People Analytics and automation are revolutionising how HR professionals approach their work. At the heart of this transformation is the ability to reduce the time spent on repetitive administrative tasks and, instead, to focus on collecting and analysing data to provide data-driven insights that can help to build a better workplace.
People analytics is not a new concept. Jac Fitz-Enz, a pioneer in HR benchmarking, talked about quantifying and accurately measuring the productivity of all major HR functional areas in his 1984 book "How to Measure Human Resources Management". However, technology is now allowing this change to move at pace.
With HR professionals spending 40% of their time on administrative tasks, implementing tools to help reduce the manual workload and record vital data is essential. Businesses can then better understand their workforce by using people analytics to evaluate employee performance, engagement, and demographic information. This intelligence can then inform strategic choices and answer critical questions such as who the best candidates are to hire or promote and who is likely to leave.
It is important to be aware of the impact on privacy and concerns around digital surveillance and to weigh up the value of the data versus how intrusive it is to the employee. The value of the data should not outweigh the importance of employee trust and motivation.
According to KPMG’s 2019 Future of HR survey, 37% of respondents feel confident about HR’s ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60% say they plan to invest in predictive analytics. Among those who have invested in AI, 88% call the investment worthwhile, with analytics listed as a main priority.
While AI is helping to make sense of this data, professionals should remain cautious about the potential for bias and the lack of context in decision-making. Human input remains crucial to ensure that data-driven insights are balanced and fair.
One example of this is using AI in the screening of CVs. This process may overlook candidates who do not use the right keywords or have a non-traditional career path but still have valuable skills and experience. AI also learns from existing data, which can lead to bias becoming part of AI modelling. For example, AI can uncover unequal pay and promotion patterns in an organisation. It may suggest a pay rise for the disadvantaged group; however, it could also continue the pattern, reading it as an indicator of success. Here, a bias dashboard can be a useful tool to help guard against unintended outcomes and check the findings and recommendations AI provides.
Ultimately, automation can be an incredibly valuable tool for HR professionals, reducing the time spent on administrative tasks and streamlining a whole range of processes. However, the main benefit to business lies in the data that new technology can provide and how this intelligence can inform better data-driven decisions.