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CATAPA Apps

Business Flow

This document illustrates the business flow for the Digital Employee Quality-of-Hire Analyst system. The workflow covers how a user requests quality-of-hire insights (by university/education and prior experience) and how the DE clarifies parameters, retrieves HRIS-only data from CATAPA, computes retention and performance proxy metrics, and returns ranked segment outputs with coverage notes.

The process begins when the user asks a question such as “Which university produces the best outcomes?” The DE then confirms the hire cohort time range (start-date based), the retention window, and which performance proxy method to use (KPI / promotion proxy / salary proxy, default combined). After retrieving the required datasets from CATAPA HRIS and the KPI custom table, the DE normalizes education and experience dimensions, calculates retention and performance outcomes, and returns a ranked summary table per segment with sample size and data coverage notes. The user can iterate by changing filters, time ranges, or segmentation dimensions.

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Flow Description (End-to-End)

  1. User Request User requests quality-of-hire analysis (e.g., “Which university produces the best outcomes?” / “How many hires joined from Campus A?”).

  2. Intent & Output Type Identification DE identifies whether the user wants:

    • Ranked analysis (best/worst segments), or

    • Count-only output (how many hires from X), or

    • Both.

  3. Hire Cohort Time Range Clarification (Start Date Based) DE checks whether a time range is provided for hires.

    1. Smallest: 6 months.

    2. Largest: 5 years. If missing, DE uses the default last 1 year and states it.

  4. Metric Clarification: Retention / Performance DE determines whether the user wants:

    1. Performance only, retention only, or both (default both).

  5. Retention Window Clarification DE determines retention window: 90/180/365 days (default 180 days).

  6. Performance Definition Clarification (Proxy Method Selection) DE determines which performance method to use:

    1. KPI custom table metric, and/or

    2. Promotion history proxy, and/or

    3. Salary increase proxy. If not specified, default = all combined and state it.

  7. Scope & Filters Clarification (Optional) DE checks for optional filters if mentioned or needed: job title/level, location, company/legal entity, and “experience” definition (years band vs last industry vs last role family). If “experience” is ambiguous, DE asks; default = “All”.

  8. Data Retrieval (CATAPA HRIS + Custom KPI Table/Data) DE retrieves cohort and required datasets from CATAPA based on the hire time range and selected metrics:

    • Employee & Employment (current).

    • Hiring data.

    • Employment Data (historical) for promotion events.

    • Termination data (for retention outcome).

    • Education history (historical) + education master data.

    • Job experience history (historical).

    • Salary decree (historical) if salary proxy is selected.

    • Custom KPI data (historical) if the KPI method is selected.

  9. Normalization (Education / Experience Dimensions) DE normalizes:

    1. Institution name (aliases).

    2. Field of study (aliases).

    3. Education level (aliases).

  10. Computation: Retention DE computes the retention rate for the selected window (90/180/365 days) for the hire cohort.

  11. Computation: Performance Proxy DE computes performance using selected methods (or the combined default):

    1. KPI-based average score (1–5) across employee lifetime (per spec).

    2. Promotion proxy (promotion events treated equally; based on Employment Status = “Promotion”).

    3. Salary increase proxy (frequency + magnitude; penalties/deductions worsen if included in your proxy definition). DE produces a coverage note (e.g., KPI data availability %).

  12. Segmentation, Ranking, and Aggregation DE groups results by requested segmentation(s):

    1. University / degree / major,

    2. Experience (as clarified),

  13. Result Packaging DE outputs ranked tables/list including:

    1. Segment name, employee count (N), retention rate, performance metric(s).

    2. Coverage/confidence notes (sample size + KPI coverage).

    3. Safe interpretation note: observed outcomes only, not causal claims.

  14. Delivery & Iteration DE sends the final analysis to the user. The user may iterate (change time range, retention window, segmentation, filters, or performance method), and DE reruns the flow with updated parameters.

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