1. Frequency Analysis
Count how often each value appears in a column. This reveals the most common categories, identifies data entry errors, and shows distribution. Upload to our Data Analyzer for automatic frequency tables.
2. Cross-Tabulation
Compare two categorical variables in a matrix. For example: product category × region = sales by category in each region. This reveals patterns invisible in raw data.
3. Time Series Grouping
If your data has dates, group by week/month/quarter to spot trends. Look for seasonality (recurring patterns), trends (consistent direction), and anomalies (unexpected spikes/drops).
4. Outlier Detection
Sort columns by value. Extremely high or low values may be errors or genuinely interesting cases. The IQR method: values below Q1-1.5×IQR or above Q3+1.5×IQR are outliers.
5. Deduplication
Sort by key columns, then visually scan for near-duplicates ("John Smith" vs "john smith" vs "Smith, John"). Standardize formats before analyzing to avoid counting the same entity multiple times.