Overview
At Figures, we've implemented several mechanisms to ensure our benchmark data remains accurate, reliable, and representative. This article explains how we maintain data quality through freshness monitoring, balanced sampling, and regular updates.
Data collection
We receive compensation data through two main channels:
HRIS Integration
Automatic daily synchronization with your HR system
Data is continuously updated
Provides real-time reflection of market changes
Spreadsheet Import
Manual data imports with varying refresh frequencies
Refresh rate depends on individual client update schedules
Data freshness
Since spreadsheet updates can occur at irregular intervals, we've implemented a data degradation system to prevent outdated information from impacting our benchmark accuracy. This ensures that older data has a decreasing influence on market insights over time.
How freshness works
All data points are tracked based on their last update date
Weight degradation begins after one year
Each month beyond the one-year mark reduces the data's weight by 15%
Data is removed from the benchmark after 18 months without updates
Weight degradation timeline
Time since update | Data weight |
0-12 months | 100% |
13 months | 85% |
14 months | 70% |
15 months | 55% |
16 months | 40% |
17 months | 25% |
18 months | 10% |
18+ months | Removed |
Balanced sample distribution
Why balance matters
Large companies with numerous employees can significantly impact benchmark calculations. For example, if a tech giant with 1000 engineers contributes to a sample alongside smaller companies with 50 engineers each, without adjustment, the large company's compensation practices would disproportionately influence the benchmark results. This could create a skewed view of the market that doesn't accurately represent diverse company practices.
Our balancing approach
To prevent this, we've implemented a sophisticated weighting system that:
Maintains data point value from all companies
Prevents market dominance by larger organisations
Ensures diverse company practices are represented
Creates a more accurate reflection of the overall market
Weight thresholds and adjustments
Sample size rules
We apply different thresholds based on the number of companies in a sample:
3 companies: No single company can represent more than 34% of the sample
4+ companies: No single company can represent more than 25% of the sample
Adjustment process
When a company exceeds these thresholds, we follow a three-step process:
Initial Assessment
Calculate each company's initial weight based on their data points
Identify companies exceeding thresholds
Weight adjustment
Take the square root of the exceeding company's percentage
This mathematical approach reduces the impact while preserving significance
Weight normalisation
Redistribute the remaining weight among other companies
Ensure all weights still sum to 100%
Apply new weights to individual employee data points
Monthly release cycle (starting 2025)
Starting in 2025, we're implementing a monthly release cycle for our benchmark data instead of real-time updates. This change allows us to enhance data quality through thorough validation while providing more stable and reliable market insights.
The monthly process explained
Each month follows a structured process:
Throughout the month:
We continue collecting data as usual. Your HRIS integrations keep syncing daily, and spreadsheet imports are processed as they arrive. However, this new data doesn't immediately impact the benchmark calculations.Validation:
Our team conducts thorough quality checks on all new data:We analyse market trends and identify any unusual patterns
We verify the consistency of new compensation data
We ensure all calculations (freshness weights, company balancing) are working correctly
Monthly release:
Usually once a month all validated data from the previous month becomes active in the benchmarkAll users see the same, consistent market data