17 |
12-Nov-18 |
20-Nov-18 |
2-Dec-17 |
|
D Orange |
Operations Officer |
OPS23B |
3 |
17-Feb-18 |
28-Feb-18 |
5-Mar-18 |
10-Mar-18 |
12-Mar-18 |
|
V. Potatoes |
Operations Director |
OPD14C |
4 |
18-Mar-18 |
10-Apr-18 |
16-Apr-18 |
24-Apr-18 |
28-Apr-18 |
We have categorised our data basing on the recruiting manager, number of panel members, date when the position became vacant, date when the position was advertised, date when the interview was held, date when the offer was accepted. We would then want to calculate the number of days to fill the vacant.
The formula for that is the date when the position was advertised and the date when the offer was accepted. Let us calculate the number of days to fill the position: For HR012A it is 10. MKI10A it is 28, for OPS23B it is 12 and for OPD14C, it is 18. Such data can also assist us with assessing the recruiting manager who takes the most time to fill a position between D. Orange and V. Potatoes.
The second step that we can probably focus on is creating graphs and charts to describe the data that we have. Once you make something visual, it becomes much easier and attractive to analyse and pick out trends. I shall do this by drawing a few charts and graphs below.
The next step will be to describe trends a basing on the statistical data shown on the tables, charts and tables. Using our case example, we can describe them as follows: The number of panel members for the interviews was 3 and 4. The average number of days to fill a position for V. Potatoes was 23 days whilst that of D. Orange was 11 days. V. Potatoes took 12 days more than his workmate to fill in positions under his section. The average number of days to fill a position for the whole organisation currently stands at 17 days.
The next step shall be to explain (and predict) trends and make a decision. V. Potatoes was taking longer to fill positions under his section as a result of failing to work efficiently. The business therefore tailor-made a staff development program entitled, ‘Speed’ to equip him with the requisite skills and competencies to operate with speed and efficiency. We therefore project that after the training course he shall be able to reduce his average number of days to fill a vacancy by 34.78% (from 23 days to 15 days) within the next two months. We have also requested him to seek guidance and support from V. Orange on how to be more efficient in as far as time to fill recruitment is concerned.
Conclusion
This paper represents independent thinking and has opened up a research question for my friends in academia. To my friends in the industry, this paper should assist you in triggering your minds! Do not copy paste what is written on this paper without placing your thoughts on it. Think independently and draw lessons from this paper! Thank you very much. Please do not forget to share this article for the benefit of others.
The writer is called Farai Mugabe. He holds a Master’s degree in Strategy and a BSc. Human Resource Management (Honours) degree. He is a top graduate from Midlands State University and is currently working for a global telecoms firm as an HR practitioner. Farai was awarded two coveted academic awards by Midlands State University and these are, The MSU Book Prize and The T & H award for the best male student in the HR Management Department