• new

    Release 2026.06 - Bringing Data Observability Into Your Code

  • new

    Contribute to the Future of AI & Data Innovation

  • new

    • Release 2026.06 - Bringing Data Observability Into Your Code

  • new

    • Contribute to the Future of AI & Data Innovation

DATA QUALITY MANAGEMENT

Build the data quality solution your organisation needs.

digna is a data observability and data quality platform that gives you the capabilities to monitor, validate and understand your data.

Use the capabilities you need, combine them into your own monitoring solution, and apply them across your data warehouses, data lakes and lakehouses.

Continuous data quality, built around your data.

Validate explicit rules. Detect unexpected changes. Monitor data timeliness. Analyse how quality evolves over time.

digna is the platform. Your data quality solution is built on top of it.

WHAT DATA QUALITY SCENARIOS CAN YOU BUILD WITH DIGNA?

Different data quality challenges. Combine the capabilities you need.

Data quality problems are not all the same. With digna, you can combine different capabilities to address each scenario.

Does the data meet defined rules?

Data Validation

Validate data against technical and business requirements, including required values, formats, completeness, uniqueness and referential integrity.

Has the data changed unexpectedly?

Data Anomalies

Monitor data behaviour over time and identify deviations in volumes, distributions, metrics and dataset behaviour before they become operational problems.

Did the data arrive on time?

Timeliness

Detect data that has not arrived, unexpected delays and changing delivery patterns so downstream processes can rely on available data.

Is data quality improving or deteriorating?

Data Analytics

Analyse data quality metrics as time series to understand trends across datasets, sources, providers and business units.

Has the structure of your data changed?

Schema Tracker

Track structural changes in datasets and understand how schema evolution can affect the reliability of downstream data products.

ONE PLATFORM. BUILD YOUR OWN DATA QUALITY SOLUTION.

Combine capabilities instead of buying a predefined data quality package.

Every organisation has different data quality requirements. You may need strict validation rules for critical customer data, anomaly detection for transaction volumes, timeliness monitoring for daily reporting tables, and analytics to understand quality trends. Or you may need only one of these capabilities.

With digna, you decide which capabilities to use and how to combine them.

There is no requirement to adopt a predefined data quality management package.

Your workflow, your level of control

Configure and manage data quality in digna’s interface. When your team needs it, use the Python SDK to automate workflows, integrate monitoring, and extend digna programmatically.

FROM EXPLICIT RULES TO CONTINUOUS UNDERSTANDING

Cover the data quality problems you know — and the ones you do not.

Known requirements

A column must not contain missing values. A value must conform to a format. A combination of columns must be unique. Data must arrive within an expected timeframe.

Unexpected change

A dataset may suddenly contain fewer records than usual. A stable distribution may change. A metric may gradually drift away from its historical pattern. These changes may not violate a predefined rule.

From rules you define to changes you did not anticipate — digna gives you the capabilities to monitor both.

MONITOR DATA QUALITY WHERE YOUR DATA LIVES

Data quality across your enterprise data environment.

digna connects to enterprise data platforms and performs data quality monitoring directly against your data environment — without unnecessarily moving data to an external platform.

DATA QUALITY IS PART OF A BROADER OBSERVABILITY APPROACH

Understand the connection between your platform, your data and your business.

Changes in the data platform can affect the data being produced. Changes in data can affect business metrics and decisions.

Data Platform Observability

Is the data platform behaving as expected? Monitor workloads, performance, usage and cost over time.

Data Quality

Is the data correct and behaving as expected? Validate rules, detect anomalies, monitor timeliness and analyse quality changes.

Business Monitoring

Is the business behaving as expected? Monitor business metrics and KPIs derived from your data.

Build trust in your data.

Use digna to build the data quality solution that fits your organisation. Combine capabilities when you need them and expand your solution as your requirements evolve.

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