Best Data Quality Tools in 2026

Informatica Data Quality & Observability is the data quality service of Informatica's Intelligent Data Management Cloud (IDMC). It automates and scales data profiling to find patterns, anomalies and quality risks; deploys data quality agents to automate cleansing, standardisation and validation; auto-generates common data quality rules across data types and sources; and presents observability of data health through data, pipeline and business lenses. Informatica's CLAIRE generative AI and its headless interfaces are used to automate quality work across applications. Because it shares the IDMC platform with Informatica's integration, catalog, governance and master data management services, it suits organisations that want quality rules applied consistently in pipelines and MDM. Salesforce completed its acquisition of Informatica on 18 November 2025. Pricing is consumption-based across IDMC services, and Informatica states that more than 5,000 enterprises use its products. The trade-offs are cost, implementation effort and the specialist skills required, which make it more than smaller teams typically need.

Ataccama ONE is a data management platform with data quality at its core, combining automated data quality checks, monitoring and anomaly detection with catalog, governance, reference data and master data management modules; it is available as Ataccama Cloud and describes its newest version as an agentic platform. Customers named on its site include Fifth Third Bank and Prudential. The pricing model is published in outline: tiers are determined by three dimensions, named users with full platform access (consumer users with view permissions are unlimited at every tier), the number of data objects such as tables, views, pipelines and BI reports managed in the platform, and active data quality configurations executed in the past 90 days. Ataccama states that exceeding a threshold never disrupts operations or triggers retroactive charges. Prices themselves are quoted. The platform's breadth suits enterprises that want quality, governance and MDM from one vendor; smaller teams wanting only pipeline tests may find it more than they need.

Precisely's Data Integrity Suite is a set of interoperable cloud services built on a common foundation. Its modules cover data quality (accurate, consistent data across operational and analytical systems), data observability (detecting anomalies before they cause downstream problems), geo addressing (verifying, standardising, cleansing and geocoding addresses), data enrichment from hundreds of curated datasets, data governance, data integration and spatial analytics. Precisely says the suite is used by more than 12,000 businesses worldwide and holds FedRAMP authorisation for public-sector use. Its distinctive strength is location and address data: few data quality vendors combine rule-based quality with authoritative address verification, geocoding and enrichment, which matters for insurers, retailers, telecoms and public bodies. Customers can consume modules as needed through the SaaS architecture, and prices are obtained from Precisely. Organisations that only need warehouse anomaly monitoring may find newer observability tools simpler to adopt.

Qlik Talend Cloud combines Qlik's data movement and change data capture with the transformation, data quality and governance capabilities that Qlik gained by acquiring Talend. Its plans include data quality and profiling, field-level metadata management, end-to-end column-level lineage and impact analysis, and data products, alongside ELT and ETL transformation and warehouse automation. Applying quality rules within the same platform that moves the data lets teams validate and cleanse records in pipelines before they reach analytics or operational systems, rather than only detecting problems afterwards. Plans range from Starter to Enterprise and are quoted, with a free trial available. The product suits organisations that are already standardising on Qlik for integration or analytics. Buyers looking for a standalone observability layer across many existing pipelines may prefer a dedicated tool, and edition differences need careful review because quality features are spread across tiers.

Collibra, whose platform also covers data catalog, governance, lineage and privacy, sells Data Quality & Observability as part of a unified governance offering. The product deploys automated and custom monitors to detect anomalies, schema changes, missing and duplicate data and missing records; uses AI and reusable rule templates to reduce manual rule writing; and offers an MCP server so that a data quality programme can be run from a large language model. Jobs can be pushed down to the customer's warehouse compute without data egress or run in a dedicated Spark engine, and lineage traces root causes upstream and affected products, models and owners downstream, with issue workflows, alerts and integrations with Jira and ServiceNow for remediation. Collibra cites a Wavestone survey in which only 37% of data and AI executives said they had improved data quality. The product is most valuable to organisations already governing data in Collibra, where quality scores attach to catalog assets and owners. Pricing is not published, and the full benefit depends on the broader Collibra platform.

SAS provides data quality capabilities through the data management features of SAS Viya, its cloud-native analytics and AI platform. SAS states that Viya continuously monitors, profiles and improves data quality while embedding governance throughout the data life cycle, and provides metadata management, data lineage and governance to establish trust across data, analytics and AI workflows, in on-premises, hybrid and multicloud environments. SAS customers use it to standardise, verify and clean data close to their analytical models; one example on the SAS site is the Drugs for Neglected Diseases initiative. Migration from SAS 9 to Viya is a current theme in the company's materials. SAS is the right choice mainly for organisations already invested in SAS analytics. Its products are licensed through quotes, and teams outside the SAS ecosystem generally find standalone data quality or observability tools easier to adopt.

Monte Carlo is a data observability vendor that monitors data warehouses, lakes, databases, BI tools and ETL pipelines, and now extends observability to AI agents, machine-learning models and performance. Its platform includes incident triage, root-cause analysis and lineage, plus a fleet of agents to automate operational work. Pricing is credit-based: customers buy credits consumed at published consumption rates, and the cost per credit depends on the tier. The Start tier supports up to 10 users and up to 1,000 monitors and integrates with warehouses, BI and ETL tools and agent frameworks; Scale adds SSO, SCIM, self-hosted storage, PII filtering, data-mesh domains and lakes and databases such as Databricks, Glue and Postgres; Enterprise adds multi-workspace support, chargebacks and enterprise warehouses including Oracle, SAP HANA, Teradata and Microsoft Fabric; and Business Critical targets maximum availability. Monte Carlo's strength is broad, low-configuration anomaly monitoring. Its per-monitor and credit model requires governance to control cost as coverage grows, and it detects rather than corrects bad data.

Datadog Data Observability brings data quality monitoring into Datadog's observability platform. Its data-quality component is based on Metaplane, a data observability start-up that now brands itself 'Metaplane by Datadog' and says the two companies have joined forces to bring software and data observability together. Metaplane's product monitors warehouse tables with machine-learning anomaly detection, alerts on schema changes, provides column-level lineage and impact analysis, monitors jobs and can test data changes in pull requests (data CI/CD); it integrates with BI tools such as Looker, Metabase, Power BI, Sigma and Tableau and with orchestration tools such as Airflow. In Datadog's price list, Data Observability: Quality Monitoring costs US$16 per monitored table per month on annual billing, and Jobs Monitoring is charged per host-hour for Spark clusters. Datadog reported revenue of US$3.43 billion for 2025. The product suits companies that already use Datadog and want data incidents in the same console as application incidents. Per-table pricing can become expensive for large warehouses.

Bigeye is a data observability vendor that describes itself as the first enterprise AI Trust platform, built on lineage-enabled data observability. Its core product offers anomaly detection and monitoring, monitoring as code, cost anomaly detection and end-to-end lineage across what it calls the broadest range of connectivity for modern and legacy data stacks; listed integrations include Snowflake, Databricks, BigQuery, Postgres, Amazon Athena and RDS, Trino, Informatica, SAS, Talend, Netezza and Greenplum, along with dbt, Airflow, Power BI and Tableau. Newer AI Trust modules apply runtime enforcement of data-access policies for AI applications, sensitive-data scanning for PII, PHI and PCI, and governance aimed at frameworks such as the EU AI Act and ISO 42001. Customer logos on the site include Cisco, USAA, Zoom and Canva, and a Udacity case study reports detection time cut from more than three days to under 24 hours. Bigeye's support for legacy warehouses suits large enterprises in transition. Pricing is not published.

Anomalo is a data quality company that uses machine learning to detect abnormal patterns in data without requiring users to write rules for every table, and describes its product as an autonomous data system for the agentic enterprise. It offers agents for table observability, data quality, data insights, conversational analytics and data documentation, with further agents for issue response, KPI monitoring and reporting marked as coming soon. An Unstructured Data Monitoring product extends quality checks to documents, aimed at organisations preparing content for AI applications. Anomalo can be deployed entirely in the customer's VPC or as SaaS, lists SOC 2 Type II, GDPR and HIPAA compliance and SAML single sign-on, and says customers can connect data in minutes with first insights within 24 hours. Its machine-learning approach reduces the rule-writing burden for large warehouses. Pricing is not published, several announced capabilities are not yet available, and ML-generated alerts require tuning to avoid noise.

Soda is a data quality platform built around pipeline testing, metrics observability and collaborative data contracts, in which data producers and consumers agree on the quality expectations for a dataset and Soda enforces them. Checks can be written in code or, on paid plans, through a no-code interface, and Soda adds AI-powered data quality features on its Team plan. Its pricing is unusually transparent for the category: a Free plan at US$0 per month includes free Soda Processing Units (SPUs), pipeline testing, metrics observability and alerting and ticketing integrations with no credit card; the Team plan costs US$750 per month with unlimited users, pay-as-you-go SPUs, collaborative data contracts and audit logs; and Enterprise is custom-priced with private deployment, SSO and premium support. Soda suits data engineering teams that want to shift quality checks left into pipelines and formalise expectations with data contracts. It requires teams to define checks, so coverage depends on effort, and anomaly detection is less automatic than in ML-first observability tools.

Great Expectations is a data validation framework in which data teams declare 'expectations', assertions such as a column never being null or values falling within a range, and run them against data in pipelines to produce validation results and documentation. The company offers GX Core, the framework, along with an Expectation Gallery of reusable checks and integrations, and GX Cloud, a managed service. GX Cloud can be used free through a Developer option, with Team and Enterprise solutions for greater flexibility; prices for the paid options are not listed on the pricing page. Great Expectations suits data engineers who prefer to define quality as code, version it alongside pipelines and run it in orchestration tools. The approach gives precise control and costs nothing to start, but coverage depends entirely on the expectations a team writes and maintains, and the framework has less automatic anomaly detection and fewer business-user features than commercial observability platforms.

Melissa is a contact-data quality specialist that verifies, standardises and enriches customer records. Its tools cover US and international address verification, phone, email and name verification, matching and deduplication, profiling, generalised and batch address cleansing, and identity verification, delivered through apps and APIs, the Listware online service and its Unison data quality platform. Melissa also provides connectors for platforms such as Microsoft SQL Server and Talend, so its verification can run inside existing ETL processes. Unlike warehouse observability tools, which detect anomalies in data pipelines, Melissa corrects the content of individual records, which matters for direct marketing, e-commerce delivery, customer onboarding and compliance. Online services are sold through credits and subscriptions. Its scope is narrower than general-purpose data quality platforms: it focuses on people, addresses and businesses rather than arbitrary datasets.