8 Best Data Quality Tools for Modern Data Teams in 2026

author-img
by Emily Winks, Data governance expert at Atlan.Last Updated on: January 30th, 2026 | 16 min read

Quick answer: What are data quality tools?

Data quality tools automate validation, monitoring, and anomaly detection to ensure data is accurate, complete, consistent, and fit for analytics and AI. Modern platforms integrate profiling, observability, and alerting with data catalogs and governance workflows, catching issues before they break downstream applications.

Top data quality tools include enterprise platforms like Atlan, Informatica, and Collibra, cloud-native observability tools like Monte Carlo and Anomalo, and open-source frameworks like Great Expectations.

Below, we’ll explore: why data quality tools matter, fundamental use cases, and best tools available, along with their top features.

Atlan Data Quality in Action →Start The Atlan Product Tour

Data quality tools at a glance

Solution Best For Key Differentiator Starting Price Free Trial/Plan
Atlan Enterprise teams needing unified quality, governance, and discovery Only platform combining active metadata, data quality, lineage, and catalog in single control plane Custom pricing Yes
Anomalo Cloud-first analytics teams prioritizing ML-driven automation Unsupervised machine learning for automated anomaly detection without manual rules Custom pricing Free pilot, per G2
Monte Carlo Data engineering teams managing complex, mission-critical pipelines End-to-end data + AI observability with incident management and lineage-driven root cause analysis Custom pricing Yes
Metaplane Small to mid-sized teams seeking quick setup with minimal overhead Fastest setup (15 minutes) with usage-based pricing and suggested monitoring From $10 per monitored table per month 14-day free trial
Informatica Large enterprises with existing Informatica ecosystems Comprehensive data management suite with advanced profiling and enterprise ETL integration Custom pricing Contact sales
SAP Organizations deeply invested in SAP ERP and S/4HANA platforms Native SAP data model validation and seamless ERP integration Custom pricing Contact sales
Qlik Talend Teams embedding quality directly into ETL and integration workflows Unified platform for integration, quality, and governance with Talend Trust Score Custom pricing 14-day free trial of Qlik Talend Cloud
Ataccama Large enterprises requiring unified quality, MDM, and governance platform AI-powered automation for rule generation with combined data catalog and quality capabilities Custom pricing Contact sales

Assess your organization's data quality maturity in 3 minutes

Take the Assessment →

What makes the best data quality tool?

The best data quality tools combine automated monitoring, context-aware prioritization, and seamless integration with modern data stacks. Key evaluation criteria include:

1. Automated anomaly detection


Statistical and ML-driven checks for freshness, volume, schema, and distribution.

2. Business context integration


Lineage, ownership, and usage signals to prioritize high-impact assets.

3. Native warehouse execution


In-database quality checks in Snowflake, Databricks, and BigQuery.

4. Workflow embedding


Quality alerts routed to Slack/Teams, quality gates in CI/CD pipelines.

5. Governance alignment


Integration with data catalogs, policies, and access controls.

6. AI readiness


Quality validation for model inputs, bias detection, and AI governance.

Organizations increasingly prioritize platforms offering unified control planes over point solutions requiring manual integration.

Why do data quality tools matter?

According to Forrester, the biggest limiting factor for GenAI adoption is data quality.

“A lot can go wrong between the user request, interpretation of the question, how the response is generated, and how the response is communicated back to the user. The old adage ‘garbage in, garbage out’ is even more true for genAI.” - Brett Kahnke, Principal Analyst, and Michelle Goetz, VP, Principal Analyst

Effective data quality tools deliver clear business value by strengthening trust, decision-making, and efficiency. Forrester highlights the following benefits:

  • Build trust: Supply high-quality data for advanced analytics and AI/ML, improving confidence in outcomes.
  • Drive better decisions: Map, validate, and enrich data, so that business teams operate on consistent, accurate information across use cases.
  • Improve efficiency: Continuously monitor key metrics to optimize processes, resources, and decisions.

Together, these benefits directly reduce risk. By preventing inaccurate, incomplete, or non-compliant data from flowing into reports, models, and applications, data quality tools help meet compliance requirements while reducing operational and reputational exposure.

What are the top use cases for data quality tools?

Data quality tools in 2026 fall into three clear categories, reflecting how organizations manage scale, complexity, and ownership across their data stacks. Each category serves a different type of team, maturity level, and use case.

Gartner research lists data analytics, AI and machine learning, data engineering, and D&A governance as the top use cases for data quality tools. However, different types of tools cater to specific types of teams, maturity levels, and use cases.

Depending on how organizations manage scale, complexity, and ownership across their data stacks, data quality tools fall into three clear categories.

Data quality tool types and their use cases at a glance


Data quality tool type What they include Use case Best for Examples
Enterprise data quality platforms End-to-end data quality embedded within broader data and analytics governance. Typically include rule definition, profiling, monitoring, lineage, policy enforcement, workflows, and deep enterprise integrations. Data & analytics governance, regulatory compliance, master data management (MDM), enterprise reporting, AI governance, cross-domain data quality standardization. Large enterprises, regulated industries, and organizations needing centralized control, compliance, and cross-domain visibility. Atlan, Informatica, Talend, SAP, Collibra, Ataccama ONE
Cloud-native data quality and observability tools Modern, warehouse-native tools focused on automated monitoring and anomaly detection. Include ML-driven checks for freshness, volume, schema, and distribution, with fast setup and alerting. Data engineering reliability, pipeline monitoring, analytics uptime, incident detection, root cause analysis in cloud data stacks. Cloud-first, engineering-led teams prioritizing speed, observability, and production analytics reliability. Monte Carlo, Anomalo, Metaplane
Open-source data quality tools Open-source frameworks are code-first tools for defining tests and assertions. Give teams full control to build custom data quality checks. However, they require engineering effort to operate at scale. Custom validation logic, embedded checks in data pipelines, experimentation, early-stage quality programs. Engineering-first teams prioritizing flexibility, code-first workflows, and lower software cost. Great Expectations, Soda Core, Deequ

What are the top data quality tools in 2026?

Now, let’s explore the best data quality tools for enterprises and cloud-native, future-forward data teams.

1. Atlan


Atlan is the only active metadata platform unifying data quality, governance, and discovery in a single control plane, using context signals to identify critical assets and automate quality checks.

Atlan’s Data Quality Studio uses context — lineage, ownership, usage, and consumption — to identify business-critical assets, automate rule generation, and align teams around a shared definition of “good data.”

Top capabilities that make Atlan stand out

  1. Identify business-critical assets using existing context from the platform. This includes:

    • Metadata signals like ownership, data products, starred assets, and downstream impact.
    • Lineage to identify high-impact upstream assets.

  2. Apply the right quality checks with no-code rules and AI-assisted suggestions.

  3. Monitor your data estate across domains and systems with:

    • A central reporting dashboard that shows quality health, incidents, and impact in one place.
    • Quality signals connected to assets, owners, and downstream usage, building a holistic, 360° view of data health.
    • Automated alerts that notify the right people in Slack or Teams when something breaks.

Recognition

Best suited for: Enterprises needing a unified context layer that serves technical and business users by embedding quality signals into daily workflows.

Pricing

  • Starting price: Custom pricing (enterprise)
  • Free trial: Yes, contact sales.
  • Free plan: No

2. Anomalo


Anomalo is a cloud-native data quality platform using unsupervised machine learning to automatically detect anomalies across structured, semi-structured, and unstructured data without manual rule configuration.

Capabilities

  1. Statistical anomaly detection for volume, freshness, and distribution.
  2. Automated rule suggestions based on data behavior.
  3. Native integrations with Snowflake, Databricks, BigQuery, Redshift, and Atlan.

Limitations

Limited governance and metadata management capabilities.

Best suited for: Cloud-first analytics teams that want fast anomaly detection without heavy governance needs.

Pricing

  • Starting price: Custom pricing (enterprise)
  • Free trial: Yes. A free pilot is available, according to G2.
  • Free plan: No

3. Monte Carlo


Monte Carlo is an end-to-end data and AI observability platform that detects data downtime and pipeline failures through automated monitoring of freshness, volume, schema, and distribution.

Capabilities

  1. End-to-end monitoring across freshness, volume, schema, and distribution.
  2. Incident detection and root cause analysis using lineage.
  3. Strong alerting and incident management workflows.
  4. Native integrations with Snowflake, Databricks, and Atlan.

Limitations

Limited metadata management and governance depth.

Best suited for: Data engineering teams managing complex pipelines who need reliability and fast incident response.

Pricing

4. Metaplane


Metaplane is a lightweight data observability tool built for 15-minute setup, offering ML-powered anomaly detection and suggested monitoring for modern cloud data warehouses.

Capabilities

  1. Automated anomaly detection on tables and columns.
  2. Simple setup with cloud warehouses.
  3. Clear visibility into freshness and schema changes.

Limitations

  1. Less customizable for complex enterprise rules.
  2. Limited governance, lineage, and policy management.

Best suited for: Small to mid-sized teams seeking quick visibility into data issues with minimal overhead.

Pricing

  • Starting price: $1249/mo as per capterra
  • Free trial: 14-day trial (no credit card required)
  • Free plan: No

5. Informatica Data Quality & Observability


Informatica delivers enterprise-grade data quality through its Intelligent Data Management Cloud (IDMC), combining automated profiling, cleansing, and validation with deep ETL and governance integration.

Capabilities

  1. Solid data quality, monitoring, and observability features.
  2. Advanced and automated profiling, cleansing, and validation.
  3. Deep integration with Informatica’s ETL and governance tools.

Limitations

  1. Complex setup raising usability and UX challenges.
  2. Higher total cost of ownership and limited customization options.
  3. Less intuitive for business users, with a steep learning curve.

Best suited for: Large enterprises with existing Informatica investments and strict compliance requirements.

Pricing

  • Starting price: Custom pricing (enterprise)
  • Free trial: Contact sales
  • Free plan: No

6. SAP


SAP Data Services provides data quality and cleansing capabilities embedded within the SAP ecosystem, offering native validation for SAP data models and seamless ERP integration.

Capabilities

  1. Strong validation and cleansing for SAP data models.
  2. Integration with SAP ERP, S/4HANA, and analytics tools.
  3. Governance-aligned controls for regulated industries.

Limitations

  1. Best experience limited to SAP-centric environments.
  2. Complex setup and steep learning curve.
  3. Less flexible for heterogeneous modern data stacks.

Best suited for: Organizations deeply invested in SAP platforms and enterprise master data management.

Pricing

  • Starting price: Custom pricing (enterprise)
  • Free trial: Contact sales
  • Free plan: No

7. Qlik Talend Cloud


Qlik Talend Cloud unifies data integration, quality, and governance in a single platform, embedding quality checks during ingestion with the proprietary Talend Trust Score.

Capabilities

  1. Profiling, validation, and enrichment during ingestion.
  2. Prebuilt quality rules and transformations.
  3. Tight integration with Qlik analytics and Talend pipelines.

Limitations

  1. Quality checks are often tied to ingestion workflows.
  2. Limited observability for downstream analytics usage.

Best suited for: Teams looking to embed data quality directly into ETL and integration pipelines.

Pricing

8. Ataccama ONE


Ataccama ONE combines data quality, master data management, and governance in a unified platform with AI-powered automation for rule generation and quality check testing.

Capabilities

  1. Rule-based and AI-assisted data quality checks.
  2. Centralized governance, profiling, and remediation.
  3. Strong support for complex enterprise data domains.

Limitations

  1. Implementation can be resource-intensive.
  2. Slower time to value for smaller teams.

Best suited for: Large enterprises seeking a single platform for quality, MDM, and governance.

Pricing

  • Starting price: Custom pricing (enterprise)
  • Free trial: Contact sales
  • Free plan: No

Real stories from real customers: How future-forward teams improve data quality with unified platforms

General Motors: Data Quality as a System of Trust

“By treating every dataset like an agreement between producers and consumers, GM is embedding trust and accountability into the fabric of its operations. Engineering and governance teams now work side by side to ensure meaning, quality, and lineage travel with every dataset — from the factory floor to the AI models shaping the future of mobility.” - Sherri Adame, Enterprise Data Governance Leader, General Motors

See how GM builds trust with quality data

Watch Now →

Workday: Data Quality for AI-Readiness

“Our beautiful governed data, while great for humans, isn’t particularly digestible for an AI. In the future, our job will not just be to govern data. It will be to teach AI how to interact with it.” - Joe DosSantos, VP of Enterprise Data and Analytics, Workday

See how Workday makes data AI-ready

Watch Now →

Ready to choose the best data quality tool for your enterprise?

Data quality tools ensure data is accurate, reliable, and fit for analytics and AI by automating validation, monitoring, and issue resolution. Used wisely, data quality tools can help narrow the data trust gap, increase data usage, and reduce the time to market for new data-driven solutions.

The right choice depends on scale, cloud maturity, and governance needs.

Enterprises increasingly need a unified control plane and that’s where platforms like Atlan can help. Atlan combines data quality, lineage, governance, and active metadata, making quality actionable across the data and AI lifecycle while driving faster adoption and trust.

Book a Demo →

FAQs about data quality tools

1. What is a data quality tool?


A data quality tool is a platform that automates validation, monitoring, and anomaly detection to ensure data accuracy, completeness, consistency, and reliability. These tools automatically profile datasets, detect issues like schema drift or freshness problems, and alert teams before errors impact analytics, operations, or AI models. Modern platforms integrate with cloud warehouses, data catalogs, and BI tools to embed quality checks throughout the data lifecycle from ingestion to consumption.

2. What core capabilities do data quality tools cover?


Data quality tools provide six core capabilities: automated validation and alerting, continuous monitoring and observability, data discovery and profiling, root cause analysis using lineage, integration with modern data stacks, and AI-powered workflows for issue resolution. These capabilities work together to catch data issues early, identify their source through lineage tracking, and route alerts to the right people through tools like Slack or Teams. Advanced platforms also include governance features like policy enforcement and data contracts.


Popular data quality tools in 2026 include enterprise platforms like Atlan, Informatica, Talend, Collibra, SAP, and Ataccama, cloud-native observability tools like Monte Carlo, Anomalo, and Metaplane, and open-source frameworks such as Great Expectations, Soda Core, and Deequ. Each category serves different organizational needs: enterprise platforms offer unified governance and quality, cloud-native tools prioritize speed and automation, while open-source frameworks provide flexibility for engineering-first teams with custom requirements.

4. What is the best data quality tool?


There is no single “best” data quality tool because the right choice depends on your organization’s scale, governance needs, cloud maturity, and team composition. However, modern enterprises increasingly choose cloud-native platforms with future-proof architecture like Atlan that unify data governance, quality, and discovery into a single control plane. Small teams may prefer lightweight tools like Metaplane, while enterprises with existing technology investments often select platforms like Informatica or SAP that integrate with their current stack.

5. What are the 7 Cs of data quality?


The 7 Cs of data quality are Completeness (no missing critical data), Consistency (data agrees across systems), Correctness or Accuracy (reflects real-world values), Conformance (follows standards and formats), Credibility (trusted and authoritative), Clarity (well-defined and understandable), and Coverage (represents the full required scope). These seven dimensions provide a comprehensive framework for evaluating data fitness across different use cases, from analytics and reporting to AI model training and regulatory compliance.

6. How do I choose a data quality tool for my organization?


Start by assessing your data scale, cloud stack complexity, and governance requirements, then look for tools that support your data sources, automate quality checks, integrate with lineage and catalogs, and embed alerts into existing workflows. Prioritize platforms offering ease of use, scalability, and alignment with your analytics and AI goals. Evaluate whether you need enterprise governance features, lightweight observability, or open-source flexibility. Consider team size, technical expertise, budget constraints, and whether you prefer pay-as-you-go pricing or annual enterprise contracts.

7. Why do you need a metadata control plane like Atlan with open source data quality tools (like Great Expectations or Soda)?


Modern data teams need platforms that unify discovery, governance, and quality into a single control plane to reduce fragmentation and scale trust across analytics and AI initiatives. Atlan’s Data Quality Studio sits above open-source tools like Great Expectations and Soda Core, aggregating their quality signals and connecting them with business context through ownership, lineage, and downstream impact mapping. This prevents failed checks from dying in logs by routing issues to the right owners with full context, enabling teams to operate on data quality rather than just running isolated checks.

Share this article

signoff-panel-logo

Atlan is the next-generation platform for data and AI governance. It is a control plane that stitches together a business's disparate data infrastructure, cataloging and enriching data with business context and security.

Book a DemoStart Tour
 

Atlan named a Leader in 2026 Gartner® Magic Quadrant™ for D&A Governance. Read Report →

[Website env: production]