To date, those of you who have adopted compensation management technology have probably done so to gain operational efficiency. Reducing time spent on repetitive administrative tasks such as data calibration and spreadsheet reconciliation, whilst benefiting from improved data integrity and security together with a clear audit trail, have been the key buying reasons to date.
But we are now seeing the advent of “datafication” in HR and, as a function, coming to terms with the challenge of improving our data management and analytics capabilities to get an optimal return on investment in human capital.
Not surprisingly, this development is coming hot on the heels of a recognition that as function we also need to move beyond the traditional soft HR agenda to one that helps businesses create sustainable competitive advantage.
In response, HR software providers are integrating data analysis tools into their HR platforms so solutions don’t just automate processes but now provide critical decision support.
You’ve probably already been involved in initiatives in your own organisation looking broadly at how analytics can improve HR, but have you considered yet how to apply this to reward?
Change the way we operate as reward practitioners
The application of business analytics delivered through technology will undoubtedly help us to evolve the role of reward. When I started working in HR over 25 years ago, reward management was very much a finance and budget driven process – we acted as process administrators – and, if I am honest, provided limited value add.
The advent of job evaluation, benchmarking and performance related pay saw reward management becoming more of an HR owned process; as we developed ourselves as subject matter experts. When it was introduced, technology helped to automate processes but the user interface in the systems available was so poor that HR tended to be the only users.
Software as a Service (SaaS) technology has, in the last five years, become the delivery model of choice for HR systems and, with a significantly improved user interface, helped the transfer of ownership of reward decisions from HR to the real business decision makers.
Now it is easy to see compensation data calibrated to support informed pay decisions and governance is achievable – with hard and soft alerts to control recommendations in line with guidelines and reward principals – so we can start to trust line managers to make effective compensation decisions.
When you start integrating business intelligence into compensation technology we can further evolve as evidence based business enablers, providing insight and decision support to business leaders with a focus on achieving key business outcomes.
Improve the way we manage compensation data
Adopting analytical tools in reward will also change the way we manage compensation data from a somewhat one-dimensional approach of metrics and reporting to one of advanced analytics and business insight.
Of all the disciplines in HR, reward has taken pride in its number crunching and reporting capabilities but now it is time to learn new skills that utilise compensation data to provide insight that links to business outcomes.
We need to demonstrate the link between pay and productivity, engagement and talent in a way that allows business leaders to make informed decisions.
A key development in advancing the effectiveness of analytics is going to be the adoption of data visualisation tools that allow us to transform the way we manage and present compensation data.
As the saying goes, “a picture is worth a thousand words” and a story is often best told graphically. It’s scientifically proven that we can process information easier graphically because tables interact primarily with our verbal system whilst graphs are perceived primarily with our visual system.
Also, statistical information is abstract.
The new approaches to data visualisation in analytics aim to translate this abstract into the physical attributes of vision. If you think about how much statistical data we use in reward with compa-ratios to benchmarking data, historical compensation data, fx exchange rate comparisons, budget analysis, total remuneration breakdowns etc this will be invaluable.
Support new approaches to reward
The evolution of performance management from an annual event to one of continuous feedback is also driving a review of traditional models of pay for performance.
Leading organisations are considering how to delink pay decisions from the annual performance rating and change the focus on employee value from a backward-looking assessment of performance only, to a broader view of pay for talent that considers multiple factors – past and present – that can capture employee value and market worth.
These new approaches require us to calibrate multiple data points on an employee to capture the notion of employee worth and ensure reward goes to those employees who provide the most value in terms of not only performance and contribution, but also potential, critical skills and future talent.
So, it’s not just about correlating performance rating and pay benchmarking data any more. Now we have to consider an employee’s retention or flight risk and whether they have critical skills for immediate and future business deliverables.
Mining talent data using business analytics will allow us to address some key questions when making compensation decisions such as:
- Am I recognising my employees most at risk of leaving?
- Who has got the key skills I need for next year’s deliverables?
- Where can I flex my people costs to speed up revenue creation?
- Am I differentiating reward for my hyper performers?
- Am I rewarding for a build versus buy approach to talent?
- Am I paying equitably compared to other managers?
Ensure equity and transparency in reward decisions.
The headlines are currently all about equal pay and pay transparency and reducing subjectivity in pay decisions.
Whichever approach you choose to manage reward, the reality is that pay decisions will never be wholly objective. Ultimately, it’s a decision about people by people but you can make more balanced decisions by providing quality information to decision makers.
Managers are often not equipped with the information to adequately differentiate performance and talent and how to reward it. This is where business analytics can help by providing actionable insight and processing information in a more meaningful way. Ultimately this will increase transparency with smarter pay decisions.
It’s time to upskill reward to unlock the potential of compensation data to drive business outcomes and fulfil a role of evidence based business enabler.