Hidden cost of bad pay data: 3 data sets that really matter
When building a fair and transparent pay framework, you need three core data sets working together:
1. Internal equity data
This shows you how employees compare within similar roles across your organisation. Are two finance managers with similar skills on vastly different salaries? You need to know this before it becomes an equal pay claim.
This isn't just about job titles. Many comparable roles have different job titles, so it’s important to understand the actual work people do through proper job evaluation.
If you participate in a salary survey, roles matched to the same survey level are a good indicator of comparable roles. However, it’s preferable to have your own job evaluation process for comparing work of equal value.
2. External market intelligence
Real market data comes from participated salary surveys where organisations submit their actual pay data, matched against evaluated roles. The median salary for a "project manager" means nothing if you don't know whether that role is managing a small £10k budget or a national project worth £10m.
External benchmark data must also be based on a reliable job evaluation process.
3. Individual contribution data
This is where skills assessments, behavioural competencies, and objective performance metrics come in. But here's the catch: if you can't measure it consistently, it shouldn't influence pay.
That's why so many performance management systems fail and why so many organisations are moving away from performance-driven pay.
For example, if you have a salary range of £40k to £50k for the same role, you need to be clear about which individual contribution factors justify pay differences within the range.
If you differentiate for performance, then how is performance measured and do employees trust that this data is fair? Do you differentiate for skills? If so, how are skills being assessed to ensure decisions are made in a fair and objective way?
Whatever those factors are, make sure you have clear guidance for managers so that this is applied consistently.
Separating good from dangerous data
How do we differentiate good data from ‘not so good’ data?
The risky (but commonly used) data:
- Job adverts showing "up to £60k" (meaningless without context)
- Recruitment surveys based solely on job titles
- That one person who left for double their salary
- Current salaries of new hires (this often transfers inequity between organisations and doesn’t mean it’s the going rate for a job)
That’s not to say that we shouldn’t use these at all, but they shouldn’t be our primary source of data. Otherwise, when employees quote pay data, all we’re saying in response is that our internet research is better than their internet research. They all have access to the same free online data.
The reliable data:
- Participated salary surveys with proper job matching
- Data that considers role content and responsibilities, not just titles
- Consistent methodologies year-on-year
- Geographic and industry-relevant samples
Of course, all data comes with challenges, like not always keeping up with the market. But panicking and throwing money or counteroffers at new hires only creates massive internal equity issues. The cost of retaining or hiring one individual is often disengaging many others in the team.
Making sense of the numbers
Understanding pay data isn't complicated, but you need to know what you're looking at:
- Median beats average: One overpaid person in the role can skew your entire dataset. The median shows you the true middle point and is not skewed by any anomalies.
- Quartiles tell a story: Don’t just look at the median. The gap between lower quartile (LQ) and upper quartile (UQ) shows market variation. Narrow gaps suggest standardised roles; wide gaps can indicate skill premiums.
From gut feeling to objective decisions
Good pay data isn't about having perfect information; it's about moving from subjective gut feelings to objective, defensible decisions. Your employees deserve to know their pay isn't determined by their manager's mood or negotiation skills.
The journey from subjective pay decisions to transparent ones starts with one simple shift: letting good data, not gut instinct, lead the way.
Supplied by REBA Associate Member, 3R Strategy
We help you attract and retain your people through a fair and equitable approach to pay and reward.
