UK productivity is no higher today than it was just before the financial crisis in 2008. The key to solving this problem, and revitalising the economy, clearly lies in the hands of small and medium enterprises (SMEs), which make up 99 per cent of the private sector. It is surely no coincidence that smaller businesses have lower levels of productivity when they also spend less on people development. Remedying the situation could equate to a seismic shift in economic performance.
How do we make this happen? The answer, I believe, is people analytics. For decades, the accounting profession has showed us how to categorise and understand the value of our physical assets. Yet people are still considered a cost to the business rather than an asset on the balance sheet. If HR professionals have the right sort of metrics to report the true value of human capital, they can unlock the 80 per cent of ‘intangible assets’ currently missing from financial reporting.
Big business already knows this only too well. Surveys have suggested that companies that have adopted advanced people analytics capabilities experience an average 25 per cent increase in productivity, alongside a huge rise in recruitment efficiency and a drop in attrition rates.
Smaller firms, by contrast, have traditionally struggled to capture the right sort of information. Big data offers them a potential answer – and it doesn’t necessarily require swathes of new tools, software or data scientists analysing mounds of data to make practical sense of the numbers and provide actionable insights. In fact, many of the most basic metrics small businesses can benefit from are already within their grasp – including data from recruitment processes, engagement surveys, learning and development systems, performance management, diversity initiatives and wellbeing programmes. All of it can be analysed and correlated with corresponding financial data without the need to reinvent the numerical wheel.
Basic data on workforce composition is a crucial starting point towards more sophisticated analysis. Indicative data includes the average number of employees (for example, full-time equivalents, part-time employees) and distribution of certain categories contributing to the diversity of the employee base (for example, age, gender, ethnicity). Understanding this gives a basis to begin looking at more complex calculations such as attrition, skills and capabilities, and return on people investment.
The difficult aspect of this for SMEs is getting started. Many are not required by law to report on their gender pay gap so won’t have begun this process, and they often don’t see the benefits of crunching the numbers for their own sake. But reporting is not an end in itself: when done correctly and in compliance with existing data regulations, the insights from what may seem a relatively benign process can become a driving force for economic growth. Where the numbers finish, the path to analytical wisdom begins.
Nadeem R Khan is founder and managing director of Optimizhr