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12 May 2025

How to Integrate HRIS, ATS, and Survey Data for Retention Analytics

Creating a Unified Data Ecosystem for Proactive Talent Retention

In a data-driven HR environment, the ability to integrate multiple sources—such as HRIS, ATS, and engagement surveys—into a coherent, actionable view of the employee experience is one of the most powerful levers in strategic retention planning. Done right, this integration enables organizations to uncover early warning signs of attrition, spot vulnerable cohorts, and tailor retention strategies with precision.

This guide walks through the end-to-end process of pulling together disparate datasets, mapping the employee journey to key attrition signals, and operationalizing insights within a scalable analytics framework.

 

I. The Strategic Imperative for Data Integration in Retention

Retention isn’t about reacting to exits—it’s about predicting them before they occur. Yet most organizations store the most valuable retention data across siloed systems:

  • The HRIS holds core demographic and employment data.
  • The ATS contains pre-hire history and time-to-hire trends.
  • The Engagement & Exit Surveys reflect sentiment and experience over time.

 

Without integrating these systems, organizations risk making retention decisions based on incomplete pictures or anecdotal reasoning.

 

II. Step-by-Step Guide to Building an Integrated Retention Analytics Ecosystem

 

1. Define the Retention Use Cases

Start with the right questions:

  • Which roles or teams are hardest to replace or losing talent most rapidly?
  • What are the known and suspected drivers of attrition in your company?
  • What decisions will these insights need to support? (e.g., targeted incentives, manager interventions)

 

This clarity informs the data requirements and scope.

 

2. Identify and Map Your Data Sources

 

System

Data Elements

Retention Relevance

HRIS

Tenure, role, level, salary changes, performance, promotions, time in role, location

Structural indicators

ATS

Source of hire, time-to-hire, candidate quality scores, onboarding status

Early attrition signals

Engagement Surveys

eNPS, engagement score, manager trust, role clarity

Sentiment predictors

Exit Interviews

Reason for leaving, satisfaction, improvement suggestions

Validation data

LMS

Learning history, completion rates

Growth motivation

Attendance/Absence

Unscheduled leave, burnout markers

Wellbeing indicators

 

Tip: Use data mapping exercises to track how each dataset aligns to different stages of the employee journey—e.g., pre-hire, onboarding, post-probation, 6-month mark, 1-year, promotion cycles, exit.

 

3. Establish a Unified Data Layer

Your goal is to centralize retention-related data into one analytics environment. Options include:

  • Data Warehouse Integration: Use platforms like Azure, Google BigQuery, or Snowflake to centralize HR data tables.
  • Data Lake with BI Tools: For larger orgs, combine structured and unstructured data (e.g., text from surveys) using ETL tools like Talend or Alteryx.
  • HR Analytics Platforms: Consider People Analytics suites (e.g., Visier, One Model) for pre-built integrations and retention modules.

 

Ensure unique identifiers (employee IDs, emails) are standardized across all systems to enable accurate joins.

 

4. Create Retention-Centric Data Models

This is where lifecycle and analytics intersect. Organize your data to follow the employee journey. Example structure:

  • Pre-Hire: Source, assessment results, offer timing
  • Day 1–90: Onboarding satisfaction, engagement pulse, manager check-ins
  • Post-Onboarding: Performance ratings, internal mobility, recognition frequency
  • Risk Zones: Drop in engagement, absence increase, missed development goals
  • Exit: Tenure at exit, reasons, retention risk flags not acted upon

 

Use machine learning or regression techniques to correlate these stages with attrition outcomes—e.g., “What early signals did our leavers show within the first 90 days?”

 

5. Design a Unified Retention Dashboard

Now visualize insights using tools like Power BI or Tableau. Key features might include:

  • Retention Risk by Segment: Function, level, tenure, manager, geography
  • Engagement vs. Exit Overlay: Correlate low eNPS with future resignations
  • Onboarding Drop-off Tracker: Monitor early exits by recruiter, manager, team
  • Heatmaps of Risk Drivers: Visualize hotspots where multiple risk factors converge

 

Layer in filters and drilldowns for executive, HRBP, and frontline manager audiences.

 

III. Sample Use Case: Preventing Early Attrition in Sales

After integrating the HRIS, ATS, and survey platforms, a B2B SaaS company discovers that:

  • 30% of new sales hires leave within 12 months.
  • Many scored low on onboarding satisfaction and had no internal mentor assigned.
  • Their offer-to-start window was >30 days—double that of employees who stayed.

 

Action Taken:

  • They rewired onboarding to include early manager coaching and peer buddies.
  • Added a new survey checkpoint at Day 45.
  • Shortened offer acceptance cycles with improved candidate experience design.

 

Outcome: Early attrition dropped by 18% within six months.

 

IV. Common Pitfalls and How to Avoid Them

  • Data Silos Persist: Solution → Ensure IT, HR, and data teams are aligned with a shared governance plan.
  • Low Data Quality: Solution → Automate validations and regularly audit for missing or inconsistent values.
  • No Action from Insights: Solution → Create clear pathways for insights to trigger HRBP and manager-level interventions.

 

V. Best Practices for Sustainable Success

  • Assign Data Owners: HRIS, TA, L&D, and Engagement each need accountable leads.
  • Iterate Monthly: Don't wait for perfect data—start small, learn fast.
  • Bring in the Business: Share dashboards with business leaders to co-own retention.

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883-373-766

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