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Article | Beyond Data

North Star principles for assessing different pay data sources

By Laurent Grimal | June 24, 2024

Choosing the right pay data source is critical for well-informed and defensible compensation decisions. We put several sources to the test.
Compensation Strategy & Design|Ukupne nagrade
Beyond Data

In the world of compensation management, selecting the right data sources is critical for making well-informed and defensible decisions. We have determined that great data sources feature key characteristics – North Star principles – that contribute to data efficacy and reliability:

  • Robust data samples
  • Data accuracy
  • Granular pay element definitions
  • Job coverage and granularity
  • Job matching transparency and accuracy
  • Data timeliness and freshness

To understand the merits of different data sources within different contexts, we tested the following sources against our North Star principles:

  • Compensation surveys
  • Government reports
  • HCM aggregators
  • Professional associations
  • Job boards
  • Web scrapers
  • Disclosed data
  • Crowdsourced data

In conducting this analysis, we were able to pinpoint the most effective uses of each data source to match rewards professionals’ specific needs.

Robust data samples, anywhere

Reputable, industry-specific salary surveys and comprehensive compensation databases provide global, extensive data sets across industries and job roles. It is most important to determine whether the sample includes typical peers competing for the same talent.

Web-scraped, job-board and crowdsourced data sources, on the other hand, typically only report on what is being hired right now. As such, these sources should be reserved for early signal detection and pay trends/differentials, and to gauge local market demand.

Data accuracy

To ensure data accuracy and mitigate bias risks, consider how data is sourced and whether biases exist. We can distinguish among three main data collection approaches:

  • Human capital management (HCM) sources: Traditional survey providers and HCM data aggregators typically collect data either directly or indirectly from systems of record. They usually employ stringent verification processes and automated validation checks to maintain reliable and accurate details.
  • Advertised pay: Job boards and web-scraped job-board data suffers from the same reliance on broad job descriptions and broad advertised pay ranges (which may be inflated).
  • Crowdsourced data: Self-reported data from employees suffers from inconsistencies in job evaluation and is not regularly updated.

By using data sources that emphasize data validation, you can confidently base your compensation decisions on trustworthy information.

Granular pay element definitions

For a comprehensive view of regards, data sources that cover the full spectrum of compensation components provide clear and globally consistent definitions. These sources go beyond basic base salary information and include elements such as incentives, allowances, benefits and perquisites.

Compensation surveys usually are the only data source that capture full pay details. HCM aggregators usually miss employee benefits and equity data, whereas online data sources only cover base and/or total pay. By using data sources with granular pay element definitions, you get a holistic understanding of total rewards.

Job coverage and granularity

Accurate job coverage and granularity are vital for precise benchmarking and decision making. Data sources that offer extensive coverage across a wide range of job roles and provide detailed job classifications support compensation comparisons accurately.

While salary surveys traditionally leverage formal leveling systems that normalize market pay levels (ensuring equitable comparisons), we find that online data sources often used broad job classifications owing to the limitations of automated job matching (web scrapers and job boards) and/or self-reporting (crowdsourcing).

Job matching transparency and accuracy

Precise job matching processes that align compensation data with similar job roles across organizations are critical for accurate comparisons and analysis. While compensation surveys provide validated, transparent and accurate job matching based on rigorous methodologies, web-scraped and job-board sources that employ automated job matching as well as broad job leveling cannot guarantee accuracy and transparency.

Data timeliness and freshness

In the United States, safe harbor guidelines prevent the publication of aggregated pay data that is fresher than six months. As such, only time-filtered advertised and self-reported pay sources can guarantee a real-time pulse on the market.

Appropriately applying your data sources

When you think about the data sources you use to inform your compensation decisions, remember to assess the strengths and limitations of each data source. Also, consider your specific needs, industry context and budgetary constraints.

By leveraging these North Star principles, you will gain a better understanding of data in various benchmarking scenarios. This is the basis for making informed decisions and developing robust compensation strategies.

And don’t limit your thinking to only one data source. Rather, consider a blended approach that combines multiple data sources, ensuring that you validate the data and leverage the strengths of each source. This will deliver comprehensive insights into market trends, benchmarking and talent attraction and retention strategies.

By taking these measures, you can ensure your organization is equipped with accurate and relevant compensation data to drive effective rewards programs. Fill out the form on this page to access a 20-page white paper that takes a deep dive into each of the compensation data sources mentioned in this article, comparing them against our North Star principles.


Senior Director, Innovation and Digital Transformation
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