Analytics and Business Intelligence
What to know
Analytics spans descriptive (what happened), diagnostic (why), predictive (what will happen), and prescriptive (what to do). BI focuses on reporting, dashboards, KPIs, and self-service. Candidates must understand the difference between a metric (measurable value) and a KPI (a metric tied to a strategic objective), and between lagging and leading indicators. Know the semantic layer (business-friendly definitions over physical data) and why it matters for consistency.
Understand data visualization principles: choose chart type by question (comparison → bar, trend → line, distribution → histogram, relationship → scatter), avoid misleading axes, and design for the audience. Know the concepts of data storytelling, drill-down, and the trade-off between self-service and governed metrics. Familiarity with the analytics maturity curve (reporting → analysis → prediction → automation) and with the role of the analytics translator/business analyst is valuable.
Traps
- Calling every metric a KPI - KPIs must tie to strategy and be actionable.
- Confusing predictive (forecast) with prescriptive (recommendation).
- Assuming more dashboards equals more insight - governance and definitions matter more.
- Using pie charts for many categories or truncated axes that distort.
Worked rule of thumb
For chart selection, ask: Am I comparing categories, showing a trend, showing distribution, or showing a relationship? Comparison → bar; trend → line; distribution → histogram/box; relationship → scatter. This resolves most visualization questions.
For KPI questions, apply the actionability test: if the metric cannot change a decision, it is a metric, not a KPI.