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Profiling – The Foundation

What Profiling Is

Profiling is the step where digna measures your data.

It reads each data source and computes a set of numbers describing it — how many rows arrived, how many values were missing, what the totals and averages were, how the values in a categorical column were distributed. Those numbers are called statistics, and they are the raw material two of the digna modules are built on.

Profiling is not something you schedule, license, or run separately. It is part of running the modules that need it. When digna Data Anomalies or digna Data Analytics is enabled on a data source, the inspection profiles that data source as part of its work — there is no second job to configure and no extra pass over your data.

Who Reads the Statistics

Module Relationship to profiling
Data Anomalies Built on it — predicts the next value of each statistic and compares the observation against it
Data Analytics Built on it — fits trend and volatility across a window of past statistic values
Data Validation Independent — evaluates its own rules row by row and does not read the statistics
Timeliness Independent — concerned with when data arrived, not what is in it
Schema Tracker Independent — reads the database catalogue, not the data

This is why understanding profiling is worth the few minutes: it explains what Data Anomalies and Data Analytics can and cannot see. Both modules are exactly as good as the statistics they were given. A column with no statistics enabled is a column those two modules cannot say anything about — however obviously wrong its contents may be.

The other three modules stand on their own. A data source can run Data Validation, Timeliness, or Schema Tracker without anomaly detection at all.

The practical consequence

If Data Anomalies is not catching something you expected it to catch, the first question is not "how do I tune it?" but "is there a statistic that would have moved?" A swapped first_name / last_name pair changes no row count and creates no null — it changes string lengths. If the string-length statistics are not enabled on those columns, no amount of tuning will find it.

What Profiling Produces

Every inspection, for every data source, profiling produces:

  • A row count for each dataset and for the data source as a whole. This always happens, for every data source, with no module enabled at all.
  • The enabled statistics, computed per dataset and per column, for the data sources whose modules need them.

Both are stored with the date they describe, so each one becomes a point in a time series. A single measurement is not interesting; the series is what allows a prediction, a trend, or a volatility figure.

See Statistics for the full list of what can be measured, and Datasets for how one table can be split into several independently measured subsets.

Your Data Stays Where It Is

Profiling is executed inside your database. digna sends SQL and receives numbers.

What comes back is aggregates — a count, a sum, an average, a count per category. No rows are copied out, and no raw values are transmitted or stored by digna. The one exception is deliberate and user-initiated: when a validation rule fails, you can ask to see the records that failed it, because a count alone is not something anyone can act on.

This is the property that makes digna usable on regulated data — clinical records, transaction data, personal data under GDPR — and it is a consequence of the architecture rather than a policy layered on top. digna is installed on-premises or in your private cloud, so there is no external service for data to be sent to.