Choosing the right data quality tool in 2025 is essential for organizations that want to trust their data, reduce risk, and make confident decisions. As the volume and complexity of data continue to grow, the need for tools that can detect anomalies, enforce validation rules, and maintain accurate, consistent datasets has never been greater. This guide highlights the top data quality tools in 2025, covering enterprise-grade platforms, modern observability solutions, and open-source frameworks that help teams manage data accuracy, completeness, reliability, and freshness at scale.
What does a data quality tool do?
A data quality tool is designed to ensure that an organization’s data is accurate, consistent, complete, and reliable. These tools help identify and fix data errors, such as duplicates, inconsistencies, and missing values, while also providing insights into data patterns and anomalies. By leveraging data quality tools, businesses can trust their data for analytics, reporting, and operational processes.
Why are data quality tools important?
Data quality tools are essential for organizations seeking to:
- Improve Decision-Making: Reliable data ensures that business decisions are based on accurate and up-to-date information.
- Enhance Customer Experiences: Clean and accurate data helps provide personalized and efficient customer service.
- Maintain Compliance: Many industries require adherence to strict data regulations, which data quality tools help achieve.
- Reduce Costs: Poor data quality can lead to costly mistakes and inefficiencies, which these tools help mitigate.
- Enable Growth: Scalable data quality solutions ensure that organizations can manage and leverage data effectively as they grow.
Top 21 data quality tools of 2025
Enterprise-grade platforms
These tools are designed for large-scale, multi-domain, and often multi-cloud deployments.
1. Informatica Data Quality
A mature platform known for enterprise-scale deployments across hybrid and multi-cloud environments.
Core Features:
- AI-driven data discovery and profiling
- Rule-based cleansing, matching, and enrichment
- Prebuilt accelerators for domains like customer and product
- Real-time quality dashboards and monitoring
- Integration with Informatica MDM and IDMC
2. Secoda
Secoda is an all-in-one enterprise data intelligence platform combining cataloging, governance, and observability. Secoda is the only platform in its category to offer a built-in observability suite alongside AI-powered catalog and governance tools.
Core Features
- Built-in observability suite with both no-code and custom SQL monitors for freshness, uniqueness, schema changes, query performance, job health, infrastructure costs, and much more
- Data Quality Scores (DQS) aggregate key metrics into a single, actionable health score
- Real-time anomaly detection and proactive threshold-based alerts
- Integration with dbt, tests, and monitors
- Usage analytics to identify underused or at-risk assets
- Alerts appearing within column-level-lineage and impact analysis to help teams quickly identify and resolve incidents across complex systems
- Multi-channel alerting via Slack, email, and in-app notifications
- Smart alerting groups related incidents and filters out false positives to reduce noise
- Deep integration of observability with catalog context for smarter, more contextual monitoring
- Secoda AI chatbot assists with documentation, data questions, and monitor suggestions on critical assets
Best suited for organizations looking for a unified platform that combines data cataloging, governance, and observability with AI-powered automation, reducing the need for multiple standalone tools.
3. Talend Data Quality
Talend offers open-source flexibility with strong integration into ETL workflows and pipelines.
Core Features:
- Interactive data profiling and anomaly detection
- Built-in deduplication and standardization logic
- Cleansing using Talend jobs or external APIs
- Real-time quality alerts and automated workflows
- Open-source and commercial versions available
4. Ataccama ONE
A unified data management platform that combines quality, governance, and MDM with powerful automation.
Core Features:
- Self-service data quality for business users
- Machine learning-driven anomaly detection
- Custom rules and AI-generated suggestions
- Visual workflow designer for DQ pipelines
- Supports both structured and unstructured data
5. Collibra
Collibra excels at governance and metadata management with growing capabilities in data quality.
Core Features:
- Data quality rules engine with business context
- Centralized rule management and scoring
- Lineage-aware quality insights
- Integrates with external observability tools
- Part of the broader Collibra Data Intelligence Cloud
6. SAP Data Intelligence
Best suited for SAP-first organizations managing complex data workflows across cloud and on-premise systems.
Core Features:
- Centralized metadata and data quality orchestration
- Machine learning-based quality profiling
- Seamless integration with SAP HANA, BW, and other SAP products
- Visual data pipeline designer with validation nodes
- Governance-first architecture with audit support
7. Oracle Enterprise Data Quality (EDQ)
Designed for large enterprises using Oracle infrastructure, with high-volume processing capabilities.
Core Features:
- Real-time data standardization and cleansing
- Matching, parsing, and address validation
- Reusable rule libraries and audit tracking
- Native integration with Oracle GoldenGate and Fusion Apps
- Support for multi-domain quality initiatives
8. IBM InfoSphere QualityStage
Highly robust quality and cleansing tool ideal for resolving complex entity relationships.
Core Features:
- Probabilistic and deterministic matching
- Rule-based standardization and parsing
- Data lineage and quality scorecards
- Real-time processing on large volumes
- Deep integration with IBM InfoSphere stack
9. Microsoft Purview
Part of the Azure ecosystem, combining governance, lineage, and quality control.
Core Features:
- Automated classification and data labeling
- Quality scoring via built-in policies
- End-to-end data lineage for quality traceability
- Native integration with Power BI and Synapse
- Role-based access and auditing for governance
Observability & ML-driven quality tools
Ideal for detecting data incidents, anomalies, and freshness issues in real time.
10. Monte Carlo
A leader in data observability focused on detecting, alerting, and preventing data quality issues in pipelines.
Core Features:
- End-to-end data monitoring across freshness, volume, and schema
- Incident management workflows
- ML-powered anomaly detection
- Data lineage-aware alerting
- Integrates with Airflow, dbt, Snowflake, and more
11. Anomalo
Uses machine learning to detect unexpected data patterns and anomalies without requiring manual rule-writing.
Core Features:
- Automated column-level anomaly detection
- Root cause analysis and alerting
- Quality scoring across freshness, nulls, distribution, etc.
- Visual dashboards for tracking issues over time
- Integration with BigQuery, Snowflake, Redshift
12. Bigeye
Focused on data SLAs and metric-based monitoring, empowering teams to detect issues proactively.
Core Features:
- Custom monitors for metrics, freshness, and distribution
- Auto-recommendation of metrics based on schema
- SLA tracking with incident timelines
- dbt, Airflow, and Looker integrations
- Root cause insights and lineage mapping
13. Lightup
Optimized for SQL-first teams, enabling quick creation of custom data quality rules and real-time alerting.
Core Features:
- Declarative SQL-based quality rule engine
- Real-time anomaly alerts
- Integration with messaging platforms and alerting tools
- UI for non-technical users to author rules
- Integration with Snowflake, Redshift, and Databricks
14. Acceldata
Focuses on operational observability across data pipelines, performance, and cost governance.
Core Features:
- Data reliability monitoring across ingestion and transformation
- Bottleneck detection in pipelines
- Data drift detection and schema validation
- Integration with Spark, Hive, Kafka, and Airflow
- Used by enterprises with high-volume data operations
15. Datafold
Best-in-class for data diffing and regression testing across warehouse changes and dbt workflows.
Core Features:
- Data diffing between staging and prod environments
- Native dbt and CI/CD integration
- Smart test suggestions
- Column-level lineage for debugging
- Detects silent data breaks before they impact users
Developer-friendly & open source tools
16. Great Expectations
The most popular open-source framework for writing and validating data expectations.
Core Features:
- Declarative tests using YAML or Python
- Profiling, documentation, and validation in one
- Data docs for collaboration and transparency
- Integration with Pandas, Spark, and SQLAlchemy
- CLI and orchestration support
17. Soda Core
A CLI-based framework that enables test-driven data quality directly from YAML configurations.
Core Features:
- Write assertions in YAML or SQL
- Schema checks, distribution tests, missing values, and duplicates
- Integration with Soda Cloud for monitoring
- CI/CD pipeline support
- Alerts via Slack, MS Teams, PagerDuty
18. Deequ
An open-source Scala-based tool developed by AWS for validating large-scale datasets on Spark.
Core Features:
- Automated constraint suggestions
- Anomaly detection over time-series data
- Built on Apache Spark
- Declarative API for data validation
- Best for distributed environments
19. dbt Core + dbt Tests
While not a standalone DQ platform, dbt Core offers essential validation capabilities for modern data teams.
Core Features:
- Built-in schema and data tests
- Custom tests using Jinja macros
- CI/CD integrations for automated QA
- Compatible with Datafold and observability tools
- Central to many data transformation workflows
20. Pandera
A rising open-source tool that adds statistical validation for data pipelines built in Python.
Core Features:
- Validate pandas, Dask, and PySpark dataframes
- Type checks, distribution checks, and null handling
- Hypothesis testing support
- Seamless integration into Python ETL workflows
- Great for data science and analytics teams
21. OpenMetadata
An open-source metadata platform with growing data quality extensions.
Core Features:
- Data quality test creation and scheduling
- Integrated lineage and schema validation
- Rule-based and dbt-integrated tests
- Scorecards and alerts via OpenMetadata UI
- Built for extensibility and open standards
How to choose a data quality tool
Selecting a data quality tool starts with aligning capabilities to your organization's specific needs. The right solution should improve data accuracy, reduce manual effort, and scale with your infrastructure. Here are the key factors to evaluate:
- Business requirements: Identify the types of data issues you need to solve, whether it's deduplication, schema drift, freshness, or governance alignment. Match tools to these use cases.
- Scalability and performance: Ensure the platform can handle your current data volume and future growth. Look for tools that support automation, parallel processing, and cloud-native architectures.
- Ease of use: Prioritize platforms with intuitive interfaces and flexible workflows that support both data engineers and business users. Self-service options reduce dependency on technical teams.
- Integration capabilities: Choose tools that work with your existing stack, including your data warehouses, ETL pipelines, transformation tools like dbt, and BI platforms. API access and native connectors are key.
- Pricing and licensing: Review the pricing model based on usage, users, or features. Make sure it fits your budget and provides transparency as your needs scale.
- Support and onboarding: Strong customer support, detailed documentation, and onboarding assistance can accelerate implementation and drive long-term success.
- Data Quality Score (DQS): Use a measurable scoring system to benchmark and monitor quality over time. Tools like Secoda’s Data Quality Score offer clear metrics for accuracy, completeness, freshness, and documentation coverage, making it easier to track progress and prioritize improvements across your datasets.
Final thoughts
Whether you are part of a fast-moving data team or managing data at enterprise scale, the right data quality tool can help you stay ahead of errors, reduce operational costs, and build trust across the business. The platforms listed above represent the top data quality tools in 2025, offering powerful features like real-time monitoring, schema validation, anomaly detection, and data profiling.
If you're looking for a modern platform that combines data cataloging, lineage, and quality scoring in one place, consider trying Secoda. With built-in AI, automated monitors, and a user-friendly interface for both technical and business teams, Secoda helps you improve data quality without adding overhead.
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