casemark/managing-dei-metrics

Managing DEI Metrics

Structures diversity, equity, and inclusion data collection, benchmarking against peer and industry standards, and preparation of disclosure-ready DEI reports for ESG frameworks and investor communications.

When To Use

• Building or auditing a portfolio company's DEI data collection infrastructure
• Benchmarking workforce composition against industry peers or index constituents
• Preparing DEI disclosures for annual reports, sustainability reports, or LP questionnaires
• Responding to ESG rating agency questionnaires (MSCI, Sustainalytics, ISS) that include diversity dimensions
• Evaluating fund-level or GP-level diversity commitments (e.g., ILPA diversity metrics template)
• Supporting regulatory disclosure under Nasdaq board diversity rules, EU CSRD, or UK FCA diversity requirements [VERIFY]

Inputs To Gather

• Entity scope: Fund-level, GP-level, or portfolio company-level; single entity or aggregated
• Reporting framework(s): SASB, GRI 405/406, TCFD-adjacent social metrics, ILPA template, UNPRI, proprietary LP templates
• Metric categories: Board composition, senior leadership, overall workforce, new hires, promotions, attrition, pay equity
• Demographic dimensions: Gender, race/ethnicity, age, disability status, veteran status; confirm which are legally collectible in relevant jurisdictions [VERIFY]
• Benchmark sources: Industry peer set, index composition data, national labor force statistics (e.g., BLS EEO-1 categories for US)
• Reporting period: Fiscal year, calendar year, or point-in-time snapshot date
• Prior period data: At least one prior period for trend analysis; ideally two or more for trajectory assessment
• Data collection method: Self-identification surveys, HRIS exports, board questionnaires, third-party data providers

Workflow

1. Define metric taxonomy

   • Map requested metrics to reporting framework definitions (e.g., GRI 405-1 distinguishes governance bodies vs. employees by category)
   • Standardize demographic category labels across entities if aggregating multiple portfolio companies
   • Confirm legal permissibility of collecting each demographic dimension per jurisdiction [VERIFY]

2. Collect and validate raw data

   • Ingest HRIS or survey data; flag response rates below 70% as potentially non-representative
   • Cross-check headcount totals against payroll or financial records
   • Identify missing data points and mark with [VERIFY] rather than imputing values
   • Note self-identification opt-out rates separately — do not merge "declined to state" with any demographic category

3. Calculate core metrics

   • Representation percentages by level (board, C-suite, VP+, manager, individual contributor)
   • Year-over-year change in representation at each level
   • Hiring and promotion rates by demographic group relative to applicant/eligible pool
   • Attrition rates by demographic group (voluntary vs. involuntary where available)
   • Pay equity ratios (median and mean) by gender and race/ethnicity, controlling for role level and geography where data permits

4. Benchmark against peers

   • Source industry benchmarks from relevant datasets (e.g., McKinsey Diversity Wins, Equileap, Bloomberg Gender-Equality Index)
   • Present entity metrics alongside 25th, 50th, and 75th percentile benchmarks
   • Flag metrics where entity falls below 25th percentile as areas of concern
   • Note benchmark vintage — stale benchmarks (>2 years old) should be flagged

5. Assess disclosure readiness

   • Map completed metrics to each target framework's required and recommended fields
   • Identify gaps: missing metrics, insufficient granularity, or data quality issues
   • For regulated disclosures (Nasdaq, CSRD, FCA), confirm all mandatory fields are populated [VERIFY]
   • Draft narrative context for quantitative metrics — explain material changes, initiatives underway, and targets

6. Compile output report

   • Structure by audience: investor-facing summary, internal management detail, regulatory submission
   • Include methodology notes covering data sources, response rates, category definitions, and benchmark sources
   • Attach data tables in appendix format suitable for LP due diligence or rating agency submission

Output

A DEI metrics management report containing:

• Executive summary: Key representation figures, notable trends, and peer positioning
• Detailed metrics tables: Broken out by level, demographic dimension, and reporting period
• Benchmark comparison: Entity vs. peer/industry percentiles with visual indicators (above/below median)
• Gap analysis: Missing data points, framework compliance gaps, and recommended remediation steps
• Methodology appendix: Data sources, collection dates, response rates, category definitions, benchmark vintage
• Disclosure crosswalk: Matrix mapping available metrics to each target framework's line items

Quality Checks

• Confirm all percentages within a category sum to 100% (accounting for rounding)
• Verify headcount figures reconcile to a known source of truth (payroll, board roster)
• Ensure no personally identifiable information appears in output — all data must be aggregated
• Check that small-group thresholds are applied (suppress demographic breakdowns where group size < 5 to prevent re-identification)
• Validate that benchmark comparisons use matching scope (e.g., same industry classification, comparable entity size)
• Confirm disclosure crosswalk covers all mandatory fields for each specified framework [VERIFY]
• Flag any metric where data quality or coverage is insufficient with [VERIFY] and a brief explanation