Data Analysis and Reporting
Core concepts
Data analysis turns raw data into decisions. Candidates must understand data types (nominal, ordinal, interval, ratio), data quality dimensions (accuracy, completeness, consistency, timeliness, validity), and the difference between descriptive, diagnostic, predictive, and prescriptive analytics. Know basic statistics: mean, median, mode, standard deviation, correlation versus causation, and sampling bias. Understand visualization choices: bar charts for comparison, line charts for trends over time, scatter plots for relationships, and pie charts only for simple part-to-whole. Know how to define a metric with a clear numerator, denominator, and time window, and how to build a dashboard that serves a specific audience.
Traps
Questions frequently test correlation versus causation: two variables moving together does not prove one causes the other. Watch for misleading charts (truncated axes, inconsistent scales, cherry-picked time ranges). Another trap is confusing a KPI with a raw metric: a KPI must tie to a goal and be actionable. Beware of answers that recommend collecting more data when the real issue is data quality or a poorly defined metric.
Worked rule of thumb
Before reporting any number, apply the SMART metric test: is it Specific, Measurable, Actionable, Relevant, and Time-bound? Define the numerator, denominator, source, and refresh cadence. If two people computing the metric would get different results, the definition is not ready.
Example: "Customer satisfaction is up" is weak; "Average post-support CSAT rose from 3.8 to 4.2 (5-point scale, n=1,200, last quarter vs. prior quarter)" is defensible.