May Leng Kwok, Regional Head at the CIPD Asia shared that “By providing data-driven insights, People Analytics can aid in managerial decisions by helping identify drivers of engagement, retention, performance, and wellbeing, among others. Companies can use the insights to customise interventions for different employee groups to optimise organisational performance.”
Helping decision making
People Analytics’ growing popularity cannot be ignored. A report by Corporate Research Forum
states that 69 per cent of organisations with 10,000 or more employees have a People Analytics team.
This popularity is attributed to People Analytics’ ability to inform managerial decisions. In the past, People Analytics has also helped companies identify high performing talents and teams and duplicate their success. For example, Google’s Project Oxygen
analysed the practices of the company’s best performing managers and used the same to coach low-performing employees.
May adds that People Analytics is also helping companies recognise social issues existing in the current climate – the need for better policies relating to employee mental and emotional wellbeing, gender pay equity, and diversity – aiding them to make decisions that meet these demands. “It has become more important to use efficient People Analytics processes to monitor progress on tackling these issues.”
Providing an example, May says, over the last decade, there’s been a rise in the reports of mental health issues in the workplace. CIPD’s 2020 Health and wellbeing at work survey report
that surveyed over 1,000 people professionals - representing 4.5 million employees - from across the UK found that three-fifths of organisations surveyed saw an increase in reported mental health conditions like anxiety and depression among employees in the last 12 months.
This information helped educate and empower many companies surveyed to recognise the need for wellbeing related policies and integrate them into the organisation’s daily practices.
Similarly, People Analytics also played an important role in helping companies deal with the workplace challenges brought about by the COVID pandemic. “Companies used analytics to limit the spread of the virus across the workforce. People Analytics helped them to redistribute workload by redeploying staff to departments experiencing a spike in demand, e.g. frontline call centre staff in a bank dealing with extra calls from business customers during COVID,” says May.
Collecting data effectively
One of the best sources of data for Analytics would be a company’s HRIS system, which usually contains data on most common HR functions like recruitment, talent and performance management, among others.
Some of the insightful data sources that could be leveraged are employee surveys, focus groups, informal chats, exit interviews, sickness absence data, or feedback through sites like Glassdoor, adds May.
“The frequency of data collection depends on the resources available and how critical the data source is to answer the question at hand. For example, some organisations conduct employee surveys annually, while others prefer to run short pulse surveys for different employee groups every few weeks,” says May.
While collecting data, there are some practices that HR should adhere to, says May. “Analytics should benefit both the employee and the organisation. Ensure that the data collected can be legally used for the intended purposes. It is also important to consult with employees on their willingness to share data and give them the option to opt-out if they feel uncomfortable. Also, ensure you link the different data sources that you regularly use so that it’s all in one place, and focus on improving data accuracy. Remember to present data in the simplest way possible for a non-technical audience and teach them how to interpret it.”
Analysing the data
The next step, post data collection, should be to transform it into valuable insights and recommendations. “Identify the questions that could help solve business issues. Choose appropriate data sources and analytical processes to derive insights that answer your questions. Show how the recommendations can directly or indirectly protect or improve the organisation’s financial performance. Where possible, try to estimate the return on investment on proposed interventions,” says May.
Key skills for HR professionals
Speaking about the critical technical skills required for an HR professional to interpret and analyse data, May says, “Basic high school maths skills and the ability to interrogate data are essential. Also, many modern HR software includes out-of-the-box analytics that makes it easier to interrogate data. Some organisations also have customised business intelligence tools that HR and managers can use, like PowerBI and Tableau. Even without these, having advanced Excel skills will help HR collect and analyse data.”
HR professionals can learn more about HR analytics/reading and interpreting data by reading the CIPD People analytics factsheet and attend CIPD and AHRI courses on this topic, says May.
Avoiding common mistakes
While collecting data to build effective, actionable analytics, it is imperative to avoid some common mistakes like asking the wrong questions, measuring the wrong things, confusing correlation with causation and not tackling the key issue. Take the example of the bank that had a problem with employee theft (see breakout box). Their initial response was to train staff. Measuring the number of staff who have gone through employee conduct training was not the correct measure in this case. Only when they explored their people data more broadly did they discover that its branches and district supervisor’s distance were the best predictors for employee theft, says May.
Another serious mistake is ignoring biases in data. May says it is critical to “Be aware of potential sources of bias and explore ways to mitigate them. Be clear about the limitations of the data to avoid undue weight being given to insights based on less accurate data to support decisions. Periodically review outcomes to see whether minority groups are disadvantaged as a result. Use both quantitative and qualitative data to confirm findings.”
Current challenges and Future
Despite its growing popularity, a McKinsey report states that in many companies, People Analytics, data mining and implementation of data analytics are still rudimentary. May says unsatisfactory skill levels of leaders making data-driven decisions and not having the resources like skilled staff, time, tools to do more advance analytics are some of the reasons for underwhelming analytics. “It can be rectified through training and/or hiring people with data analytics skills.”
May is confident that People Analytics is here to stay, and companies will do well to invest in it. “I see more widespread use of People Analytics as 1) HR professionals become more data-savvy, 2) data quality and linkage between different sources improves and is easily accessible from one place, 3) out-of-the-box descriptive, predictive and prescriptive analytics become more easily customised by non-technical users,” says May. She also adds, “The use of Organisational network analysis (ONA) as a technique to understand the relationships and information flows in organisations is becoming more common”.
Case Studies 1: ArcelorMittal, world’s largest steel company uses people measures and performance management systems to build their talent base.
The HR function at ArcelorMittal works closely with other business units to find and develop suitable talent through their Global Employee Development Programme. Rigorous gathering and analysis of their performance management measures ensure that their talent is well supported throughout their career. The insights that the people measures provide has allowed ArcelorMittal to develop a robust succession plan (83% of movement in 2013 came from their plan) as well as increase their employee engagement scores across the organisation.
Case Study 2: ASDA mines customer and employee data to determine the number of store colleagues they need and what skills they will need to develop to achieve an optimum level of service for their customers.
ASDA uses HR Analytics to understand the workstation behaviour of their employees, finding out how long each task by their employees take, how the function is carried out, measuring service provided, time taken on check out, restocking time, and delivery response, among others. ASDA then centralises this data, using it to create and manage standards. They set goals for scanning, transaction, and pick rates and ensure training is provided to their staff to meet these targets for an optimum working environment and one that offers a smooth shopping experience to their customers.
Case Study 3: May cited this example by John Bersin, Global Industry Analyst, Founder of Bersin by Deloitte on how People Analytics dictated an important business decision.
A Canadian bank that had a problem with employee theft in its retail business mined its employee data to identify patterns of theft. Its initial response to train staff was not effective. It then explored its employee data and found that the best predictive driver of theft was the distance between its branches and district supervisor. Employees in remote branches had higher incidences of theft. To address this, the bank changed the rotation of branch managers and spent more time visiting remote branches.
The CIPD recognises the value that analytical evidence has to the future people profession. Visit CIPD Knowledge Hub
for a complete listing of CIPD’s research and insights into data and analytics practice.
Keeping the growing popularity and interest in People Analytics among HR professionals in mind, the Australian HR Institute, in collaboration with CIPD, has launched a course, Mining Data for HR Insights. You can find out more about the programme here https://hrsolutions.cipd.asia/ahri-md4i-course-brochure/