ICCP CDP study plan

Define and contrast the core data management disciplines (data quality, metadata, master data, reference data, and data lifecycle) in your own words

Diagram the data lifecycle from capture through archival and identify the control point at each stage

Classify ten common data quality dimensions and write one detection rule for each

Explain the difference between master data and reference data using two concrete business examples

Compare relational, dimensional, and non-relational storage models on structure, integrity enforcement, and query patterns

Describe how metadata repositories, data dictionaries, and glossaries differ in purpose and audience

Walk through a data integration scenario and select an appropriate pattern (ETL, ELT, CDC, or virtualization) with justification

Identify three data security and privacy controls and state which lifecycle stage each protects

Build a star schema for a given fact-and-dimension scenario, choosing grain and conformed dimensions explicitly

Distinguish Type 1, Type 2, and Type 3 slowly changing dimension handling and pick one for a stated requirement

Compare Inmon, Kimball, and Data Vault approaches on structure, agility, and auditability

Explain columnar storage, partitioning, and clustering effects on warehouse query performance

Differentiate descriptive, diagnostic, predictive, and prescriptive BI use cases and match each to a delivery mechanism

Design a semantic layer or metric definition approach that prevents conflicting KPI calculations

Evaluate dashboard design choices against readability, performance, and self-service goals

Describe batch versus streaming delivery trade-offs for near-real-time reporting requirements

Draft a data governance operating model naming decision rights for data owners, stewards, and custodians

Write three policy statements (retention, access, quality) that are testable rather than aspirational

Map a data quality issue from detection through root cause to remediation with assigned accountability

Distinguish data governance from data stewardship and from IT data administration

Define three data quality KPIs with thresholds and measurement frequency

Explain how data lineage and metadata management support regulatory and audit requests

Identify privacy and consent obligations that constrain analytics use of personal data

Describe how a data catalog enables stewardship workflows and issue tracking

Frame a business problem as an analytics question with a defined target variable and success criterion

Distinguish supervised from unsupervised methods and select an appropriate technique for three scenarios

Explain overfitting, train/test splitting, and cross-validation to a non-technical stakeholder

Interpret common evaluation metrics (accuracy, precision, recall, RMSE, lift) and state when each misleads

Describe how analytics results feed back into governance and data quality priorities

Synthesize all four domains into an integrated end-to-end data program narrative

Complete timed practice sets per domain and log every missed concept with a corrective note

Re-derive the weakest ten concepts from memory without notes

Review governance-to-analytics handoffs, since questions often bridge domains