The role of people analytics in HR is expanding
People analytics, often known as HR analytics, is a component that incorporates people data into analytical processes to address business challenges. It combines people data (such as payroll and absence management) with business data (such as operations performance data) to provide people professionals and their stakeholders with insights into their workforce, HR policies, and practices, resulting in better evidence-based decision making.
People Analytics may help managers make better decisions by identifying drivers of engagement, retention, performance, and wellbeing, among other things, by offering data-driven insights. Companies can use the data to tailor treatments to different employee groups to improve overall performance.
Helping Decision Making:
The growing popularity of People Analytics cannot be overlooked. According to research by the Corporate Research Forum, 69 percent of companies with 10,000 or more employees have a People Analytics department. People Analytics' ability to inform managerial decisions is credited with its appeal. People analytics has previously assisted businesses in identifying and replicating high-performing individuals and teams. Google's Project Oxygen, for example, looked at the best-performing managers' approaches and applied them to low-performing personnel.
People analytics is also assisting businesses in recognizing societal challenges that exist in today's climate - the need for stronger policies relating to employee mental and emotional welfare, gender pay parity, and diversity – and making decisions that meet these needs. It's become more critical to track progress on these issues using efficient People Analytics techniques."
For instance, there has been an increase in reports of mental health concerns in the workplace during the last decade. In the last 12 months, three-fifths of organizations surveyed saw an increase in reported mental health conditions like anxiety and depression among employees, according to the CIPD's 2020 Health and Wellbeing at Work survey, which polled over 1,000 professionals from across the UK, representing 4.5 million employees.
Many of the organizations questioned were able to see the need for well-being-related policies and integrate them into their everyday operations because of this information. Similarly, People Analytics played a critical role in assisting businesses in dealing with the workplace issues posed by the COVID outbreak. Companies employed analytics to keep the virus from spreading throughout the workforce.
People Analytics assisted them in redistributing workload by redeploying people to areas that were experiencing a surge in demand, such as frontline call centre employees in a bank coping with extra calls from business customers during COVID.
Collecting data effectively :
A company's HRIS system, which often incorporates data on most major HR operations including recruitment, talent, and performance management, among others, is one of the greatest sources of data for Analytics. Employee surveys, focus groups, casual talks, exit interviews, sickness absence data, or feedback from sites like Glassdoor are just a few examples of useful data sources. The frequency with which data is collected is determined by the resources available and the importance of the data source in answering the question at hand.
Some companies, for example, perform staff surveys once a year, while others prefer to conduct short pulse surveys every few weeks for different employee groups.
There are specific best practices that HR should follow while collecting data, and analytics should help both the employee and the company. Ascertain that the information gathered can be utilized legally for the intended purposes. Employees should also be consulted about their readiness to disclose data, and they should be given the choice to opt-out if they are uncomfortable.
Also, be sure to integrate the many data sources you use on a regular basis so that everything is in one place, and work on improving data accuracy. Remember to give facts to a non-technical audience in the simplest way possible and teach them how to analyse them.
Analysing the data:
Following data collection, the next step should be to turn it into useful insights and recommendations. Determine which questions might be useful in resolving company problems. To obtain insights that address your queries, choose relevant data sources and analytical procedures. Demonstrate how the recommendations can protect or improve the organization's financial performance, either directly or indirectly. Try to assess the return on investment on planned interventions as much as feasible.
Key skills for HR professionals:
Basic high school arithmetic abilities and the ability to probe data are crucial, the technical skills required for an HR professional to comprehend and analyze data. Furthermore, many modern HR software packages feature out-of-the-box analytics, making data analysis much easier.
Some companies have bespoke business intelligence tools, such as PowerBI and Tableau, that HR and management can use. Even if you don't have these, strong Excel abilities will assist HR in collecting and analysing data. HR workers can improve their data reading and interpretation skills by learning more about HR analytics.
Avoiding common mistakes:
When gathering data for effective, actionable analytics, it's critical to avoid frequent blunders like asking the wrong questions, measuring the wrong things, confusing correlation with causation, and failing to address the main problem. Take, for example, a bank with a theft problem among its employees (see breakout box).
The first thing they did was train their employees. In this scenario, counting the number of employees who have completed employee behaviour training was not the right metric to use.
Only after digging deeper into their people data did, they realise that the distance between the company's branches and the distance between the district supervisor and the individual were the best indicators of employee theft.
Another serious blunder is ignoring data biases:
Be mindful of potential sources of prejudice and research measures to mitigate them. To avoid giving disproportionate weight to insights based on less precise data to support judgments, be clear about the data's limits. Review outcomes on a regular basis to assess if minority groups suffer as a result. Confirm findings using both quantitative and qualitative data.
Current challenges and Future:
Despite its growing popularity, according to a McKinsey analysis, People Analytics, data mining, and data analytics implementation are still primitive in many firms. Underwhelming analytics can be attributed to a lack of ability among leaders to make data-driven decisions, as well as a lack of resources such as experienced employees, time, and tools to perform more advanced analyses. It can be fixed by training and/or hiring personnel with data analytics expertise."
Analytics is here to stay, and firms who invest in it will reap the benefits.
As 1) HR professionals become more data-savvy, 2) data quality and linkage across different sources improves and is easily available from one location, and 3) out-of-the-box descriptive, predictive, and prescriptive analytics become more easily customizable by non-technical users, Organizational network analysis (ONA) is becoming more widespread as a technique to study the relationships and information flow in companies.