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Profiling Metrics and Engine Output

How dataset overview, type inference, missing values, duplicates, outliers, and quality flags should be interpreted.

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Profiling Metrics And Engine Output

After the source is accepted, the tool builds a profile from the CSV and optional descriptor context.

Core quality dimensions

Data Profiling prioritizes the signals that most often affect downstream analysis and decision confidence.

Dataset Overview

The overview answers the first operational questions:

  • How many rows and columns are present?
  • How many cells are missing?
  • What percentage of rows are duplicates?
  • How many columns are numeric, categorical, datetime, identifier, text, boolean, or unknown?
  • How many columns carry quality flags?
  • How many potential outlier cells were detected?

These values are not the final interpretation. They are triage signals that tell the team where to inspect next.

Column Type Inference

Every column receives an inferred type. Type inference allows the profile to choose appropriate statistics. A numeric field can receive min, max, mean, standard deviation, quartiles, median, p95, and outlier counts. A datetime field can receive min date, max date, date range, and unique count. Text fields emphasize length and uniqueness. Categorical fields emphasize top values and cardinality.

Column type interpretation map

Range, mean, median, p95, outliers

Numeric · Useful signals

Skew, units, impossible values

Numeric · Typical risk

Check bounds and business meaning

Numeric · Action

Top values, unique count, rare levels

Categorical · Useful signals

Mixed labels or unexpected categories

Categorical · Typical risk

Normalize categories before analysis

Categorical · Action

Min date, max date, date range

Datetime · Useful signals

Timezone, stale records, gaps

Datetime · Typical risk

Confirm period and update cadence

Datetime · Action

Unique percent and duplication pattern

Identifier · Useful signals

False keys or join collisions

Identifier · Typical risk

Validate key rules before merging

Identifier · Action

Each inferred type has different useful statistics and different failure modes.

Missing Values

Missingness is tracked at dataset and column level. The profile can also capture special missing counts when the engine recognizes values that often mean missing, such as blank-like or sentinel values.

Interpret missingness in context:

  • small missingness may be acceptable for descriptive analysis
  • high missingness can make a column unusable
  • systematic missingness can reveal process gaps
  • missingness concentrated in one group or time period can bias conclusions

Duplicates

Duplicate rows can mean repeated events, data export issues, or legitimate repeated records. Profiling reports the duplicate count and percentage, but the domain owner must decide whether duplicates are errors.

Outliers And Extremes

Outlier counts highlight records outside expected distribution ranges. Outliers are not automatically wrong. They may be rare but valid cases, data-entry errors, unit problems, or high-value signals.

Quality Flags

Quality flags are short warnings attached to columns. They help reviewers scan for likely issues such as high missingness, high cardinality, constant values, possible identifiers, unusual distributions, or sparse categories.

Use flags to prioritize review. Do not delete or transform data solely because a flag exists.