Profiling tab

The Profiling tab is automatically generated for every connected table in the DQC Platform. It provides an at-a-glance view of column-level statistics and correlations — helping you identify patterns, spot issues, and define meaningful data quality rules.

What the profiling tab shows

For each column, the tab displays summary statistics:

• Null values

• Uniqueness

• Minimum

• Maximum

• Average

• PII

This gives you a clear first impression of the data’s shape and health — and helps identify potential rule candidates like "Not empty", "No outlier", or "Categorical".

Key stats like NULL count and value ranges appear per column

Correlation matrix

Below the statistics table, you'll find a correlation matrix showing the relationship between columns:

• Yellow = positive correlation

• Blue = negative correlation

This is particularly useful when identifying potential multi-column rules, such as "Start Date should be before End Date" or "Awarded Amount = Consumed + Remaining".

Visualizes how strongly each column is related to others

Example use case

Scenario: You’re exploring a new table and want to define initial data quality rules based on existing patterns.

Solution:
Use the Profiling tab to identify:

• Columns with high NULL rates → create a "Not empty" rule

• Columns with low uniqueness → consider "Categorical"

• Fields with strong correlations → define a multi-column rule (e.g. Awarded Amount = Consumed + Remaining Amount)

Notes

• The Profiling tab is read-only

• Use it to quickly spot columns that require rules

• Learn more: Overview of existing rules, Rule prediction and how it works, Working with the Rules tab