Given a scenario, translate business requirements to form a report.

• Data content
• Filtering
• Views
• Date range
• Frequency
• Audience for report – Distribution list

Given a scenario, use appropriate design components for reports and dashboards.

Report cover page – Instructions – Summary – Observations and insights

• Design elements – Color schemes – Layout – Font size and style – Key chart elements – Titles – Labels – Legends – Corporate reporting standards/style guide – Branding – Color codes – Logos/trademarks – Watermark
• Documentation elements – Version number – Reference data sources – Reference dates – Report run date – Data refresh date

Given a scenario, use appropriate methods for dashboard development.

• Dashboard considerations – Data sources and attributes – Field definitions – Dimensions – Measures – Continuous/live data feed vs. static data – Consumer types – C-level executives – Management – External vendors/stakeholders – General public – Technical experts
• Development process – Mockup/wireframe – Layout/presentation – Flow/navigation – Data story planning – Approval granted – Develop dashboard – Deploy to production
• Delivery considerations – Subscription – Scheduled delivery – Interactive (drill down/roll up) – Saved searches – Filtering

Static – Web interface – Dashboard optimization – Access permissions

Given a scenario, apply the appropriate type of visualization.

• Line chart
• Pie chart
• Bubble chart
• Scatter plot
• Bar chart
• Histogram
• Waterfall
• Heat map
• Geographic map
• Tree map
• Stacked chart
• Infographic
• Word cloud

Compare and contrast types of reports

• Static vs. dynamic reports – Point-in-time – Real time
• Ad-hoc/one-time report
• Self-service/on demand
• Recurring reports – Compliance reports (e.g., financial, health, and safety) – Risk and regulatory reports – Operational reports [e.g., performance, key performance indicators (KPIs)]
• Tactical/research report

Data Governance, Quality, and Controls

Summarize important data governance concepts.

• Access requirements – Role-based – User group-based – Data use agreements – Release approvals
• Security requirements – Data encryption – Data transmission – De-identify data/data masking
• Storage environment requirements – Shared drive vs. cloud based vs. local storage
• Use requirements – Acceptable use policy – Data processing – Data deletion – Data retention
• Entity relationship requirements – Record link restrictions – Data constraints – Cardinality
• Data classification – Personally identifiable information (PII) – Personal health information (PHI) – Payment card industry (PCI)
• Jurisdiction requirements – Impact of industry and governmental regulations
• Data breach reporting – Escalate to appropriate authority

Given a scenario, apply data quality control concepts.

• Circumstances to check for quality – Data acquisition/data source – Data transformation/intrahops – Pass through – Conversion – Data manipulation – Final product (report/dashboard, etc.)
• Automated validation – Data field to data type validation – Number of data points
• Data quality dimensions – Data consistency – Data accuracy – Data completeness – Data integrity – Data attribute limitations
• Data quality rule and metrics – Conformity – Non-conformity – Rows passed – Rows failed
• Methods to validate quality – Cross-validation – Sample/spot check – Reasonable expectations – Data profiling – Data audits

Explain master data management (MDM) concepts.

• Processes – Consolidation of multiple data fields – Standardization of data field names – Data dictionary • Circumstances for MDM – Mergers and acquisitions – Compliance with policies and regulations – Streamline data access