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Profiling Statistics Reference

What a Statistic Is

Every digna module is built on the same foundation: a small set of statistics computed per dataset and per column, in-database, on each inspection.

Profiling is the one step that always runs — it is not gated behind a module flag. The modules then differ only in what they do with the numbers profiling produced:

Module What it does with the statistics
Data Anomalies Predicts the next value and compares the observation against it
Data Analytics Fits trend and volatility over a window of past values
Data Volume Reports row counts at dataset and data source level (always on)

Because the statistics are shared, enabling a second module costs no extra profiling work on the source.

The Statistics

Statistic Group Level SQL it becomes
Row Count Data volume Table count(*)
Null Count Missing values Column count(CASE WHEN col IS NULL THEN 1 END)
Sum Numerical metrics Column sum(col)
Average Numerical metrics Column avg(col)
Minimum String Length Text length Column min(length(col))
Average String Length Text length Column avg(length(col))
Maximum String Length Text length Column max(length(col))
Count Distribution Column count(col), grouped by the column's own value
Number of Unique Values Distribution Column count(distinct col)
Number of Negative Values Distribution Column count(CASE WHEN col < 0 THEN 1 END)
Number of Zero Values Distribution Column count(CASE WHEN col = 0 THEN 1 END)
Number of Positive Values Distribution Column count(CASE WHEN col > 0 THEN 1 END)

The SQL above is the PostgreSQL rendering. Each supported technology carries its own template set, so the same statistic is expressed in the dialect of Teradata, Snowflake, Databricks, Oracle, MS SQL Server, Netezza, Hive, or Impala as appropriate — but the meaning is identical across all of them.

Count is the distribution statistic

Count is the only statistic that also groups by the value of the column itself, producing one measured value per distinct category. That is what lets digna learn the shape of a categorical column and flag an unexpected member — not just how many non-null values it has.

Attribute Categories Decide the Default Set

When an attribute (column) is added to a data source it is given a category, and the category determines which statistics are switched on automatically:

Category Typical content Statistics enabled by default
Numerical Amounts, quantities, measurements Null Count, Sum, Average
Categorical Discrete labels, types, classifications Null Count, Count
Unspecified Free text, IDs, mixed or unstructured values Null Count, Average String Length, Number of Unique Values
Custom Anything needing a specific set Exactly the statistics you select — nothing is inferred

Unspecified is the default for a column that is neither clearly numerical nor clearly categorical.

Row Count sits outside this table. It is table-level, belongs to no category, and is always computed.

Use Custom when the defaults do not fit — for example to add Minimum and Maximum String Length to a code column where a width change matters, or Number of Negative Values to an amount column that should never go below zero.

The column-swap signal

A swapped first_name / last_name pair produces no null, no row-count change, and no rule violation. What moves is Average String Length on both columns at once. Categorising those columns as Unspecified — or adding the string-length statistics through Custom — is what makes that failure visible.

Positive Statistics

Each statistic carries a flag saying whether it can legitimately be negative. All of them are non-negative by construction except Sum and Average, which follow the data.

For the non-negative ones, digna floors the whole predicted band at zero, so a prediction interval never suggests that a count could be below zero. For Sum and Average it does not, because a negative total is a real possibility.

Turning Statistics Off

Not every column deserves every statistic. Each combination of table + column + statistic is a mapping that can be switched off individually, and switching one off removes it from anomaly detection without removing the profiled value.

The same mapping also carries four optional numeric limits used by Data Anomalies:

Setting Effect
lower_limit / upper_limit Clamp the predicted value and its bands into a range you know the metric cannot leave
min_threshold / max_threshold Put a floor and a ceiling on the width of the tolerance band

min_threshold is the practical answer to a metric that is almost perfectly stable: without a floor, a series that has been identical for weeks produces a near-zero band, and the first ordinary fluctuation is reported. max_threshold is the opposite guard, for a noisy series whose band would otherwise grow wide enough to hide a genuine problem.