Data Governance, Databases, and Advanced Analytics

This part focuses on how data is created, stored, governed, integrated, and analyzed. The exam value here comes from understanding what makes data trustworthy enough for reporting, control evaluation, and assurance use.

In This Part

• Chapter 11: Data Life Cycle and Governance explains ownership, stewardship, retention, and control over data through time.
• Chapter 12: Database Structures and Administration introduces how data is organized and maintained.
• Chapter 13: Data Warehousing and Big Data Environments covers broader data environments and their control implications.
• Chapter 14: Data Integration and Analytics connects data movement to analysis and decision support.
• Chapter 15: Business Process Modeling and Improvement shows how process review and redesign affect system quality and control effectiveness.

How to Use This Part

• Read this part after Part II so the architecture context is already established.
• Focus on integrity, completeness, governance, and auditability, not just technical terminology.
• Return here when an ISC question turns on whether data can be trusted or used appropriately.

In this section

• Data Governance Across the Data Life Cycle
  Data life cycle governance, classification, metadata, retention, and destruction.
  • Data Creation, Active Use, Archival, and Final Disposition Controls
    Data creation, active use, archival, and final disposition controls.
  • Data Classification Levels, Sensitivity Tiers, and Metadata Management
    Data classification tiers and metadata management.
  • Data Retention Schedules, Destruction Policies, and Compliance
    Data retention schedules, destruction policies, and compliance.
• Database Structures, Query Logic, and Security Administration
  Database structures, query logic, integrity controls, and security.
  • Relational Databases, Schemas, Normalization, and Redundancy Control
    Relational databases, schemas, normalization, and redundancy control.
  • SQL Queries, Clauses, Joins, and Common Operators
    SQL queries, clauses, joins, and common operators.
  • Data Dictionaries, Referential Integrity, and Validation Controls
    Data dictionaries, referential integrity, and validation controls.
  • Database Security, Encryption, Privileges, and User Access
    Database security, encryption, privileges, and user access.
• Data Warehouses, ETL, and Big Data Governance
  Data warehouses, ETL, diverse data types, and big-data governance.
  • Data Warehouses, Data Lakes, Data Marts, and Their Use Cases
    Data warehouses, data lakes, data marts, and their use cases.
  • ETL Processes, Transformation Risk, and Related Controls
    ETL processes, transformation risk, and related controls.
  • Managing Structured, Semi-Structured, and Unstructured Data
    Managing structured, semi-structured, and unstructured data.
  • Big Data Governance Challenges, Privacy, and Accountability
    Big data governance challenges, privacy, and accountability.
• Data Integration, Visualization, and Analytical Control Risk
  Data integration, dashboards, predictive models, and analytics risk.
  • Combining Disparate Data Sources for Reliable Analysis
    Combining disparate data sources for reliable analysis.
  • Visualization Tools, Reporting Dashboards, and Insight Communication
    Visualization tools, dashboards, and insight communication.
  • Predictive Analytics, Machine Learning, and AI Fundamentals for CPAs
    Predictive analytics, machine learning, and AI fundamentals for CPAs.
  • Control Risks in Data Analytics and Continuous Monitoring
    Control risks in data analytics and continuous monitoring.
• Process Modeling, Bottleneck Analysis, and Automation
  Process modeling, bottleneck analysis, control gaps, and automation.
  • BPMN Use Cases for Process Documentation and Analysis
    BPMN use cases for process documentation and analysis.
  • Process Bottlenecks, Control Gaps, and Improvement Opportunities
    Process bottlenecks, control gaps, and improvement opportunities.
  • Process Automation, Efficiency Gains, and Implementation Metrics
    Process automation, efficiency gains, and implementation metrics.