Introduction to Business Data Analytics
Business data analytics is the use of data, statistical methods and software to improve decisions. The exam tests the analytics maturity ladder, the decision context and governance rather than coding.
Core types
- Descriptive analytics explains what happened through dashboards, KPIs and variance reports.
- Diagnostic analytics explains why, using drill-down, correlation and root-cause analysis.
- Predictive analytics forecasts outcomes with regression, classification or time-series models.
- Prescriptive analytics recommends actions using optimisation, simulation or decision rules. A useful sequence is question → data → model → validation → decision → monitoring. Analytics differs from reporting because it supports a decision, not just a historical snapshot.
Worked example
A retailer sees a 5% fall in monthly sales. Descriptive analytics shows the fall is concentrated in two regions. Diagnostic analysis finds stock-outs after a supplier delay. A predictive model forecasts next month’s demand by SKU. Prescriptive analysis reallocates inventory and sets reorder points. The value comes from the linked decision, not from the dashboard alone.
Traps
Do not confuse correlation with causation; do not build a model without a decision owner; do not ignore data quality and selection bias; and do not treat a complex model as automatically better than a simple rule.