The 10 Best Data Quality Assessment Tools of August 2025

Data quality assessment tools are software solutions that automatically monitor, validate, and ensure your data remains accurate, complete, and reliable. Without them, you’re essentially flying blind and hoping your dashboards show the right numbers and your models train on good data.

Here’s the harsh reality. Data professionals spend roughly 40% of their time fixing data issues. That’s nearly half your week investigating broken pipelines, tracking down metric discrepancies, and explaining why the CEO’s dashboard shows impossible numbers. Poor data quality doesn’t just waste time. It destroys trust, drives terrible decisions, and costs organizations millions in lost opportunities and failed initiatives.

This guide examines the top 10 data quality assessment tools transforming how modern data teams work. From open-source frameworks to enterprise platforms, we’ll explore what makes each tool unique and help you find the right fit. 

What are data quality assessment tools?

Data quality assessment tools are software applications that automatically evaluate and ensure the accuracy, consistency, and reliability of your data. They serve as automated quality control for your data pipelines, constantly checking that your information meets the standards your business depends on.

These tools work by monitoring key aspects of data health. They check completeness (are all expected records present?), accuracy (do values make sense?), consistency (does the same data match across different sources?), and timeliness (is data arriving when expected?). Most data quality tools run these checks automatically, alerting teams when something goes wrong.

Data quality monitoring solutions do more than simple rule-based checks. They use machine learning to understand normal patterns in your data and flag unusual changes. This means they can catch problems you didn’t even know to look for. Whether you call them data quality tools, data + AI observability platforms, or data reliability solutions, they all share the same goal of making sure you can trust your data.

The importance of data quality

Imagine discovering a critical dashboard is broken due to bad data at 3 PM on a Friday. Not fun. This scenario plays out daily across data teams worldwide, turning what should be strategic work into constant firefighting.

The impact of poor data quality hits hard. Data teams waste enormous amounts of time tracking down issues. That stat about 40% of time spent fixing data problems? It’s real, and it hurts. Bad data leads to misguided decisions, lost revenue, and eroded trust. When executives can’t rely on their dashboards, they stop using them altogether. A centralized data quality dashboard showing the health of your data assets can rebuild that trust.

For data engineers, data quality issues mean late-night alerts and endless debugging sessions instead of building new pipelines. You’re stuck playing detective rather than architect. Data analysts face their own nightmare: presenting insights only to have someone point out the numbers don’t add up. Your credibility takes a hit every time.

No one likes getting that Slack message about broken data. That’s why ensuring data quality isn’t just a nice-to-have. It’s critical for maintaining sanity and delivering value. Good data quality tools give engineers more time to build and analysts more confidence in their analysis.

Top 10 data quality assessment tools

Below we explore ten of the best data quality assessment tools available today. Each tool has unique strengths, from open-source libraries to enterprise-grade platforms, so data teams of all sizes can find an option that fits their needs. Monte Carlo leads our list as it’s widely regarded as a top solution in this space.

1. Monte Carlo

Monte Carlo pioneered the data + AI observability category in 2019, fundamentally changing how organizations ensure data reliability. Founded by Barr Moses and Lior Gavish, the platform applies proven observability principles from software engineering to data pipelines. Today, Monte Carlo monitors data health for over 150 enterprises including CNN, JetBlue, HubSpot, and Toast – organizations that can’t afford bad data affecting their analytics or AI models. 

Key Features:

• End-to-End Data Integration: Connects seamlessly across your entire data stack – warehouses, lakes, ETL tools, and BI dashboards – providing complete visibility from source to consumption without gaps in coverage.
• ML-Powered Anomaly Detection: Automatically establishes baseline patterns for volume, distribution, and schema metrics, then alerts in real-time when data drifts from normal behavior, catching issues before they impact business decisions.
• Automated Root Cause Analysis: Traces data lineage and dependencies instantly when incidents occur, pinpointing whether a broken SQL job, unexpected schema change, or upstream failure caused the problem – reducing investigation time from hours to minutes.
• Data Lineage & Cataloging: Built-in catalog displays comprehensive lineage graphs showing all upstream sources and downstream consumers for every data asset, enabling teams to quickly assess impact and prioritize fixes.
• Secure, Scalable Architecture: Handles petabyte-scale environments by analyzing only metadata rather than copying customer data, ensuring both performance and security with SOC 2 compliance and enterprise-grade protections.
• No-Code Onboarding: Deploy comprehensive monitoring in minutes using pre-built integrations and out-of-the-box monitors – no engineering resources required to start catching data quality issues immediately.
• Broad Integration Support: 50+ native connectors cover databases, cloud warehouses, data lakes, and SaaS applications, fitting naturally into existing data ecosystems without architectural changes.
• Real-Time Alerts & Dashboards: Intelligent alerting through Slack, email, PagerDuty, and other channels ensures the right people know about issues immediately, while intuitive dashboards track quality trends over time.

Benefits:

• Higher Data Reliability: Proactively catches schema changes, missing data, and anomalies before they affect analytics or ML models. Teams trust their data again because issues surface before stakeholders notice problems.
• Faster Issue Resolution: Automated root cause analysis combined with comprehensive lineage views transforms debugging from detective work into straightforward fixes. Engineers solve problems in minutes instead of hours.
• Ease of Use and Adoption: The no-code interface and automated setup mean both engineers and analysts can use Monte Carlo effectively. Minimal training required, maximum impact on data quality practices across your organization.

Pricing:

Monte Carlo uses a custom, usage-based pricing model tailored to each organization’s data volume and monitoring needs. The platform is positioned as an enterprise solution with pricing that reflects its comprehensive capabilities and proven ROI. While there’s no free tier, prospective customers work with Monte Carlo’s sales team to structure pricing that aligns with their specific requirements and expected value. Most customers find the investment pays for itself quickly through reduced data incidents and faster issue resolution.

2. Great expectations (GX)

Great expectations is the leading open-source data quality framework, created to help data teams test and document their data pipelines. Founded in 2017 by Abe Gong and James Campbell, the project emerged from frustration with manual data validation processes. The framework allows teams to express what they expect from their data as simple, declarative assertions, then automatically validate data against these expectations. Great Expectations has become the de facto standard for data testing in the modern data stack, with thousands of organizations using it and an active community of contributors. 

Key Features:

• Expectation Library: Ships with 300+ pre-built expectations (data quality checks) covering common validation scenarios like null checks, value ranges, regex patterns, and referential integrity – ready to use out of the box.
• Custom Expectations: Allows writing custom expectations in Python using a declarative style, enabling teams to create domain-specific validations that match their unique business rules and data requirements.
• Pipeline Integration: Integrates seamlessly with orchestration tools like Airflow, dbt, and Prefect, allowing data quality checks to run as part of ETL/ELT workflows.
• Version Control Friendly: Expectations can be stored as JSON/YAML files, making them easy to version control, review, and collaborate on using standard development workflows.

Benefits:

• Developer-Friendly: Built by and for data engineers, Great Expectations fits naturally into existing development workflows with CLI tools, programmatic APIs, and Git integration.
• Cost-Effective: The core framework is completely free, allowing teams to implement enterprise-grade data quality testing without licensing fees – only paying for infrastructure to run it.
• Community Support: Benefits from a large, active community providing plugins, integrations, best practices, and support through forums and documentation.

Pricing:

Great Expectations Core is completely free and open source under the Apache 2.0 license. Teams can use it in production without any costs. Great Expectations Cloud offers a managed service with a free Developer tier for individuals and paid Team/Enterprise tiers (starting in the low thousands per month) that include features like web UI, collaboration tools, and support. Pricing for cloud tiers scales based on usage metrics like expectation suite runs per month.

3. Soda

Soda is a data quality platform that combines an open-source core with cloud-based collaboration features. Founded in 2018 in Brussels by Maarten Masschelein and Tom Baeyens, Soda emerged from the founders’ experience building data infrastructure at major enterprises. The company’s mission centers on making data quality testing accessible to entire data teams, not just engineers. Soda’s approach emphasizes “shifting left” – catching data issues early in the development process through testing and monitoring. 

Key Features:

• Soda Checks Language (YAML DSL): Uses a human-readable YAML syntax for defining data quality rules, making checks accessible to both technical and non-technical team members without heavy coding.
• Built-in Metrics Library: Includes 25+ pre-built data quality metrics covering row counts, null rates, duplicates, and other common validations, with support for custom SQL queries.
• Multi-Source Compatibility: Connects to 20+ data sources including PostgreSQL, Snowflake, BigQuery, Redshift, and even CSV files, enabling consistent quality checks across diverse data platforms.
• Alerting & Collaboration: Sends notifications to Slack, email, Jira, and other channels, with features for commenting on issues and tracking resolution progress.

Benefits:

• Accessibility for All Users: The YAML-based checks and collaborative cloud interface make data quality accessible to analysts and business users, not just engineers.
• Flexible Deployment: Teams can start with the open-source core and scale to the cloud platform as needs grow, avoiding vendor lock-in.
• Quick Time to Value: Simple syntax and pre-built checks enable teams to implement quality monitoring rapidly without extensive setup or training.

Pricing:

Soda offers a generous free tier for Soda Cloud supporting up to 3 datasets forever. The Team plan costs $8 per dataset per month with annual billing, including unlimited users and all integrations. Enterprise plans offer custom pricing with exclusive features like collaborative data contracts, no-code check creation, AI-powered quality features, private cloud deployment, and premium support. The open-source Soda Core remains free for teams preferring self-managed deployment.

4. Bigeye

Bigeye is a data observability platform founded in 2019 by Kyle Kirwan and Egor Gryaznov, both former Uber engineers who experienced firsthand the challenges of maintaining data quality at scale. The company emerged from their work building Uber’s internal data quality tools, bringing those enterprise-grade capabilities to the broader market. Bigeye focuses on providing customizable, automated data quality monitoring with deep lineage integration to help organizations maintain trust in their data. 

Key Features:

• Automated Data Discovery: Automatically scans and profiles connected data sources to understand normal patterns and propose appropriate monitors without manual configuration.
• Custom Metrics & Rules: Provides both out-of-the-box monitoring for common metrics (freshness, volume, distribution) and a flexible rule builder for defining custom business logic.
• Visual Rule Builder: Offers both no-code UI and config-as-code options for creating monitors, accommodating users of different technical skill levels.
• Collaboration Features: Includes commenting, issue assignment, and integration with ticketing tools like Jira to fit existing IT workflows.

Benefits:

• Comprehensive Coverage: Combines automated discovery with customizable monitoring to ensure nothing falls through the cracks in complex data environments.
• Faster Root Cause Analysis: Lineage-enabled observability dramatically reduces time spent investigating data issues by showing exact upstream causes and downstream impacts.
• Flexible Implementation: Accommodates both technical and non-technical users with visual interfaces and programmatic options, enabling broad adoption.

Pricing:

Bigeye uses custom enterprise pricing based on factors including number of tables monitored, data volume, and monitoring complexity. The platform follows a usage-based model that scales with organizational needs, typically structured as annual SaaS subscriptions. While there’s no free tier, Bigeye offers trials or pilot programs for evaluation. Procurement is available through AWS and Azure marketplaces, and multi-year contracts often include volume discounts and dedicated support services.

5. Datafold

Datafold is a data reliability platform founded in 2020 by Alex Egorov and George Baev, who previously built data infrastructure at Lyft and PayPal. The company pioneered the concept of “data diffing” – comparing datasets to identify differences, similar to how developers use Git diff for code. This approach helps data teams catch unintended changes before they reach production. 

Key Features:

• Data Diff Engine: Performs value-level comparisons between datasets, even across different databases, highlighting any differing rows or cells to catch regressions before production.
• CI/CD Integration: Provides native plugins for GitHub, GitLab, and other platforms to automatically run data quality checks on code changes and post results directly on pull requests.
• Column-Level Lineage: Maps relationships between individual fields across transformations, enabling precise understanding of data dependencies.
• Production Monitoring: Complements pre-deployment testing with ongoing monitors for schema changes, freshness, metrics, and cross-database consistency.
• Performance Optimization: Uses advanced algorithms to compare billions of rows in seconds, making it practical for CI/CD pipelines where speed matters.
• Developer-Centric Design: Includes CLI tools, configuration as code, and tight dbt integration to fit naturally into engineering workflows.

Benefits:

• Prevent Data Regressions: Catches unintended data changes during development, similar to how unit tests prevent code regressions, dramatically reducing production incidents.
• Faster Development Cycles: Immediate feedback on data impact enables engineers to iterate quickly with confidence, knowing exactly what their changes affect.
• Seamless Workflow Integration: Fits into existing Git-based development processes without requiring new tools or major process changes.

Pricing:

Datafold offers a free tier providing access to core Data Diff features for small teams. Paid plans unlock unlimited diffs, production monitoring, advanced lineage, and enterprise features, with pricing based on developer seats and number of tables monitored. The platform uses transparent, usage-based pricing with options to license specific modules separately. Enterprise deployments include self-hosted options at premium pricing. As a moderate-cost solution, Datafold provides specialized value for development workflows at a lower price point than comprehensive observability suites.

6. Acceldata

Acceldata is a data observability platform founded in 2018 by Rohit Choudhary, Ashwin Rajeeva, and Vikas Sinha. The founders brought deep expertise from building data infrastructure at companies like Amazon and Hortonworks. Acceldata positions itself as the “first Data Observability platform,” going well past data quality to monitor pipeline performance, infrastructure health, and costs. 

Key Features:

• Multi-Dimensional Monitoring: Simultaneously tracks data quality, pipeline performance, and infrastructure metrics in one unified platform, eliminating monitoring silos.
• Intelligent Root Cause Analysis: Correlates data quality issues with infrastructure events, deployments, and lineage to pinpoint exact failure causes across the stack.
• Petabyte-Scale Architecture: Handles massive data volumes through in-memory processing and horizontal scaling without performance degradation.
• Hybrid & Multi-Cloud Support: Provides unified visibility across on-premise Hadoop, cloud warehouses, and mixed environments through a single interface.

Benefits:

• Unified Observability: Eliminates the need for multiple monitoring tools by covering data quality, performance, and infrastructure in one platform.
• Reduced MTTR: Intelligent correlation and root cause analysis dramatically reduce time spent investigating complex multi-system issues.
• Cost Optimization: Infrastructure monitoring and predictive analytics help optimize resource usage and prevent costly overruns.

Pricing:

Acceldata uses custom enterprise pricing based on deployment scale, data volume, and selected modules. The platform offers modular SKUs like “ADOC for Data Quality” and “ADOC for Cost Optimization” that can be purchased separately or bundled. Pricing factors include number of data sources, event volume, feature requirements, and support level. A 30-day free trial allows evaluation before purchase. As a premium solution aimed at large enterprises, Acceldata’s comprehensive capabilities often justify the investment through prevented incidents and optimized operations.

7. Metaplane

Metaplane is a data observability platform founded in 2020 by Kevin Hu, Peter Casinelli, and Guru Mahendran, who previously worked together at Google and Apptimize. The company set out to build the “Datadog for data” – making data monitoring as accessible and automated as application monitoring. In a significant validation of this vision, Datadog acquired Metaplane in 2024, promising deeper integration between application and data observability. 

Key Features:

• Automated Setup: Connects to data warehouses and immediately begins monitoring critical tables based on query patterns, requiring minimal configuration.
• Comprehensive Metrics Monitoring: Automatically tracks volume, freshness, schema changes, distribution patterns, uniqueness, and null rates across datasets.
• Integration Ecosystem: Connects with Slack, Teams, PagerDuty, dbt, and other tools, with feedback loops to continuously improve model accuracy.
• Warehouse Spend Monitoring: Tracks cloud data warehouse credit usage for cost anomalies as an add-on feature.

Benefits:

• Immediate Time to Value: Minutes from signup to monitoring, making it ideal for teams needing quick coverage without lengthy implementations.
• Democratized Access: Unlimited users ensure everyone from engineers to analysts can understand data health without budget constraints.
• Backed by Datadog: Integration with Datadog’s ecosystem promises unified observability across applications and data.

Pricing:

Metaplane offers a free tier for getting started with limited tables. Usage-based pricing centers on monitored tables, with costs scaling linearly – you pay only for tables actively monitored past a 30-day window. Team plans start around a few hundred dollars monthly for moderate usage, including a base number of tables with transparent overage rates. Enterprise plans provide custom pricing for thousands of tables, including SSO, advanced governance, and priority support. The platform’s affordability and transparent model make enterprise-grade observability accessible to mid-sized teams.

8. Informatica

Informatica is a data management pioneer founded in 1993 by Diaz Nesamoney and Gaurav Dhillon, making it one of the oldest and most established players in the data quality space. The company went public in 1999, was taken private in 2015, and returned to public markets in 2021 with a valuation exceeding $8 billion. Informatica Data Quality, part of their Intelligent Data Management Cloud (IDMC), represents decades of enterprise data management expertise. 

Key Features:

• AI-Powered Cleansing: CLAIRE AI engine suggests and applies transformations for standardization, parsing, and enrichment based on detected patterns.
• Advanced Matching & Deduplication: Uses sophisticated fuzzy matching algorithms to identify and consolidate duplicate records despite variations in data entry.
• Deep Integration: Embeds seamlessly with Informatica’s ETL, MDM, and governance tools for end-to-end data management.
• Reference Data Management: Includes pre-built accelerators for common standardization tasks like address validation and industry codes.

Benefits:

• Proven Enterprise Reliability: Decades of refinement and thousands of implementations ensure stability for mission-critical operations.
• Regulatory Compliance: Built-in features for data masking, privacy, and governance help meet stringent compliance requirements.
• Global Scale: Handles massive data volumes across complex multinational operations with consistent performance.

Pricing:

Informatica uses consumption-based pricing through IDMC, where organizations purchase credits based on usage including records processed and compute hours. The platform offers Basic, Advanced, and Enterprise editions with progressively more features. Pricing requires engagement with Informatica’s sales team for custom quotes based on data volume, environments, and bundled products. As a premium enterprise solution, Informatica commands higher prices justified by comprehensive capabilities, world-class support, and proven reliability at scale. Free trials may be available through sales engagement.

9. Talend

Talend was founded in 2005 by Bertrand Diard and Fabrice Bonan in France, emerging as an open-source alternative to expensive proprietary data integration tools. The company went public in 2016 and was subsequently acquired by Thoma Bravo in 2021, then by Qlik in 2023 for $2.3 billion. This acquisition combines Talend’s data integration and quality capabilities with Qlik’s analytics platform. Talend Data Quality maintains both its open-source heritage through Talend Open Studio and enterprise offerings through Talend Data Fabric. 

Key Features:

• Talend Trust Score™: Automatically calculates a 0-100 reliability score for datasets based on validity, completeness, and consistency factors.
• Self-Service Data Preparation: Intuitive spreadsheet-like interface enables business users to profile and clean data without coding expertise.
• Unified Platform Integration: Seamlessly works with Talend’s ETL, API, and MDM tools for comprehensive data management.
• Open Source Option: Talend Open Studio provides free access to basic profiling and cleansing capabilities.

Benefits:

• Accessibility Across Teams: Combines developer-friendly tools with business user interfaces, democratizing data quality management.
• Flexible Deployment Options: Choose between open-source, cloud, or on-premise deployment based on needs and budget.
• Rapid Implementation: Pre-built components and intuitive interfaces enable quick deployment and adoption.

Pricing:

Talend follows a subscription model as part of Talend Data Fabric, with annual or multi-year options. Pricing may be based on users, connectors, or data volume. A free trial is available for evaluation, and Talend Open Studio for Data Quality remains completely free for basic use. Platform editions likely span Standard through Enterprise tiers with scaling capabilities and pricing. Bundling with other Talend components often provides cost efficiencies. Under Qlik ownership, new “Qlik Enterprise Integrations” bundles may emerge. Contact Talend/Qlik sales for customized quotes based on specific requirements.

10. Anomalo

Anomalo is an AI-powered data quality platform founded in 2018 by Jeremy Stanley (former VP of Data Science at Instacart) and Elliot Shmukler (former Chief Product Officer at Instacart). The founders experienced firsthand how subtle data issues could impact business decisions and built Anomalo to automatically detect these “unknown unknowns” without manual rule configuration. 

Key Features:

• No-Code Validation Rules: Intuitive interface for defining business constraints like “transactions must equal subcategory sums” without writing code.
• Native Cloud Integrations: Deep optimization for Snowflake, Databricks, BigQuery, and Redshift with automatic table discovery and monitoring setup.
• Unstructured Data Support: Recently expanded to monitor document counts and patterns in unstructured data using ML extraction.
• Governance Features: Includes compliance checking and PII detection to maintain data integrity and regulatory standards.

Benefits:

• Catch Unknown Issues: Unsupervised ML discovers problems you didn’t know to look for, going well past rule-based monitoring.
• Strategic Platform Support: Backing by Snowflake and Databricks ensures optimal performance on modern cloud data platforms.
• Comprehensive Coverage: Monitors structured, semi-structured, and unstructured data from a single platform.

Pricing:

Anomalo follows enterprise custom pricing based on monitored data sources, volume, and advanced features. As a premium solution, it targets data-forward enterprises where preventing errors justifies the investment. Annual SaaS subscriptions are standard, potentially available through cloud marketplaces. No free tier exists, but proof-of-concept pilots help demonstrate value on actual organizational data. Pricing includes support and customer success resources to maximize platform value. Organizations should engage Anomalo’s sales team for quotes tailored to their specific scale and requirements.

What’s the role of machine learning in data quality assessment tools?

Machine learning (ML) plays a key role in modern data quality assessment tools. Instead of relying solely on predefined rules, these tools use ML algorithms to automatically identify unusual patterns or anomalies in your data. Think of it as having an intelligent assistant watching your data around the clock, noticing problems even before you do.

ML-driven data quality tools typically learn what’s “normal” for your data by analyzing historical patterns. When something unexpected happens, like a sudden drop in transaction volume or an unusual increase in missing values, the tool alerts you immediately. This proactive approach saves you from constantly writing new rules and lets your team spend more time solving problems rather than spotting them.

Tools such as Monte Carlo use machine learning specifically for anomaly detection, schema change detection, and identifying data drift. They help data engineers quickly pinpoint issues without digging through logs or SQL queries. By catching these problems early, businesses can avoid costly mistakes, ensure trustworthy dashboards, and maintain stakeholder confidence.

In short, machine learning transforms data quality from reactive firefighting into proactive monitoring. It reduces manual effort, increases data reliability, and frees up your team to focus on strategic projects.

How can data quality assessments reduce operational costs?

Data quality assessments help lower operational costs by identifying issues before they escalate. Poor data quality leads to incorrect insights, misguided business decisions, and costly remediation efforts. Regular assessments let your team catch these problems early, reducing the resources spent on fixing errors later.

For instance, a routine data quality check might reveal inaccurate customer information. Fixing this upfront means fewer misdirected marketing campaigns and less wasted effort contacting non-existent or incorrect leads. Your team spends less time correcting errors manually and more time on tasks that directly benefit the business.

By prioritizing regular data quality assessments, you can avoid unnecessary expenses and run your operations more efficiently.

What metrics should I use in a data quality assessment?

Choosing the right data quality metrics is critical for an effective data quality assessment. While specifics depend on your organization’s needs, here are key metrics you should consider:

• Completeness measures how much essential data is missing. For instance, if your customer records lack email addresses or phone numbers, your data completeness is low.
• Accuracy tracks how correct your data entries are. If customer addresses contain typos or incorrect zip codes, accuracy suffers.
• Consistency evaluates whether your data is uniform across systems. For example, a customer should have identical contact details in your CRM and billing system.
• Freshness (Timeliness) assesses whether data is updated often enough. If sales data isn’t updated daily or weekly as required, insights quickly become unreliable.
• Uniqueness checks for duplicate records. Multiple entries for the same customer or transaction skew analytics and cause confusion.

Focusing on these metrics helps ensure your data assessment targets real-world problems, keeping your data reliable, actionable, and trustworthy.

Conclusion

Ensuring data quality is no longer optional. It’s essential for any organization that makes decisions based on data. The tools we’ve covered can dramatically reduce time spent firefighting data issues while building trust in your analytics and AI initiatives.

Among these solutions, Monte Carlo stands out as the complete data + AI observability platform for organizations that need enterprise-grade reliability. But the best choice depends on your team’s size, technical expertise, budget, and specific requirements.

Here’s what makes Monte Carlo different. We help data teams catch issues before they impact business decisions. Companies like Nasdaq, Honeywell, and Roche trust us with their most critical data pipelines because we deliver results. Our ML-powered anomaly detection learns your data patterns automatically. No manual threshold setting, no constant tweaking. When something breaks, our automated root cause analysis and field-level lineage show you exactly what happened and why. Investigation time drops from hours to minutes. The result? Teams see 80% less data downtime and spend their time building instead of fixing. Book a Monte Carlo demo today and see how we can transform your data quality from a constant headache into a competitive advantage.

Our promise: we will show you the product.