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Blog›HR / People

Pay Equity and Band Analysis Without the Spreadsheet Marathon

Compensation reviews involve sensitive data, complex band logic, and regulatory pressure. Most HR teams are still doing them in Excel files that take a week to prepare and are outdated before they land in the room.

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HR team meeting

The problem

  • →Pay equity gaps by gender, ethnicity, or tenure are invisible until someone builds a controlled regression model — a task that falls outside most HR teams' tooling.
  • →Salary band compression — where new hires earn close to or more than tenured employees in the same band — accumulates quietly and only becomes visible during annual reviews when it is already a retention problem.
  • →Merit cycle modelling requires scenario analysis across budget constraints, performance ratings, and band ranges simultaneously, which Excel handles poorly at scale.
  • →Market benchmark data from third-party surveys sits in separate spreadsheets that no one has linked to actual employee compensation in a systematic way.

Why the usual approach breaks down

Pay equity analysis requires a controlled statistical model, not a simple average

A raw gender pay gap figure is meaningless without controlling for role, level, tenure, and location. Building a valid controlled comparison requires regression logic that goes well beyond pivot tables, and getting it wrong produces numbers that can mislead leadership or fail a regulatory audit.

Salary band data and actual compensation live in separate systems

Band ranges often exist in a separate HR policy document or spreadsheet, while actual salaries are in the HRIS. Joining them requires a consistent job code or level ID that is not always applied rigorously across systems.

Merit cycle modelling has too many interacting constraints for manual what-if analysis

A merit cycle involves budget pools, performance rating distributions, compa-ratio targets, and band caps — all interacting. Changing one assumption ripples through the rest in ways that are impossible to track reliably in a shared Excel file with multiple editors.

Compensation data is among the most privacy-sensitive HR data you hold

Salary information requires strict access controls. Uploading compensation files to arbitrary SaaS tools or sharing them over email creates exposure that causes many compensation reviews to happen offline, in isolated spreadsheets, with no audit trail.

How AnalityQa AI AI solves it

Upload your data — or connect it live — and ask in plain English.

01

Upload your HRIS and band data and run a pay equity audit in one session

Drop your compensation export alongside your salary band ranges into AnalityQa AI AI. It joins on job code or level, builds the controlled comparison groups, and surfaces pay equity gaps with statistical context — no data team required.

02

Band compression heatmap in plain English

Ask 'show me which bands have the most compression between new hires and employees with more than two years of tenure' and get a visual heatmap ranked by severity, with the underlying employee count for each cell.

03

Scenario modelling for merit cycles

Set your budget constraint, performance rating distribution, and compa-ratio targets in natural language. AnalityQa AI AI computes the resulting pay increases, flags employees who would breach their band cap, and shows the budget impact under each scenario.

04

Market benchmark comparison from an uploaded survey file

Upload a third-party salary survey export alongside your HRIS data and ask for a percentile comparison by role and location. AnalityQa AI AI joins on job family or benchmark code and shows you where your ranges sit relative to the market.

05

Built for sensitive compensation data

All compensation records are encrypted in transit and at rest, isolated per tenant, never used for model training, and can be deleted on request. Access controls let you restrict which users in your organisation can query salary-level data.

You askedGenerated in 4.2s

"Run a controlled pay equity analysis by gender, controlling for level, department, and tenure."

Headcount

284+12

Attrition

8.1%−1.4pp

Time to hire

31d−4d

Table: adjusted gender pay gap by department and level with significance indicators

Last 12 mo

Heatmap: compa-ratio by band and tenure group, sorted by compression severity

Summary table: merit scenario — spend, avg increase, cap-breach count, by department

A dashboard built in AnalityQa AI — from question to chart, no SQL.

Real examples

Paste your data. Ask. Ship.

You

Run a controlled pay equity analysis by gender, controlling for level, department, and tenure.

AI

AnalityQa AI AI builds a regression-style comparison that holds level, department, and tenure constant, then reports the adjusted pay gap by gender with confidence intervals and sample sizes.

Table: adjusted gender pay gap by department and level with significance indicators
You

Show me a salary band compression heatmap — new hires vs. 2+ year employees — across all bands.

AI

It computes compa-ratios for each tenure group within each band, calculates the compression differential, and renders a heatmap sorted by severity.

Heatmap: compa-ratio by band and tenure group, sorted by compression severity
You

Model a 4% merit budget. Prioritise employees below the band midpoint and cap increases at 8%.

AI

AnalityQa AI AI applies the priority rule and cap, distributes the budget across eligible employees, and shows the total spend, average increase, and the number of employees who would have exceeded the cap.

Summary table: merit scenario — spend, avg increase, cap-breach count, by department
You

Compare our salary ranges to the market survey at the 50th percentile by job family.

AI

It joins the uploaded survey file to your band ranges on job family code, computes the gap to market P50, and flags bands that are more than 10% below market.

Bar chart: band midpoint vs. market P50 by job family, with gap percentage
You

Which employees in the Engineering band are below their band minimum after last year's merit cycle?

AI

AnalityQa AI AI filters for current Engineering employees, compares each salary to the band minimum in your range file, and returns a list of employees below minimum with the dollar amount needed to bring them to floor.

Table: below-minimum employees in Engineering — current salary, band min, gap

What teams get out of it

✓Compensation teams complete a controlled pay equity audit in a single afternoon rather than across two weeks of spreadsheet work.
✓Band compression issues are identified and quantified before they drive attrition, rather than being discovered retrospectively.
✓Merit cycle scenario modelling that previously required a specialist analyst becomes a self-service exercise for HR business partners.
✓Compensation analysis that previously required isolated spreadsheets can run in a controlled, auditable environment with appropriate access restrictions.

Frequently asked questions

Is the pay equity analysis statistically valid?+

AnalityQa AI AI uses a controlled comparison methodology that adjusts for the dimensions you specify — level, tenure, location, department. It reports sample sizes and confidence indicators so you can assess reliability. For formal legal reporting, we recommend using the output as a starting point and having a specialist validate the methodology for your jurisdiction.

Can it handle multiple currencies for a global compensation review?+

Yes. Specify the exchange rates or upload a currency table and AnalityQa AI AI converts all figures to your reporting currency before analysis. It also supports local-currency breakdowns for country-level views.

How is compensation data protected?+

Compensation data is encrypted at rest and in transit, and is never used to train any model. Row-level access controls let you restrict which users can see individual salary figures vs. aggregate statistics only.

Can I connect directly to our HRIS database instead of uploading a CSV?+

Yes. AnalityQa AI AI supports direct PostgreSQL and MySQL connections. Connect your HRIS database and query it in natural language without exporting any files. Google Sheets connections are also supported.

How does it handle job code inconsistencies between our HRIS and the band range file?+

You can tell AnalityQa AI AI which columns to join on and how to handle mismatches — for example, falling back to job family when an exact job code match is not found. Unmatched records are flagged in the output rather than silently dropped.

Can the merit scenario modelling account for promotion increases separately from merit increases?+

Yes. If your data includes a promotion flag or a separate promotion budget column, AnalityQa AI AI can model the two pools independently and show the combined impact on the total compensation budget.

What plan do I need for multi-scenario merit modelling?+

Multi-scenario modelling with saved scenarios is available on Pro and Business plans. Single-scenario analysis from an uploaded file is available on the free tier.

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