For some HR practitioners, they aren’t all that comfortable being around or dealing with Remuneration. It’s often seen as the gateway drug to data analytics and systems. Because of this, “Rem’mers” are often seen as the black sheep in the family – the weird one that works at their computer all day, staring at numbers and largely keeping to themselves, their data and their graphs.
However as organisations demand a better and deeper understanding of all kinds of data – their customer base, their costs and revenue projections, the efficiency of their sales, marketing and pricing strategies – there is going to be a greater need for HR / People functions to step up their ability to deal with their people data.
Given that most Rem’mers are dealing with data day in / day out and already providing various forms of analysis, Rem’mers find themselves in a pivotal position to start contributing more and more at a strategic level when it comes to understanding their people data and challenging people decisions with data and insights.
For instance, as Equal Pay movements and discussions get the air time they rightly deserve, People teams need to be able to provide clear and concise proof of where they sit on the spectrum, highlighting where issues are within an organisation or which job families are more greatly affected to address the issue effectively. Rem’mers can do this.
Or what are the year on year attrition levels of employees that have a STI in a year with good payout versus a bad payout and what impact does effective communications have on this? Is there a correlation between attrition levels and compa-ratios? Rem-mers can help with this too.
These pretty basic insights can help inform and guide organisations – through experimentation, testing, trial and error – in developing improved approaches to their people challenges. Better approaches and outcomes lead to more engaged employees. More engaged employees mean better outcomes and experiences for customers.
This all sounds very utopian – but it’s not. There are a number of case studies out there showing the very real link between using data to drive better HR decision-making – Peter Cappelli’s excellent HBR article calls out all the big tech names – Google, Microsoft, IBM and rightly so, they are all pioneers in the area and continue to do some amazing stuff – but then there are others such as online travel agency Agoda’s example in McKinsey Quarterly, or Sears (in CEB’s Case Study).
Admittedly, these examples are all large US companies, but the fundamentals are the same – they are all making better people and business decisions based on what their employee data is telling them and developing new solutions that work for them and the context that they operate in.
There are challenges here for Rem’mers too. We need to step out from the light of our computer screens and contribute to the discussions. We need to be able to present more effectively by understanding that many in our audience aren’t as comfortable dealing with numbers or analytics. This can be a struggle at times but in order to address issues as effectively as possible, we need to keep the message clean and simple so everyone knows what the issue is, what needs to be done to address it and then, crucially, what was the feedback so future issues can be addressed more effectively.
For me, this is an exciting time to be in a People team and even more so involved with Remuneration and HR Analytics. The responsibility of providing insights and analysis to help organisations deliver great places for their people to feel valued and thrive in is, personally, an incredibly motivating one. The black sheep are now being embraced by the wider flock and our contributions will only enhance People functions ability to operate at the strategic level now and into the future.
I am incredibly grateful to Natalie Roberts, Emma Le Grice and Kathryn Greene who were all, despite being subjected to having to read the various iterations of this piece, kind enough to provide feedback and encouragement along the way. Any remaining errors or omissions remain solely due to the rookieness of the author.
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