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Data Quality: Tools, Standards & Practice

Data quality definitions, validation tools, platforms and standards, with links to the site's frameworks and observability pages.

23 topics•4 categories•Updated November 2026

Concepts & Overviews

4 entries

What data quality is and why it matters.

01What is data quality? (TechTarget)TechTarget definition and importance of data quality. 02DAMA InternationalData management association and its body of knowledge. 03ISO 8000 (data quality)ISO data quality standard family; the ISO site blocks automated checks. 04What is data quality? (IBM)IBM's explainer on data quality dimensions and practice.

Open-source Validation Tools

6 entries

Test and monitor data in pipelines.

05Great ExpectationsOpen-source data validation framework. The project's GitHub repository now sits under the fivetran organization. 06SodaData quality checks and monitoring. 07dbt data testsTests for dbt models. Fivetran and dbt Labs completed their merger in June 2026, according to their press release. 08Deequ (AWS Labs)Library for unit tests on large datasets with Apache Spark. GitHub lists 2.1.0 as the latest release, from September 2026. 09PanderaStatistical data validation for pandas, Polars and other dataframes. 10ElementaryOpen-source data observability and tests for dbt projects.

Commercial Platforms

8 entries

Enterprise data quality products.

11Informatica Data QualityData quality product from Informatica. Informatica is now part of Salesforce, which completed its acquisition in November 2025. 12AtaccamaData quality and governance platform. 13CollibraData intelligence platform with data quality features. 14ActianData management and analytics vendor. 15BMC SoftwareIT operations and automation vendor. 16Monte CarloData and AI observability for detecting data incidents. 17BigeyeData observability and quality monitoring. 18AnomaloAutomated data quality monitoring with machine learning.

Related Pages

5 entries

Data and quality pages on this site.

19Data Quality FrameworksFrameworks for data quality. 20Data Quality SolutionsData quality solutions. 21Data ObservabilityMonitoring data freshness, quality and lineage across pipelines. 22Data Governance PlatformsData governance platforms. 23Master Data Management SoftwareMDM software.

Frequently Asked Questions

What is data quality?

Data quality is how well data fits its intended use, usually described by dimensions such as accuracy, completeness, consistency, timeliness, validity and uniqueness. For AI, poor data quality becomes poor model output, so quality checks now sit in front of training sets and retrieval indexes as well.

How do I test data quality in pipelines?

Use validation frameworks such as Great Expectations, Soda, dbt tests or Deequ to run automated checks at each stage and alert on failures.

Which standards apply?

ISO 8000 covers data quality and DAMA's body of knowledge covers data management practice.

What is new in the open-source data quality tools?

On GitHub in October 2026, Great Expectations 1.24.1 (22 October), Soda Core v4.29.0 (27 October), dbt Core v2.0.8 (14 October), Pandera v0.35.0 (25 October) and Deequ 2.1.0 (16 September) were the newest releases. Fivetran and dbt Labs completed their merger on 1 June 2026, and the dbt Fusion runtime is being open sourced as dbt Core v2.0 under the Apache 2.0 license.

What does the 2026 Gartner Magic Quadrant for Augmented Data Quality Solutions say?

The 2026 Magic Quadrant for Augmented Data Quality Solutions is dated 11 February 2026 in IBM's citation, and Qlik says Gartner evaluated 13 vendors. Ataccama (fifth consecutive Leader placement, ranked first for Completeness of Vision), Salesforce's Informatica (18th time), Qlik (seventh year) and IBM each say they were named Leaders; the other vendors' placements could not be confirmed. Ataccama's summary says AI assistants are now formally evaluated, innovation explicitly covers generative and agentic AI automation, alerts and visualisation are a mandatory capability, unstructured data scope adds graph technologies for relationships between extracted entities, and "AI/ML development" is renamed "Analytics and AI readiness".