Real Time data vs Fixed Date of publication data

Globally there is an uptick in organisations offering real time pay data. They’re capitalising on AI and with clever analytical tools are creating pretty smart looking data sets. 

But what type of remuneration data should you use? real time data or data published at a fixed point in time? It will depend on your needs. 

To select the most suitable data for your organisation consider the type of organisation it is, the lifecycle stage of your organisation, your overall strategies and plans including your remuneration and recruitment strategy and policies, employee turnover and the behaviour of the labour market. 

REAL-TIME DATA 

Could be helpful for…

· Taking a competitive position in the labour market

· To test the ‘going rate’ for hard-to-recruit positions

· Market understanding when talent is headhunted 

· Insight if a position warrants a premium, or not

· Reviewing pay on a start date anniversary

· Or when you don’t need much data and want it as current as possible

Some limitations include…

· Real time data will fluctuate, both up and down. It could be tempting to wait for ‘preferential’ data

· Real time data may create pressure on labour costs and HR admin 

· It is likely to be challenging to integrate real time data within an existing pay framework

· Typically, real time data is only available with job matching and having a close ‘fit’ to the job match is crucial

· It will take time for new/novel jobs to be identified and integrated into the job match database

· Real time data will attract organisations seeking ‘fast’ data and may reflect a subset of the labour market

DATA PUBLISHED AT A SPECIFIC POINT IN TIME

Could be helpful when…

· You require a fixed annualised remuneration framework which informs linked policies and processes 

· You review pay across employees on a fixed date annually, supporting internal equity and moderation practices

· You seek to be market competitive but not necessarily lead the market

· You choose to use analytical job sizing as most data publishers have a proprietary job evaluation methodology. Noting that they may also publish job match data

Some limitations include…

· It is by its nature, historical, with a lag between data gathering and publication

· The data reflects remuneration decisions made within the last year

· The data will fluctuate up and down however a fixed review date should enable anomalies to be equitably managed 

· While published data can illustrate premiums and the ‘going rate’ for hard-to-recruit roles, it is important to be aware of the age of the data when making decisions

Regardless of your choice, the important thing is to align it to your organisation’s needs.  It is also wise to check the dataset or database size and composition, if the data is moderated and swamping is managed. 

And remember the golden rule, the more data, the less volatile the results. 

Cathy Fitzsimons

3 July 2024

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