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COMPEL Body of Knowledge
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COMPEL Specialization Stream · AITM-DR

COMPEL Academy — AI Data Readiness Associate

Professional certification that certifies a practitioner can assess, remediate, and govern enterprise data for AI transformation use cases against the COMPEL data readiness criteria.

Associate Technical Track 16 hours 16 CE credits

Profession title: AI Data Readiness Associate

Audience: Data engineers, data stewards, and governance analysts preparing data for AI use cases.

AI Data Readiness Associate badge

Enroll in the AITM-DR track

Registration, enablement, and the proctored assessment are delivered through compel.one. Seats open continuously.

Register on compel.one →

Prerequisite chain

  1. AITF AI Transformation Foundations
  2. → AITM-DR (this credential)

Learning outcomes

The learning journey is sequenced to cover each outcome below in order. Every article in the journey maps to at least one outcome.

  1. 1. Run a COMPEL data readiness assessment on a candidate use case.
  2. 2. Instrument lineage, provenance, and quality controls end-to-end.
  3. 3. Remediate sensitive-data and bias risks in training data.

Body of Knowledge articles (15)

Module M1.1 (15 items)

  1. Article
    What Data Readiness Is (and What It Is Not)
    M1.1-Art01
  2. Article
    Data Quality Dimensions Extended for AI
    M1.1-Art02
  3. Article
    Data Governance and Data Contracts
    M1.1-Art03
  4. Article
    Data Lineage, Provenance, and Documentation
    M1.1-Art04
  5. Article
    Labeling Strategy and Annotation Governance
    M1.1-Art05
  6. Article
    Feature Stores and Vector Stores as Governance Artifacts
    M1.1-Art06
  7. Article
    Bias-Relevant Variables and Subgroup Coverage
    M1.1-Art07
  8. Article
    Privacy, Sensitive Data Classes, and Data Minimization
    M1.1-Art08
  9. Article
    Third-Party and Open-Source Data Readiness
    M1.1-Art09
  10. Article
    Drift Monitoring, Incident Classification, and Sustainment
    M1.1-Art10
  11. Article
    The Readiness Scorecard
    M1.1-Art11
  12. Lab
    Lab 1 — Dataset Profiling and Quality Scoring
    M1.1-Art51
  13. Lab
    Lab 2 — Data Contract and Datasheet for a RAG Source
    M1.1-Art52
  14. Case Study
    Case Study — Amsterdam SyRI and Rotterdam Welfare-Fraud Algorithm
    M1.1-Art61
  15. Template
    Template — AI Data Readiness Scorecard
    M1.1-Art71

Competencies demonstrated

  • → Data readiness assessment and scoring
  • → Lineage, provenance, and sensitive-data handling
  • → Feature governance and training-data hygiene
  • → Data quality controls through the COMPEL lifecycle

Exam blueprint summary

Assessment
Non-proctored assessment
Passing score
70% passing score
Portfolio
Not required
Renewal
Every 24 months
Recommended hours
16
CE credits
16

Linked Core Mastery context

The Specialization Stream assumes AITF Foundations fluency. These Core Mastery resources are the recommended grounding before entering the AITM-DR learning journey.

Formal credential definition

The machine-readable Open Badges 3.0 / W3C Verifiable Credential definition for AITM-DR is published at /credential/aitm-data-readiness . HR platforms and AI citation engines can fetch the JSON-LD document at /credential/aitm-data-readiness.json .