CDMP study plan
CDMP · Give each week a clear focus.
8 weeks · About 7 hours per week
Data Management Fundamentals (DMF)
Define each of the eleven data management knowledge areas in your own words and state one business problem each one solves
Map the data management lifecycle stages to a real organization you know, naming who owns each stage
Distinguish data management from data governance, data architecture, and IT operations with two concrete examples each
Explain the difference between data, information, and knowledge and apply it to a single dataset of your choice
Your checkpoint
A one-page concept map linking all eleven knowledge areas to lifecycle stages, with a short written definition for each area
Data Modeling and Design
Draw conceptual, logical, and physical models for the same small business scenario and annotate what changes at each level
Apply normalization rules from first through third normal form to a denormalized sample table and document each fix
Build an entity-relationship diagram using both Chen and crow's-foot notation, including cardinality and optionality
Explain when denormalization is justified and list three trade-offs against write integrity
Your checkpoint
A three-level model set (conceptual, logical, physical) for one scenario, plus a normalization worksheet showing before and after states
Metadata & Data Quality
Classify metadata into business, technical, and operational categories using ten examples from a system you can describe
Draft a metadata repository entry template covering name, definition, owner, steward, source, and lineage
Define six data quality dimensions and write one measurable test for each against a sample dataset
Distinguish data profiling, cleansing, and monitoring, and place each in a continuous quality cycle
Identify root causes of three common defect types and propose a prevention control for each
Your checkpoint
A metadata dictionary of at least fifteen fields with definitions and owners, plus a data quality scorecard template covering six dimensions
Data Governance
Describe the operating model choices for a data governance program and justify one for a mid-size organization
Write role descriptions for data owner, data steward, and data custodian that separate accountability from execution
Draft a decision-rights matrix for three data-related decisions such as access approval and definition changes
Explain how a data governance council escalates and resolves cross-functional conflicts
Connect governance to policy, standard, and procedure with one example of each
Your checkpoint
A two-page governance charter with operating model, roles, decision rights, and an escalation path
Data Warehousing & Business Intelligence
Compare Inmon, Kimball, and data vault approaches on structure, purpose, and change tolerance
Design a star schema with one fact table and three dimensions, choosing grain and surrogate keys deliberately
Explain slowly changing dimension types 1, 2, and 3 and state when each is appropriate
Distinguish OLTP from analytical workloads and describe how that shapes physical design
Outline the path from source data to a business intelligence report, naming each transformation stage
Your checkpoint
A star schema diagram with documented grain, key strategy, and SCD handling, plus a one-page comparison of the three warehouse approaches
Reference and Master Data Management
Distinguish reference data from master data and give three examples of each from different industries
Describe the four common master data management implementation styles and their trade-offs
Explain how entity resolution, matching, and survivorship work together to produce a golden record
Identify three sources of duplicate and conflicting master records and a control for each
Outline how reference data sets are governed, versioned, and distributed to consuming systems
Your checkpoint
A master data design brief for one domain such as customer or product, covering style choice, matching rules, survivorship logic, and governance touchpoints
Data Integration and Interoperability
Compare batch, change data capture, and real-time integration patterns on latency, cost, and complexity
Describe extract, transform, load versus extract, load, transform and state when each fits
Explain how a data integration architecture supports lineage and metadata capture
List interoperability standards relevant to your sector and describe what problem each solves
Identify three failure modes in integration pipelines and the monitoring control that detects each
Your checkpoint
An integration architecture diagram for one data flow, annotated with pattern choice, latency target, error handling, and lineage capture points
Data Management Fundamentals (DMF) & Data Governance
Rehearse the full knowledge-area map and correct any weak areas found in a timed self-test
Synthesize governance and fundamentals into one argument for how a program matures over time
Write concise answers to ten scenario prompts that combine at least two domains each
Review every prior deliverable and note the three concepts you still explain least confidently
Your checkpoint
A timed practice exam result log with a written remediation note for every missed item, plus a one-page synthesis of the full program lifecycle
Milestones
Week 2: You can sketch conceptual, logical, and physical models for a new scenario in under twenty minutes and explain each normalization step aloud without notes
Week 4: You can state the difference between metadata and data quality concerns for the same field, and your governance charter names a decision owner for every listed decision
Week 6: You can choose a warehouse approach and an MDM style for a described scenario and defend both choices against two alternatives with trade-offs
Week 8: You score at least 80 percent on a timed mixed-domain self-test and can explain every missed item in your own words without referring to notes