[Legacy] Map data sources to jobs

Mapping data sources to jobs is a key step in the Market Pricing process that enables you to efficiently match your job architecture in Pave to your market data for competitive compensation benchmarking.

How mapping works

1. Map job families, levels, and pay zones - Rather than mapping data sources directly to compensation bands (which can result in thousands of repetitive mappings and repeat work), you map the individual components of each job once (family, level, pay zone) and selectively use overrides for any exceptions:

   

2. Set weights and logic - Define how your data sources should be applied and weighted when generating benchmarks.

   

3. Generate benchmarks - Generate the benchmarks for each job and review your data coverage across your job architecture and pay types.

   

Prerequisites

• Set up your job architecture
• Create your data sources

Core concepts of mapping

When mapping job families, job levels, and pay zones, there are 3 core entities that can be mapped in each step:

1. Job architecture - the first few columns reference the job architecture configured in Pave. Values in these columns should match the display values from your job architecture (families, levels, and pay zones).
2. Pave data sources - these columns reference the Pave data catalog, which is used to map any Pave data sources (aka data cuts) that have been created.
3. Survey data sources - these columns reference the parsed values from your survey data sources, which is used to map your survey data source job codes / locations.

Methods to create and update mappings

For each mapping step, there are two methods to create and update your mappings:

1. (Recommended) CSV upload - easiest for bulk actions and first-time setup.
2. In-app - good for incremental updates.

See the job families, job levels, pay zones, and overrides sections below for details on each method.

Map job families

Map parsed data source job families to your job architecture in Pave.

To start, go to Market Pricing > Benchmarking > Edit job mapping > Map job families

Column configuration for job families

The columns in the job family mappings align with the 3 core entities.

Job architecture columns

These columns should be populated with values from your job architecture in Pave.

Pave automatically creates a row for every distinct Function + Family + Ladder from your job architecture.

If needed, these values can be found by clicking a specific job under Market Pricing > Jobs or in bulk by downloading your Jobs CSV (Market Pricing > Jobs > (…) > Download Jobs CSV).

Pave data source columns

Survey data source columns

Survey data source columns are dynamically created based on the distinct set of Survey providers in your survey data sources list. Each distinct Survey provider will create the following set of columns:

Map job families with CSV upload

1. Click Download CSV to get a CSV with all mapping rows

2. Open the CSV and fill or update the Pave data source and Survey data source columns with the relevant mappings and weights for each ladder

3. Save the CSV, click Upload CSV in Pave

   

4. Click Upload Mappings and select your CSV

5. Map the columns from your CSV to the correct Job architecture, Pave data source, and survey data source columns

   • Pave will attempt to auto-map your CSV to the correct columns. But if needed, you can map a survey data source column by:

     • Click the Template column dropdown > Add new

       

     • Enter the Column name in the expected format for the Survey provider. It’s important that the {Survey provider} value matches your survey data source Survey provider exactly.

     • Choose the relevant Data type as either Job Code Prefix or Weight , then click Add

       

6. Once columns are mapped, click Next

7. Review the results and finalize your job family mappings

   • Pave will validate your mapped values, flagging any issues or errors before finishing the upload

   • Any row that references a Function + Family + Ladder that doesn’t exist in your job architecture will throw an error

   • You may either resolve issues by editing the cells directly in-app (double click on cell) or updating then re-uploading the CSV

     

8. After reviewing, click Import to save your new mappings

:::info

• Note: Importing replaces all existing values in the mapping table

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Map job families in-app

You can make adjustments to your job family mappings directly in-app. This can be useful for incremental updates.

1. Click Edit

   

2. Edit, duplicate, or delete any mappings

   • To modify Job architecture columns (including adding a completely new ladder), follow the CSV upload method

3. Click Save

   

Map job levels

Map parsed data source job levels to your job architecture in Pave.

To start, go to Market Pricing > Benchmarking > Edit job mapping > Map job levels

Column configuration for job levels

The columns in the job level mappings align with the 3 core entities.

Job architecture columns

These columns should be populated with values from your job architecture in Pave.

Pave automatically creates a row for every distinct Display Level from your job architecture.

If needed, these values can be found by clicking a specific job under Market Pricing > Jobs or in bulk by downloading your Jobs CSV (Market Pricing > Jobs > (…) > Download Jobs CSV).

Pave data source columns

Survey data source columns

Survey data source columns are dynamically created based on the distinct set of Survey providers in your survey data sources list. Each distinct Survey provider will create the following set of columns:

Map job levels with CSV upload

Follow the same process as job family mapping.

Map job levels in-app

Follow the same process as job family mapping.

Map pay zones

Map parsed data source locations to your job architecture in Pave.

To start, go to Market Pricing > Benchmarking > Edit job mapping > Map pay zones

Column configuration for pay zones

The columns in the pay zone mappings align with the 3 core entities.

Job architecture columns

These columns should be populated with values from your job architecture in Pave.

Pave automatically creates a row for every distinct Pay Zone from your job architecture.

If needed, these values can be found on Market Pricing > Pay Zones.

Pave data source columns

Survey data source columns

Survey data source columns are dynamically created based on the distinct set of Survey providers in your survey data sources list. Each distinct Survey provider will create the following set of columns:

Map pay zones with CSV upload

Follow the same process as job family mapping.

Map pay zones in-app

Follow the same process as job family mapping.

Apply overrides

After mapping families, levels and pay zones, you can set overrides to:

1. Add exceptions for individual jobs - Overrides allow you to map a specific job directly to a survey job code when the mapping doesn’t align with your standard family and level mappings. Example:
   • If you map your P5 level to P5 for all jobs across your organization
   • But your Software Engineering - P5 roles map to P6 in Radford instead
   • You would create an override mapping the individual job to the individual survey provider job coded (e.g., EN.SODE.P6 instead of the default EN.SODE.P5)
2. Map survey data sources that aren’t natively supported - In cases where the provider code format is different from the formats Pave currently supports, overrides can be used to map the individual jobs to the survey job code.

To start, go to Market Pricing > Benchmarking > Edit job mapping > Apply overrides

How overrides work

When generating benchmarks, Pave will first check if an override exists for each job. If there is no override, Pave will use the defined family and level mappings from the other steps. If there is an override, Pave will:

• For each Survey provider column, check if there is a job code value populated
  • If there is a job code, the override job code will be used for those Survey provider data sources
  • Otherwise the defined family and level mappings will be used for those Survey provider data sources

Column configuration for overrides

The columns in the overrides mappings align with the 3 core entities.

Job architecture columns

These columns should be populated with values from your job architecture in Pave.

Pave data source columns

Survey data source columns

Survey data source columns are dynamically created based on the distinct set of Survey providers in your survey data sources list. Each distinct Survey provider will create the following set of columns:

Map overrides with CSV upload

Follow the same process as job family mapping.

Map overrides in-app

Follow the same process as job family mapping.

Blend multiple data source families, levels, or pay zones

To blend multiple data source families/levels/pay zones, duplicate the ladder row and fill in the relevant data source columns. Pave will generate benchmarks for relevant jobs using the weighted blend of each ladder.