Best Data Quality Tools (2026): Top 10 Compared

Soda describes itself as a data quality platform that gives data teams a shared workflow to monitor quality, catch problems early and act on them. Its documentation splits the work into two approaches. Data observability is reactive: ML-powered monitoring of metrics such as freshness, row counts and null values, with alerts when values move away from historical norms. Data testing is proactive: checks on known expectations that run during development or in CI/CD so that bad data does not reach production.

Data contracts connect the two. They make expectations about a dataset explicit and let the teams that produce data and the teams that consume it agree on them at the source. Soda's product pages also list record-level diagnostics, lineage and impact analysis, automated record-level resolution and pipeline fixing, an MCP server and use cases including cloud migration reconciliation and BCBS 239 compliance.

Soda publishes its prices, which is uncommon in this category.

Anomalo sells automated data quality monitoring built on unsupervised machine learning. Its AWS Marketplace listing describes checks that automatically identify missing and anomalous data and that look inside tables rather than only at their metadata, configured either without code or through an API.

The company now presents the product as a set of AI agents. A Table Observability Agent monitors data availability, freshness and schema consistency. A Data Quality Agent lets users describe what good data looks like in natural language and then watches for deviations that could break dashboards, reports or AI models. A Data Insights Agent reports noteworthy changes in the data without being asked, and a Conversational Analytics Agent answers questions about the data. A Data Issue First Responder Agent, which would triage alerts and open tickets in ServiceNow or Jira, is listed as coming soon.

Anomalo positions the product for enterprises in sectors such as financial services, insurance and media.

Ataccama ONE is a data trust platform that manages data quality end to end, from finding problems to fixing them at the source. Ataccama states that it was named a Leader in the 2026 Gartner Magic Quadrant for Augmented Data Quality Solutions for the fifth consecutive time.

The data quality module monitors, cleanses and standardises data across systems, with rules written once and applied anywhere. The same platform adds a data catalog, data observability for pipeline anomalies, lineage from source to consumption, reference data management and master data management with AI-powered matching. Ataccama pitches the combination to regulated organisations that need audit-ready data for rules such as Basel, Solvency and the Dodd-Frank Act.

The ONE AI Agent is presented as a digital data steward. A user writes a prompt and the agent plans and runs the workflow: generating rules, creating test and reference data, enriching metadata and writing documentation. Ataccama says it cuts the time to create and apply a single rule from nine minutes to one.

Great Expectations is an open-source Python framework for testing data. Its central idea is the Expectation: an expressive, extensible unit test for data, such as a column never being null or values falling within a range. Expectations give engineers and business users a shared language for data rules, and the framework can generate documentation for each set of validation results so that everyone sees the same outcome. GX Core supports Python 3.10 to 3.13 and is released under the Apache 2.0 licence.

GX Cloud, the hosted product, added collaborative workflows, policy management and governance on top of GX Core. The company is in transition. In May 2026 it announced that FICO had agreed to acquire GX Cloud as the basis of FICO's data quality offering, and that Fivetran would become steward of GX Core, which is to remain an open-source, community-driven project.

Buyers adopting GX Core today are choosing an open-source framework; the future of the hosted product depends on FICO.

Monte Carlo is a data and AI observability platform. It uses machine learning to learn the normal behaviour of data across warehouses, lakes, ETL jobs, BI tools and AI applications, detects issues at each stage of the pipeline, assesses their impact and notifies the people responsible. Automatic field-level lineage and a central catalog show where each asset lives, who owns it and how healthy it is.

The company now also markets observability for AI agents, and a fleet of its own agents that automate triage and root cause analysis. Tiers scale from Start, for small teams with up to 10 users and up to 1,000 monitors, through Scale, which adds SSO, SCIM, audit logging and data mesh support, and Enterprise, which adds multi-workspace support, cost chargebacks and integrations with Oracle, SAP HANA, Teradata and Microsoft Fabric. Business Critical adds a dedicated instance and disaster recovery to a different region.

Monte Carlo prices through credits, and its public AWS Marketplace listing is the one published figure.

Bigeye began as a data observability platform and now calls its product the AI Trust Platform. The observability module monitors pipeline health across the data warehouse and applies deeper ML-based quality monitoring to the tables that matter most. The AWS Marketplace listing cites a library of more than 70 data quality metrics, which can be deployed through the user interface or programmatically with YAML configuration, and anomaly detection that replaces large numbers of manual alert rules.

Lineage is central to the product. Bigeye describes end-to-end lineage for enterprise environments with connectivity to both modern and legacy data stacks, used for upstream and downstream root cause and impact analysis. Further modules add metadata management, scanning for PII, PHI and PCI data in structured and unstructured sources, governance features such as certification and a business glossary, and AI Guardian, which enforces data access policies for AI applications at runtime.

Buyers pay by the number of tables under active monitoring.

Sifflet is a data observability platform for data engineers and data consumers. Its core tier bundles monitoring of fundamental metrics, such as freshness, schema and volume, with custom metrics, a data catalog and lineage, so that teams can find an asset, see what depends on it and understand why it broke from one place. Sifflet presents root cause analysis and business impact assessment as the way to shorten time to resolution.

The platform serves both technical and non-technical users. Engineers can connect it to coding workflows programmatically, while business users work through the interface and a metadata search engine. Sifflet's Sentinel agent suggests monitors, and agents for issue diagnosis and fix suggestions, named Sage and Forge, are in early access.

Tiers are set by the number of assets monitored. The smallest, up to 500 assets, can be bought self-serve or through marketplaces. The enterprise tier adds 24/7 support and hybrid or self-hosted deployment.

DQLabs sells PRIZM, a platform that combines data quality, data observability and data discovery. Its AWS Marketplace listing cites more than 250 out-of-the-box rules across volume, schema, freshness, reliability, distribution, frequency, statistics and pattern matching, plus custom rules for business logic. AI-augmented profiling analyses value frequencies, ranges and patterns, and the platform supports issue identification at attribute, table and pipeline level with root cause and impact analysis through lineage.

The discovery module enriches business metadata automatically with classifications, tags and relationships. DQLabs also describes LLM-based features for advanced quality checks and metadata enrichment, and a conversational interface aimed at users from citizen analysts to engineers.

The pricing model is unusual in this category. DQLabs does not charge per user, per row or per asset; scope is set by the data source types connected, and each includes unlimited assets and users. Everything beyond the base package, such as more sources, tenants or a sandbox, is a quoted add-on.

Elementary is a data observability tool built around dbt. The open-source edition, Elementary OSS, is a dbt package plus a command-line tool. The package collects data quality metrics, test results and run metadata into a schema in the warehouse; the CLI reads it to generate an observability report, surface anomalies and failed tests, and send alerts to Slack and Microsoft Teams. Anomaly detection runs as native dbt tests, and configuration is managed as code in the dbt project.

Elementary Cloud adds automated monitors for freshness, volume and schema, ML anomaly detection, column-level lineage from ingestion to BI, a built-in catalog, performance monitoring, data health scores and incident management. AI agents cover test coverage, triage and resolution, performance and cost, governance and cataloging. Elementary states that the Cloud product does not require access to customer data, reading only the metadata schema, and that it is SOC 2 Type II certified.

Teams that stop paying can fall back to the self-hosted open-source version.

Informatica, now part of Salesforce, sells Data Quality and Observability as part of its Intelligent Data Management Cloud. Where many tools on this page focus on detecting problems, Informatica also emphasises correcting them: the product continuously profiles data to find patterns, anomalies and quality risks, and uses AI to speed up cleansing, standardisation and validation.

Informatica says the product can generate common data quality rules automatically across data from a wide range of sources, using AI-powered rules and accelerators. Its observability features describe the health of data through three lenses, the data itself, the pipelines that move it and the business processes that rely on it. Informatica also positions data quality as the base for customer data used in applications and for AI initiatives.

The product sits in the same cloud platform as Informatica's data catalog, data integration, master data management and governance products, and is priced on consumption across that platform.