How This Workflow Works
This workflow analyzes the distribution of numeric fields in a dataset, calculates key statistics, and flags records with unusually high or low values compared to the rest of the data. It can be applied to any numeric data and produces both an interactive dashboard and downloadable reports for further review.
Key Features:
- Automatically validate and check the quality of numeric, string, date, and missing value fields
- Identify outliers and anomalies in numeric data using standard deviation tolerance thresholds
- Customize thresholds and field selections to fit different datasets and business needs
- Generate an interactive dashboard and detailed reports for further analysis or sharing
Step-by-step:
1. Analyze Numeric Field Distribution:
The workflow calculates summary statistics for each numeric field, including measures of central tendency and variability. This step provides a clear view of how values are distributed and highlights the overall dispersion within the dataset.
2. Validate Data Quality:
Automated checks are performed on numeric, string, date, and missing value fields to ensure data integrity. This includes identifying invalid entries, missing values, and inconsistencies that could affect the reliability of the analysis.
3. Detect Outliers Using Standard Deviation:
The workflow allows the user to dynamically adjust the standard deviation tolerance threshold to identify records with values that fall significantly above or below the mean. These outliers are flagged for further review, helping to pinpoint unexpected or potentially high-risk transactions.
4. Visualize and Share Insights:
Results are presented through an interactive dashboard and static reports. Users can explore flagged records, review summary statistics, and export findings for distribution or further investigation.