CDGP study plan

Define data governance and distinguish it from data management, listing at least six driving forces (regulatory, reputational, operational) behind governance programs

Diagram the operating model components - data governance office, data stewardship, data owners, data custodians, and a governance council - and assign decision rights to each

Draft a one-page RACI showing who decides, who is accountable, and who is consulted for three governance scenarios (new data domain, policy exception, data issue escalation)

Explain how data policies, standards, and procedures cascade from principle to executable control, using two examples

Identify the metrics a governance program reports to executives and explain how each ties to business value

Describe the core data-governance artifacts (policy, standard, procedure, guideline, work product) and classify twelve sample governance statements into the correct artifact type

Explain data stewardship models and contrast centralized, decentralized, and hybrid models on cost, speed, and consistency

Define data quality dimensions including completeness, accuracy, validity, timeliness, consistency, and uniqueness, and write a testable definition for each

Distinguish root-cause analysis from data-profiling activities and outline a five-step quality improvement loop

Map three common governance failures (no executive sponsor, unclear ownership, unenforced standards) to concrete mitigation actions

Compare enterprise, conceptual, logical, and physical architecture layers and state the purpose and audience of each

Explain at least four architecture patterns (hub-and-spoke, data lake, data warehouse, data fabric/mesh) and state one trade-off for each

Build logical and physical models for a single business process, applying normalization to third normal form and justifying deliberate denormalization

Apply naming conventions, primary/foreign key, cardinality, and optionality rules correctly to a model of at least six entities

Distinguish dimensional modeling concepts (fact, dimension, grain, slowly changing dimension Type 2) and sketch a star schema for a stated business question

Design a data-quality scorecard with at least six measures, each with a threshold, tolerance band, and named accountable owner

Write the steps to profile a dataset for completeness, uniqueness, and referential integrity and interpret the outputs

Distinguish data cleansing, standardization, enrichment, and deduplication and choose the right technique for four scenario descriptions

Explain the cost-of-poor-quality argument and quantify it for a hypothetical case using rework, compliance, and lost-revenue drivers

Design an incident workflow from detection through root cause, remediation, and preventive control

Classify metadata into business, technical, and operational categories and assign ten sample assets to the right category

Explain the role of a business glossary, data dictionary, and metadata repository and describe how they interlock

Trace data lineage across source, ETL, and reporting layers for one reported metric and document each hop

Describe how metadata supports data quality monitoring, impact analysis, and regulatory response

Identify the governance controls needed to keep a glossary current (ownership, approval workflow, change management)

Compare metadata-management operating models (centralized repository versus distributed with federated access) and state selection criteria

Explain metadata standards and interchange considerations between tools, including why a shared model prevents silos

Define metadata quality attributes and describe how to measure glossary coverage and lineage completeness

Describe how metadata enables self-service analytics and the risks of stale or unowned metadata

Prepare a metadata repository requirements outline covering search, versioning, access control, and integration

Define each of the data-management knowledge areas and state how they interact within the data-management lifecycle

Explain the data lifecycle stages from planning and design through archiving and purging, and name the control needed at each stage

Distinguish data management roles (data owner, steward, custodian, architect, DBA) and their decision rights

Explain data security and privacy fundamentals (classification, access control, masking, retention) and connect them to governance policy

Describe the DAMA-style environmental elements (goals, principles, organizational culture) that shape a data-management program

Complete two full-length timed practice exams and record scores per domain

Review every missed item and write a one-line correction note linking it to the underlying concept

Re-derive the highest-error domain's core frameworks from memory without notes

Consolidate a one-page formula-and-definitions sheet covering quality measures, architecture layers, and metadata categories

Build a final 48-hour review checklist ordered by weakest domain first