Data Rules

Data rules control how benchmark data is calculated and applied to your jobs. This guide covers how to create data rules, add or remove bands from rules, and configure survey selection and fallbacks.

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What Are Data Rules?

Data rules define how Pave combines multiple survey sources to produce a single benchmark value for each job.

After mapping data sources to your job architecture, the next step is to define how those data sources should be aggregated and prioritized when calculating benchmarks for each job. This is done by creating data rules with weights and fallback logic.

Key components:

• Primary data sources - Which surveys and percentiles to use first
• Fallback data sources - What to use when primary data is missing
• Weights - How much each survey contributes to the composite
• Pay types - Which compensation components to benchmark

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Creating Data Rules

Data rules are created from the Benchmarking tab by selecting bands and adding them to a rule.

Access Data Rules

1. Go to Market Pricing
2. Open your band set
3. Click Benchmarking tab
4. Select bands (checkboxes)
5. Click Apply data rules button in toolbar

Apply Data Rules Modal

When you click Apply data rules, you'll see a modal with three options:

Radio buttons:

• ⚪ Create new rule (selected by default)
• ⚪ Add to existing rule
• ⚪ Duplicate existing rule

Continue button (blue)

Create New Rule

1. Select bands in the Benchmarking tab
2. Click Apply data rules
3. Select Create new rule
4. Click Continue
5. Configure the rule in the Create data rule modal

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Create Data Rule Modal

Header: "Create data rule"

Name

Text input field for the rule name.

Selected Bands (30)

Shows which bands will be assigned to this data rule.

Search field: "Search selected jobs"

Table columns:

• Ladder - Shows ladder + pay zone
• Level - Shows individual level tags (P1, P2, P3, P4, P5, P6) with X to remove

You can remove individual levels by clicking the X on each level tag.

Pay Types

Dropdown: "Select pay types..."

Choose which pay types this data rule applies to (Base pay, Total pay, Variable pay, etc.)

Primary Data

Description: "Select the surveys and percentiles you want to benchmark against."

+ Add button (top right) - Add additional primary data sources

Table:

Columns:

• Data source - Dropdown to select survey (e.g., McLagan, Mercer, Pave)
• Breakout - Dropdown to select data breakout/cut
• Percentile - Dropdown to select percentile (P25, P50, P75, etc.)
• Weight - Numeric input (default: 1)
• ⊖ - Remove this data source row

:::info Note: Only include percentiles you want to weight in your composite. All survey slices will be visible in the Benchmarking sidepeek, as long as one percentile from that survey is selected in your data rule.

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Add Fallback

+ Add fallback button (blue outlined button)

Click to add fallback tiers (up to 3 fallbacks total).

Each fallback follows the same structure as Primary Data:

• Data source dropdown
• Breakout dropdown
• Percentile dropdown
• Weight input
• Remove button

Fallback hierarchy:

When generating benchmarks, the system will look for data in priority order:

1. Primary data source matches with weight > 0
2. First fallback matches with weight > 0
3. Second fallback matches with weight > 0
4. Third fallback matches with weight > 0
5. Any mappings with weight = 0

Only one data source with data is needed from the highest priority group.

Buttons

• Cancel - Close without saving
• Create (or Save for edits) - Blue button to create the data rule

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Adding Bands to Data Rules

You can add bands to an existing data rule from the Benchmarking tab.

Steps to Add to Existing Rule

1. Go to Benchmarking tab
2. Select bands that are NOT currently in any data rule
3. Click Apply data rules
4. Select Add to existing rule
5. Click Continue
6. Choose which existing data rule to add these bands to
7. Review the bands being added
8. Click Save

Result: The selected bands are now included in that data rule and will use its primary data sources, fallbacks, and weights.

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Removing Bands from Data Rules

To remove bands from a data rule, you must delete the data rule or edit it to remove specific bands.

Option 1: Remove Specific Bands from Rule

1. Go to Benchmarking tab
2. Find bands that belong to the data rule you want to edit
3. Click Apply data rules on those bands
4. Edit the existing data rule
5. Remove individual levels by clicking the X next to level tags in the Selected Bands section
6. Click Save

Result: Removed bands no longer use this data rule. They will need to be assigned to a different data rule or left without benchmarks.

Option 2: Delete the Entire Data Rule

1. Go to Benchmarking tab
2. Open data rules management
3. Find the data rule to delete
4. Click Delete
5. Confirm deletion

Result: All bands in that data rule no longer have benchmark data. They must be assigned to a new data rule.

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Duplicate Existing Rule

When creating a new data rule, you can duplicate an existing one to save time.

Steps to Duplicate

1. Select bands in the Benchmarking tab
2. Click Apply data rules
3. Select Duplicate existing rule
4. Click Continue
5. Choose which data rule to duplicate
6. The new rule inherits:
   • Primary data sources
   • Fallback configuration
   • Weights
   • Pay types
7. Edit as needed
8. Click Create

Use when: You want similar logic to an existing rule but for different bands.

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Types of weighting

There are two approaches to weighting data sources:

1. (Most common) Manual weighting - Manually assign specific weights to each data source. This gives you full control but requires more effort to maintain.

2. Market weighting by employee or company - Weight data source benchmarks by number of employees or number of companies represented in the data set. This enhances the accuracy and relevance of compensation benchmarks by considering the relative sample sizes of different data sources, but is heavily dependent on data coverage.

:::info Note: If using a survey data source, there must be an employee or company count column uploaded and mapped in Survey Upload for market weighting to work.

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Related Articles

• Manage Data Sources: Setting up surveys before creating data rules
• Job Mapping: Mapping jobs to surveys
• Reviewing Benchmarks: Seeing data rule results