What does the quality score mean?

A single-glance summary of completeness and consistency: missing cells, duplicates, and type conflicts lower it. Use it to compare before/after cleaning runs, not as a certified audit.

Which columns should I chart?

Numeric columns become measures; low-cardinality text and date columns become categories or axes. The Profiler's cardinality counts tell you which is which.

Documentation

Profiling & analyzing data

Audit quality before you clean, chart, or present.

Quick answer

Load the file in the Data Profiler to get column types, missing counts, duplicate counts, cardinality, and distribution stats. Use the findings to decide what to clean, filter, or visualize next.

Steps

Open Tools → Data Profiler and load the file.
Review detected column types and the per-column missing counts.
Check cardinality and duplicate counts for key identifier columns.
Note skew, outliers, and distribution hints for numeric columns.
Act on the findings: clean, filter, or move straight to charts.

Tips

Profile raw files first, then re-profile after cleaning to prove the fix.
High-cardinality text columns make poor chart categories — prefer low-cardinality ones.

Common questions

What does the quality score mean?: A single-glance summary of completeness and consistency: missing cells, duplicates, and type conflicts lower it. Use it to compare before/after cleaning runs, not as a certified audit.
Which columns should I chart?: Numeric columns become measures; low-cardinality text and date columns become categories or axes. The Profiler's cardinality counts tell you which is which.

Use it in

Back to documentation Cleaning data Removing duplicates Handling missing values

Documentation

Profiling & analyzing data

Audit quality before you clean, chart, or present.

Quick answer

Load the file in the Data Profiler to get column types, missing counts, duplicate counts, cardinality, and distribution stats. Use the findings to decide what to clean, filter, or visualize next.

Steps

Open Tools → Data Profiler and load the file.
Review detected column types and the per-column missing counts.
Check cardinality and duplicate counts for key identifier columns.
Note skew, outliers, and distribution hints for numeric columns.
Act on the findings: clean, filter, or move straight to charts.

Tips

Profile raw files first, then re-profile after cleaning to prove the fix.
High-cardinality text columns make poor chart categories — prefer low-cardinality ones.

Common questions

What does the quality score mean?: A single-glance summary of completeness and consistency: missing cells, duplicates, and type conflicts lower it. Use it to compare before/after cleaning runs, not as a certified audit.
Which columns should I chart?: Numeric columns become measures; low-cardinality text and date columns become categories or axes. The Profiler's cardinality counts tell you which is which.

Use it in

Back to documentation Cleaning data Removing duplicates Handling missing values

Profiling & analyzing data

Quick answer

Steps

Tips

Common questions

Use it in

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Profiling & analyzing data

Quick answer

Steps

Tips

Common questions

Use it in