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Welcome to our March 2026 release notes! This monthโ€™s release focuses on data quality, freshness, and richer company workforce insights. Weโ€™ve made significant improvements across our datasets to ensure the information you rely on is more accurate, more current, and easier to analyze at scale. Hereโ€™s whatโ€™s new: Dive into the full details below, or jump to what interests you using the table of contents below.

๐Ÿ“ Schema Changes

New Headcount Insights Fields (Company Data)

Whatโ€™s New? This month, weโ€™re adding 7 new Headcount Insights Fields to our Company Dataset. These new fields are designed to support workforce analytics use cases with richer data on employee movement, tenure, and geographic growth trends. New Headcount Insights Fields
FIELD NAMEFIELD TYPEFIELD BUNDLEFIELD DESCRIPTION
employee_turnover_rateObjectPremium
Comprehensive
The percentage of employees who left the company compared to the companyโ€™s average workforce size over the most recent period. This field may report turnover rates for various periods.
annualized_employee_turnover_rateObjectComprehensiveThe annualized percentage of employees who left the company compared to the companyโ€™s average workforce size over the most recent period. This field is identical to the employee_turnover_rate field, except that each period is annualized to enable direct comparison across different points in time.
median_employee_tenureFloat (>=0)Premium
Comprehensive
The median number of years that employees have worked at the company.
median_tenure_by_levelObjectPremium
Comprehensive
The median number of years that employees have worked at the company, broken out by job title levels.
median_tenure_by_roleObjectPremium
Comprehensive
The median number of years that employees have worked at the company, broken out by job title role.
employee_growth_rate_12_month_by_countryObjectPremium
Comprehensive
The twelve month rate of change across each country. For each country, this field contains the current employee count, the employee count from one year ago, and the rate of growth or decline. See also: employee_count_by_country
Note: users accessing PDLโ€™s Tabular Schema instead receive this data as columns in the employee_count_by_country table.
affiliated_entities.employee_countInteger (>= 0)Premium
Comprehensive
The current employee count for the related company listed in the affiliated_entities object.
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Why It Matters These new fields expand our Company Dataset records with widely used workforce metrics and more ready-to-use indicators of company headcount trends. Together, these fields make it easier to analyze hiring, retention, and geographic expansion without additional calculations. Key benefits include:
  • Turnover fields: Provide standardized turnover metrics that make it easier to benchmark employee retention and workforce stability across companies and time periods
  • Median tenure fields: Adds more robust metrics for understanding employee tenure that complement our existing average tenure fields
  • Growth rate by country field: Simplifies analysis of a companyโ€™s geographic presence and makes it easier to identify locations with growing or shrinking footprints
  • Affiliated profile employee count field: Enables easier aggregation of employee counts across parent companies, subsidiaries, and related entities.
Availability
These fields will be automatically added to the Company Premium and Company Comprehensive field bundles.
Customers receiving either of the Premium or Comprehensive field bundles will automatically receive the new fields (unless they have opted out of such updates previously).
For the employee_turnover_rate field specifically:
  • the 12-month turnover rate will be available in the Premium bundle
  • the 3-, 6-, 12-, and 24-month turnover rates will be available in the Comprehensive bundle
FIELD NAMEBUNDLE
employee_turnover_ratePremium
Comprehensive
employee_turnover_rate.3_monthComprehensive
employee_turnover_rate.6_monthComprehensive
employee_turnover_rate.12_monthPremium
Comprehensive
employee_turnover_rate.24_monthComprehensive
Weโ€™d Love Your Feedback!
Your input helps shape what we build next. Share your thoughts on these fields or suggest new ones via our Roadmap Feature Request Board!

๐Ÿš€ Data Updates

Freshness

The number of jobs and locations verified in our datasets (based on the job_last_verified and location_last_updated fields). Monthly (v33.1 โ†’ v33.2)
Freshness updates over the past month.

Job Changes

The number of person records where the primary job experience changed in our Person Dataset (based on the job_last_changed field). Monthly (v33.1 โ†’ v33.2)
Freshness updates over the past month.

Coverage (Full Stats: Person, Company, Job Posting, IP)

Monthly (v33.1 โ†’ v33.2)

Resume Dataset API Dataset Email Dataset Mobile Phone Dataset Company Dataset Job Posting Dataset

Commentary

  • Person
    • We saw a 133% increase in coverage for language proficiencies in our Resume Dataset as a result of some improvements we made this month.
    • Work email coverage increased by 5% this month, as part of our ongoing efforts to improve the quality and deliverability of our top-level email addresses
    • We saw a 7% drop in coverage for our coverage of job_company_total_funding_raised as a result of a logic improvement we made to ensure more accurate funding information.
  • Job Posting Data
    • This month we saw a 8% increase in the number of unique companies tracked in our Job Posting Dataset.

๐Ÿ›  Improvements and Bug Fixes

Improvements

  • Sourcing Diversification
    • We expanded our data sourcing resulting in a significant increase in profile updates and job change detections.
  • Additional Language Proficiencies
    • We identified gaps in how language proficiency levels were being detected and resolved issues that caused some entries to be missed or misread. As a result, proficiency coverage has increased by 133%.
  • Total Funding Set to null when Unknown
    • We updated the total_funding_raised field in our Company records to return null when all associated funding_details.funding_raised values are null. Previously, this field returned 0 in these cases. This change more accurately represents situations where funding information is unavailable rather than indicating that no funding was raised.

Bug Fixes

  • Incorrect End Dates on Current Roles
    • We resolved an issue where some profiles incorrectly contained an end_date (often 2026) on their current role due to a source data error. In affected records, this caused active positions to be interpreted as ended.
    • This fix restores the correct representation of current roles and resolves several downstream impacts introduced in the previous release, including:
      • Primary job detection: Some profiles temporarily lost their active primary role because the incorrect end_date caused the role to appear inactive.
      • Headcount and workforce metrics: Company headcount trends and derived insights fields (e.g. employee headcount and role transition fields) may have shown artificial declines due to the misclassification of active employees.
      • Experience-based calculations: Metrics derived from current job tenure (such as years at current company) may have been temporarily understated.
    • Not impacted: job_last_changed
      • Our job_last_changed metrics reported this month and last month were not affected by this bug and are accurate representations of the job changes detected in our dataset.
    • With this fix, current roles are again correctly identified as active, restoring accurate job status and downstream metrics.
  • Removed Certifications from Names
    • We expanded our name cleaning logic to ensure certain certifications do not appear in name fields (i.e. CDPSE, NRCSS)
  • Volunteering Experience as Active Job
    • We fixed an issue where volunteer experience was being added into the experience fields and in some cases being selected as an active job.
  • Location Names as Experience Summaries
    • We fixed an issue where the experience.summary field for some records was being incorrectly populated with raw experience location information (e.g. New York, New York, US). This fix removes summaries that directly match location data, improving the quality and uniqueness of professional summary results.
  • Education Raw School Values
    • We fixed an issue with a data source where raw school values (e.g. university of oregon) were being incorrectly mapped to the education.raw field instead of the education.school.raw field.