In Qlik Sense, the concepts of subset ratio and information density are crucial tools for understanding the quality and reliability of data in a visualization application. Here's what they are used for:
Subset Ratio
The subset ratio is a measure used to identify the proportion of distinct values in one data field relative to another related field. It is primarily used to detect anomalies and gaps in the data. For example, if you have two data tables linked by a common field, the subset ratio tells you the proportion of values from the primary table field that are present in the secondary table field.
Usefulness:
- Anomaly Detection: A low subset ratio can indicate anomalies, such as missing data or mismatches between tables.
- Performance Optimization: By identifying gaps and inconsistencies, you can optimize your data models to improve query and analysis performance.
- Data Quality Improvement: It helps ensure data integrity by highlighting areas that need special attention for data cleaning and adjustment.
Information Density
Information density is a measure that evaluates the proportion of non-null values to the total number of values in a data field. It is an indicator of data completeness.
Usefulness:
- Data Quality Evaluation: High information density indicates well-populated and complete data, which is crucial for reliable analyses.
- Missing Data Detection: It helps identify fields where data is often missing, allowing you to focus efforts on data collection and improvement.
- Dashboard Design: When designing visualizations, knowing which fields have high information density can guide the choice of fields to use for accurate and representative analyses.
Conclusion
In summary, the subset ratio and information density are essential indicators in Qlik Sense for ensuring the integrity, quality, and efficiency of data analyses. They help detect anomalies, optimize performance, and ensure that your data is complete and reliable for fact-based decision-making.
