Gender pay gap: why messy reporting hinders analytics

Written by
Edward Houghton

23 Jan 2018

23 Jan 2018 • by Edward Houghton

Large UK organisation have until April 2018 to publish their gender pay gap: the mean and median difference between the pay that men and women receive. It’s hoped that by requiring organisations to report on these figures the government, and businesses, will be better able to understand why the pay gap exists and the actions that can be taken to close it.

But in the scramble to get their numbers out, some organisations are getting the basics wrong and risk reporting incorrect or even false information to meet the deadline, potentially opening themselves up to significant reputational risk. When it comes to HR analytics, quality is paramount. 

Reporting bad sums

In recent years, HR analytics has been a key area of investment for many large organisations in order to gain greater insight into their workforce. The reality for many businesses though is that even significant resource investment in analytics doesn’t always deliver accurate or meaningful results. 

A high profile incident of analytics ‘gone wrong’ was recently highlighted by fashion retailer Hugo Boss, which reported a gender pay gap of 0%, before a Financial Times investigation into their data prompted a recalculation of their mean pay gap (to 33%) and median pay gap (to 77%). Bad sums and even worse quality assurance led to a highly embarrassing situation whereby inaccurate workforce data was reported and then very publicly scrutinised. 

This incident highlights a real issue with HR analytics and reporting: in many cases organisations lack even the basic data for reporting, and are unable to adequately calculate and explain simple ratios.

There are a number of possible reasons behind this. It could be that a lack of skills or capability in those generating and analysing the numbers is to blame, and as it is a new requirement, many are on a learning curve in this first year of reporting. 

More likely though, it is down to the data itself, which may be unclearly defined, inadequately housed and potentially incomplete. For multinational organisations with multiple data sets and complex data systems, calculating what on the surface look like simple ratios can end up being deceptively problematic. 

Getting the basics right

The pressure to get these numbers right is not likely to go away. If anything, the requirement on organisations to report their people data will increase. Gender pay gap ratios highlight that even the simplest type of information may be of interest to an organisation’s stakeholders – and in pushing for their publication, the government, via regulators, has highlighted that information relating to the workforce has value and should be accurately calculated. However, there are significant risks to miscalculating and/or misinforming on measures that must be publicly reported – as the earlier brand backlash highlights, organisations unable to do their sums are likely to come under fire.

While the rhetoric concerning people analytics and people data concerns itself with complex predictive and prescriptive analytics, the reality for most people professionals is that the simplest of equations can be difficult to complete accurately. The people profession has for some time been swept up by promises of big tech and professional services promising sophisticated analytics systems and cognitive computing.

However, the reality of company reports (as CIPD research has shown) is that relatively little insight is disclosed to those stakeholders that matter. Instead of “big ticket” projects, HR should look to build capability alongside simple, incremental investment in technology. 

Start small, get it right and that will create confidence in the data – enabling the practice of people metrics and analysis to grow, and for investment in this space to grow as well. 

The new opportunity

For many, HR analytics has seen a slow start. Often, it requires initial investment and training of staff. There’s also the challenge of deciding what data the business needs and once you have the numbers, interrogating the data to truly understand what it is showing and where future investments and changes in people management and development are needed. The pace of take-up has been slow, but a new wave of government regulation, and indications of future expectations on reporting, provides a new opportunity for organisation and people professionals to get to grips with HR analytics. 

While this first year of gender pay gap reporting has put many on a learning curve, it has provided a welcome nudge to get more businesses thinking about their workforce and how they can - and must – use data better to understand people risks and opportunities. The government’s new corporate governance reforms also require the UK’s publicly listed companies to publish the pay ratio between their chief executive and their average British worker and we expect more workforce data requirements to follow.

Rather than waiting for regulation to hit, people professionals have a real opportunity to embrace and accelerate their understanding of the workforce through better HR data and analysis. Better data will ultimately mean better business and workforce decisions and people professionals should seize the opportunity to be the driving force of this change.