HR analytics is only as reliable as the data it draws from. The challenge is not a shortage of data - most organizations of 200 employees or more are generating workforce data from a dozen or more systems. The challenge is integrating those systems, resolving inconsistencies between them, and maintaining the data quality disciplines that make the resulting analysis trustworthy over time.
This article describes the primary HR data source categories, the specific systems that dominate each category, their key data elements, typical integration approaches, and the data quality problems that each source commonly introduces.
Human Resources Information Systems (HRIS)
The HRIS is the system of record for the employee relationship. It holds the authoritative record of who is employed, in what role, at what pay, in which location, under which manager - and the history of how all of those attributes have changed over time.
Core platforms:
Workday is the dominant enterprise HRIS for organizations above roughly 1,000 employees. Its strengths for analytics include a well-structured object model (Worker, Position, Job Profile, Organization), strong API access via Workday’s RaaS (Reports as a Service) and REST APIs, and a compensation and position management model that supports workforce planning use cases. The data model is highly configurable, which creates complexity: field definitions, custom fields, and business process configurations vary significantly between implementations, making cross-customer benchmarking difficult.
SAP SuccessFactors is common in large enterprises, particularly those already running SAP ERP. Its analytics capabilities have expanded significantly through the acquisition of Qualtrics for engagement and the native reporting environment. Integration complexity is higher than Workday for most analytics use cases.
BambooHR is the leading mid-market HRIS for organizations in the 50-500 employee range. Its data model is simpler and less configurable than Workday, which makes integrations more consistent. The API is well-documented and straightforward. Analytics use cases are typically simpler - headcount tracking, turnover calculation, basic employee profile data - which BambooHR supports well.
ADP Workforce Now is common in organizations that centralized payroll first and added HR features over time. Its strength is payroll data accuracy and history. Its analytics weaknesses are a less structured employee profile data model and more limited API access compared to Workday or BambooHR.
Key data elements from HRIS:
- Employee ID, hire date, termination date, and termination reason (the foundation for turnover and tenure calculations)
- Job title, job family, job level, and department hierarchy (required for segmentation and compensation equity analysis)
- Location and work arrangement (office, remote, hybrid)
- Manager ID and manager history (enables manager effectiveness analysis)
- Compensation: base salary, target bonus, actual bonus, equity grants (primary input for pay equity analysis)
- Employment status: full-time, part-time, contractor classification
- Performance ratings (if managed in the HRIS; often stored separately in performance tools)
Common HRIS data quality issues:
- Inactive record persistence: Former employees with miscoded termination dates or incorrect active/inactive status inflate headcount counts.
- Job title inconsistency: Free-text job title fields accumulate hundreds of variations (“Software Engineer,” “Swe,” “Software Eng,” “SWE II”) that require normalization before analysis.
- Manager hierarchy gaps: Manager fields that point to terminated employees, blank manager fields for new hires, or outdated manager relationships that were not updated when teams reorganized.
- Effective date discipline: Historical changes - promotions, transfers, pay adjustments - need accurate effective dates to support any trend analysis. Systems that record the change date (when data was entered) rather than the effective date (when the change took effect) introduce systematic bias in historical analysis.
Applicant Tracking Systems (ATS)
The ATS is the system of record for the recruiting process. It tracks every candidate from application through hire (or rejection), capturing the stages of the recruiting pipeline, the time elapsed at each stage, and the outcome.
Core platforms:
Greenhouse is the most analytics-friendly enterprise ATS. Its structured data model - applications, stages, interviews, scorecards, offers - exports cleanly, and its reporting API provides good access to pipeline data. The scorecard data from structured interviews, when used consistently, provides a rich source of predictive data for quality-of-hire analysis.
Lever is a strong competitor with similar analytics capabilities and a slightly simpler configuration model. Its CRM-style approach to candidate relationship management produces richer sourcing and candidate journey data than some alternatives.
Taleo (now Oracle Taleo) is common in large enterprises, particularly those running Oracle HCM. Its data model is complex and its reporting capabilities have historically lagged behind Greenhouse and Lever, though Oracle has invested in improving this through Oracle Analytics.
iCIMS is common in high-volume recruiting environments (retail, healthcare, logistics). Its workflow automation capabilities are strong; its analytics interfaces require more configuration to produce clean data.
Key data elements from ATS:
- Application date, stage-by-stage dates, and disposition at each stage (foundation for time-to-hire and funnel conversion calculations)
- Source attribution: which channel produced the application (LinkedIn, Indeed, employee referral, agency, careers page, etc.)
- Recruiter and hiring manager assignments
- Structured interview scorecard ratings
- Offer details: offer date, accepted/declined date, offer amount, decline reason
- EEOC self-identification data (in U.S. environments, stored separately and accessed through specific reporting controls)
Common ATS data quality issues:
- Stage timestamp completeness: Recruiters who move candidates through stages in batch (advancing multiple candidates at once at end of week) produce inaccurate stage timing data that distorts time-to-hire calculations.
- Source attribution inconsistency: “LinkedIn” might appear as LinkedIn Recruiter, LinkedIn Jobs, LinkedIn Inmail, or a recruiter’s manual entry. Standardizing source taxonomy is a recurring data governance challenge.
- Scorecard utilization gaps: Interview scorecards that are not completed, completed late, or completed with uniform ratings across criteria are not analytically useful. Scorecard completion rate is itself a useful metric for diagnosing interviewer discipline.
- Candidate deduplication: Candidates who apply multiple times for different roles, or who are entered into the system multiple times, create inaccurate pipeline counts.
Payroll Systems
Payroll systems hold the authoritative record of compensation paid - not just what compensation was planned or approved, but what was actually disbursed. For pay equity analysis and compensation benchmarking, payroll data is more accurate than HRIS compensation fields because it reflects the actual payment history, including corrections, adjustments, and one-time payments.
Core platforms:
ADP (Workforce Now, Run, and the enterprise ADP Global Payroll product) is the largest payroll processor globally. Its data exports are standardized across its product lines, though the specific export format and available history depth vary. ADP’s reporting API access is more limited than HRIS APIs; many organizations pull payroll data through scheduled flat-file exports.
Gusto is the dominant payroll platform for small and mid-market organizations (typically under 500 employees). Its API is well-documented and provides transaction-level payroll data. For analytics use cases that require payroll history, Gusto’s data is generally clean and well-structured.
Paychex is common in mid-market environments and offers similar data depth to ADP, typically accessed through file exports or the Paychex Flex API.
Key data elements from payroll:
- Pay period earnings by category: base salary, overtime, bonus payments, equity vest events
- Deductions: benefits premiums, retirement contributions, tax withholdings
- Year-to-date earnings and prior-year W-2 data
- Pay rate change history with effective dates
Integration note: When both an HRIS and a payroll system are present (the common configuration for mid-market and enterprise organizations), the HRIS compensation fields and the payroll actual-earnings records will differ. The payroll system should be used for compensation analytics when precision is required; the HRIS should be used for position-level approved pay data when analyzing budgeted vs. actual compensation.
Engagement Survey Platforms
Engagement surveys are the primary source of attitudinal and experiential data about the workforce. Unlike behavioral data (which is observable), engagement data is self-reported and requires careful instrument design and administration discipline to be analytically valid.
Core platforms:
Culture Amp is widely used in mid-market and enterprise HR organizations. It provides structured survey templates, strong anonymity protections that preserve segment-level reporting even at small group sizes, and analytical features including driver analysis (identifying which engagement factors most influence overall scores) and year-over-year comparison. Culture Amp’s data exports include response-level data (appropriately anonymized) that can be joined to HRIS data for segmented analysis.
Glint (now Microsoft Viva Glint) is the enterprise-grade competitor. Its integration with Microsoft 365 creates opportunities for connecting engagement data to productivity and collaboration patterns from Teams and email metadata. It is common in organizations already committed to the Microsoft ecosystem.
Qualtrics EmployeeXM is the most configurable option and supports complex survey designs including 360-degree feedback and multi-rater assessments. Its analytics environment (Qualtrics XM Discover) is powerful but requires significant configuration investment.
Lattice combines engagement surveys with performance management, goal tracking, and one-on-one meeting tools. For organizations that want a single platform for the employee experience data layer, Lattice’s integration across these domains enables cross-functional analysis that is difficult to achieve when the data is fragmented across separate tools.
Key data elements from engagement platforms:
- Survey response dates, response rates by segment
- eNPS score and distribution
- Engagement index scores and sub-dimension scores (collaboration, recognition, growth, manager relationship, etc.)
- Open-ended text responses (analyzed through qualitative coding or NLP)
- Participation rates by department, location, and manager (low participation in a segment is itself a signal)
Data quality considerations:
- Anonymity thresholds: Most platforms suppress segment-level results when a segment has fewer than 5-10 respondents to protect anonymity. This creates gaps in the data for small teams. Analytics designs need to account for these suppression rules.
- Response rate bias: Low response rates in specific segments may reflect disengagement (the most disengaged employees are least likely to complete surveys), creating systematic undercount of the problem.
- Survey fatigue: Increasing survey frequency without decreasing survey length or demonstrating that responses lead to action reduces response quality over time.
Learning Management Systems (LMS)
The LMS records training assignments, completions, assessment scores, and certification status. For organizations with regulatory compliance training requirements, the LMS data is operationally critical; for analytics purposes, it provides the learning activity data that feeds training completion rate calculations and skills inventory analysis.
Core platforms:
Cornerstone OnDemand is the dominant enterprise LMS. Its reporting environment is comprehensive but complex; analytics use cases typically require data extraction to a separate analytical environment rather than relying on native reports.
LinkedIn Learning (and the integrated LinkedIn Learning Hub) is common as a development platform for knowledge worker populations. Its data integrates well with LinkedIn’s professional development tracking features and provides access to content consumption data that native LMS platforms typically do not.
Docebo and TalentLMS are common in mid-market organizations. Both have well-documented APIs for data extraction.
Key data elements from LMS:
- Course assignments and assignment dates
- Completion dates and completion status
- Assessment scores and pass/fail results
- Time spent per course
- Certification issue and expiration dates
Performance Management Platforms
Performance ratings, goal achievement records, and manager assessments are increasingly stored in dedicated performance management tools rather than HRIS systems.
Core platforms:
Lattice provides goal tracking (OKRs), continuous feedback, performance review workflows, and engagement surveys in a single platform. Its analytics environment shows individual and team performance trends.
15Five is focused on continuous performance management - weekly check-ins, pulse surveys, and manager-employee dialogue - with quarterly review capabilities. Its data is more behavioral (conversation frequency, goal check-in rates) than rating-centric.
Workday Performance (native to Workday) is used by organizations that want to keep performance data within the HRIS. Its integration with other Workday workforce data is seamless; its analytics capabilities are comparable to mid-market standalone tools.
Key data elements from performance tools:
- Performance rating distributions by manager, department, and level
- Goal completion rates
- Calibration outputs (final adjusted ratings after calibration sessions)
- 360-degree feedback scores
- Manager assessment ratings for quality-of-hire analysis
Integrating HR Data Sources
The analytical value of these sources compounds when they are integrated. Joining ATS data to HRIS data enables quality-of-hire analysis. Joining HRIS data to engagement survey data enables retention risk modeling. Joining payroll data to performance data enables compensation equity analysis.
The typical integration architecture for a mature HR analytics program moves data from each source system into a centralized data warehouse - BigQuery, Snowflake, or Redshift - where it is standardized, joined, and made available to BI and analytics tools such as Plotono. Employee ID is the universal key for joining HRIS, payroll, performance, and engagement data. Candidate ID (from the ATS) links to Employee ID upon hire.
The investment in data infrastructure pays back most quickly in attrition analysis and pay equity analysis, where the business impact of the insight is largest and the data joins are most tractable. For a detailed treatment of the analytical techniques that build on this data foundation, see HR Techniques. For the KPIs calculated from these sources, see HR KPIs.