Learn Data Analytics Fundamentals: From Questions to Insights
Start with a question
A useful analysis begins with an answerable question. Instead of asking ‘How are sales doing?’, define a period, measure, and comparison, such as which product categories contributed most to a monthly change. A clear question identifies the data needed and the decision the work should support.
The analytics lifecycle
- Define: Understand the need and agree on what a useful answer looks like.
- Collect: Locate sources and understand how data was recorded.
- Prepare: Check missing values, duplicates, inconsistent labels, and incorrect formats.
- Analyze: Summarize and compare data with appropriate methods.
- Communicate: Explain findings, assumptions, limitations, and next steps.
Check data quality
Review row counts, date ranges, categories, missing fields, and duplicate records. Keep source data and document corrections so the work can be reviewed. A small data issue can change a total or create a misleading pattern.
Observation versus insight
An observation describes a pattern, such as revenue rising in one month. An insight adds context by checking categories, time periods, or business events. Avoid claiming causation unless the analysis supports it. State what the data shows and what still needs investigation.
Practice
With sales data containing date, category, quantity, unit price, and region, write a focused question. Check data quality, calculate revenue as quantity multiplied by unit price, and summarize by category. Write two supported observations and one limitation.