Information Systems, Data Analytics and Digital Transformation
What this domain tests
This domain tests how information systems support accounting and control, how data analytics creates value, and how digital transformation changes risk and the finance function.
Core rules
- ERP systems integrate modules on a single database, improving data consistency but creating implementation, change-management and vendor-lock-in risks.
- COBIT governs IT; ITIL governs service management. IT general controls cover access, change management and operations; application controls cover input, processing and output.
- Analytics types: descriptive (what happened), diagnostic (why), predictive (what will happen) and prescriptive (what to do).
- Data quality dimensions: accuracy, completeness, timeliness, consistency and validity. Poor data produces unreliable models (“garbage in, garbage out”).
- Digital transformation: RPA, AI/ML, cloud, blockchain and IoT. Model risk, data privacy (GDPR) and cybersecurity become board-level concerns.
Worked example
A retailer wants predictive analytics for inventory. Define the question; source data from ERP and CRM; test completeness and accuracy; build and validate the model on held-out data; deploy with version control and human override. Access to the model should be restricted, and outputs monitored for drift. Skipping data-quality checks or overfitting to history will produce confident but wrong forecasts.
Traps
Treating analytics as an IT-only project; ignoring data quality; assuming RPA fixes a poorly designed process; confusing IT general controls with application controls; and overlooking model risk and privacy obligations.