Data quality tools automatically detect and fix issues like missing values, duplicates, formatting errors, and invalid data before they break your reports and dashboards.
This guide compares 15 tools across pricing, features, and ideal use cases.
Quick comparison table
Tool | Best For | Starting Price | Implementation Time | Code Required |
|---|---|---|---|---|
Mammoth Analytics | Business analysts, self-service | $16/month | 1-3 days | No |
Great Expectations | Data engineers, Python users | Free (open source) | 1-2 weeks | Yes |
Monte Carlo | Data observability, monitoring | $50,000/year | 2-4 weeks | Minimal |
Informatica DQ | Enterprise, compliance-heavy | $200,000+/year | 3-6 months | Minimal |
Talend Data Quality | Mid-market, data integration | $50,000-150,000/year | 6-12 weeks | Minimal |
Soda | SQL users, CI/CD integration | Free tier available | 1-2 weeks | SQL |
Ataccama | Large enterprises, MDM | $100,000+/year | 3-6 months | Minimal |
Collibra | Data governance, cataloging | $50,000-300,000/year | 2-6 months | No |
dbt | Analytics engineers, transformation | Free tier available | 1-4 weeks | SQL |
Datafold | Data diffing, CI/CD | $25,000+/year | 1-2 weeks | Minimal |
Bigeye | Automated monitoring | $35,000+/year | 2-3 weeks | Minimal |
Anomalo | ML-based monitoring | $40,000+/year | 2-4 weeks | Minimal |
Databand | Pipeline observability | Contact for pricing | 2-4 weeks | Minimal |
OpenRefine | Small datasets, one-off cleaning | Free (open source) | 1 day | No |
Trifacta | Data wrangling, visual prep | $10,000+/year | 2-4 weeks | No |
The 15 best data quality tools
1. Mammoth Analytics
What it does: Visual, no-code data quality platform that generates DAMA framework assessments in under 2 minutes and creates fix pipelines automatically.
Best for: Business analysts who need self-service data quality without IT dependencies. Teams tired of waiting weeks for data engineers.
Key features:
- One-click data quality reports covering all 6 DAMA dimensions
- “Apply Fix” buttons that generate transformation pipelines automatically
- AI-powered bulk replace for standardizing messy data
- Works on 1M to 1B+ row datasets
- Built-in dashboard creation after data cleaning
Pricing: $16/month
Implementation time: 1-3 days. Upload data or connect database, click “Data Quality,” start fixing issues.
Code required: No. Entirely visual interface.
Pros: Zero learning curve, business user focused, fast time-to-value, handles massive datasets, complete audit trails for compliance, most affordable option.
Cons: Newer player (less enterprise case studies than Informatica), primarily focused on data prep + quality vs. full MDM.
2. Great Expectations
What it does: Open-source Python library for data testing, documentation, and profiling.
Best for: Data engineers and technical teams already working in Python who want version-controlled data tests.
Key features:
- Write data quality tests in Python
- Integrates with CI/CD pipelines
- Auto-generates data documentation
- Strong community and documentation
- Free core version
Pricing: Free (open source), paid cloud version for collaboration
Implementation time: 1-2 weeks for technical users
Code required: Yes. Python expertise required.
Pros: Free, flexible, great for technical teams, version control, strong CI/CD integration.
Cons: Business analysts can’t use it, requires Python skills, no GUI, limited profiling compared to commercial tools.
Best for: Teams where data engineers write all quality checks and Python is already the standard.
3. Monte Carlo
What it does: Data observability platform that uses ML to detect anomalies in data pipelines without manual rules.
Best for: Modern data stack teams (Snowflake, Databricks, BigQuery) who need automated monitoring.
Key features:
- Automatic anomaly detection
- No manual rule writing required
- Pipeline health monitoring
- Incident management and alerting
- Lineage tracking
Pricing: Starts around $50,000/year
Implementation time: 2-4 weeks
Code required: Minimal (mostly configuration)
Pros: Fast setup, automatic detection, modern integrations, good for complex pipelines.
Cons: Focused on monitoring, not fixing. You still need other tools to clean data. Premium pricing.
Best for: Organizations with modern data warehouses who want monitoring but have separate transformation tools.
4. Informatica Data Quality
What it does: Enterprise data quality platform covering profiling, cleansing, matching, and monitoring.
Best for: Fortune 500 companies with dedicated data quality teams and complex compliance requirements.
Key features:
- Comprehensive DQ coverage
- Pre-built rules and accelerators
- Strong MDM integration
- Handles complex matching and deduplication
- Proven at massive scale
Pricing: $200,000+/year (including implementation, training, support)
Implementation time: 3-6 months
Code required: Minimal (mostly configuration)
Pros: Enterprise-proven, comprehensive features, strong support, handles any scale.
Cons: Expensive, long implementation, steep learning curve, requires specialized expertise, overkill for most mid-market companies.
Best for: Large enterprises with budgets over $200K and dedicated data governance teams.
5. Talend Data Quality
What it does: Data quality tools integrated with Talend’s ETL/integration platform.
Best for: Mid-market companies wanting combined data integration and quality tools.
Key features:
- Visual pipeline development
- Built-in data profiling
- Cleansing and standardization
- Open-source core (paid enterprise version)
- Integration with Talend ETL
Pricing: $50,000-150,000/year depending on scale
Implementation time: 6-12 weeks
Code required: Minimal (visual interface)
Pros: Unified platform for integration + quality, lower cost than Informatica, visual interface.
Cons: Performance issues with very large datasets, limited AI features, enterprise features require paid version.
Best for: Teams already using Talend for ETL or wanting one vendor for integration and quality.
6. Soda
What it does: SQL-based data quality testing integrated with data pipelines.
Best for: Teams comfortable with SQL who need quality checks in their orchestration tools.
Key features:
- Write tests in SQL
- Integrates with Airflow, dbt, etc.
- Open-source core
- Cloud collaboration features
- Anomaly detection (paid)
Pricing: Free tier available, paid plans start around $15,000/year
Implementation time: 1-2 weeks
Code required: SQL
Pros: SQL-based (familiar), good orchestration integration, affordable, open-source core.
Cons: Limited profiling, requires knowing what to test, best for validation not discovery.
Best for: SQL-comfortable teams needing validation tests in existing data pipelines.
7. Ataccama ONE
What it does: Enterprise data quality and master data management platform.
Best for: Large organizations needing both DQ and MDM with AI-powered capabilities.
Key features:
- AI-powered profiling and matching
- Full MDM capabilities
- Data catalog integration
- Self-service data preparation
- Cloud and on-premise
Pricing: $100,000+/year
Implementation time: 3-6 months
Code required: Minimal
Pros: Strong AI features, unified DQ + MDM, good for complex matching problems.
Cons: Expensive, long implementation, complex for simple use cases.
Best for: Enterprises needing MDM alongside data quality.
8. Collibra
What it does: Data governance and cataloging platform with quality monitoring.
Best for: Enterprise data governance initiatives where quality is part of broader governance program.
Key features:
- Data cataloging and discovery
- Business glossary
- Data lineage
- Quality dashboards
- Policy management
Pricing: $50,000-300,000/year depending on scale
Implementation time: 2-6 months
Code required: No
Pros: Comprehensive governance, strong catalog, good for documentation and discovery.
Cons: More governance than quality focused, expensive, won’t fix your data (just documents problems).
Best for: Large organizations building comprehensive data governance programs.
9. dbt (with testing features)
What it does: Data transformation tool with built-in data quality testing.
Best for: Analytics engineers transforming data in SQL warehouses.
Key features:
- SQL-based transformations
- Built-in data tests
- Documentation generation
- Version control
- Strong community
Pricing: Free (open source), dbt Cloud starts $50/month
Implementation time: 1-4 weeks
Code required: SQL
Pros: Free core version, excellent for transformations, strong adoption, good testing framework.
Cons: Limited quality features compared to dedicated tools, requires SQL, focused on transformation not profiling.
Best for: Teams doing SQL-based transformations who want basic quality tests built in.
10. Datafold
What it does: Data diffing and quality monitoring for CI/CD workflows.
Best for: Teams wanting to test data changes before deployment.
Key features:
- Column-level diffing
- CI/CD integration
- Impact analysis
- Automated regression testing
- Works with dbt
Pricing: Starts around $25,000/year
Implementation time: 1-2 weeks
Code required: Minimal
Pros: Unique diffing capability, good CI/CD fit, prevents breaking changes.
Cons: Focused on change detection, not comprehensive quality, requires modern stack.
Best for: Teams using dbt or other transformation tools who need change validation.
11. Bigeye
What it does: Automated data quality monitoring with ML-powered anomaly detection.
Best for: Teams wanting automatic monitoring without manual rule creation.
Key features:
- Automatic metric tracking
- ML anomaly detection
- Slack/email alerts
- SQL and no-SQL support
- Lineage tracking
Pricing: Starts around $35,000/year
Implementation time: 2-3 weeks
Code required: Minimal
Pros: Fast setup, automatic detection, good alert system, modern interface.
Cons: Monitoring focused (doesn’t fix issues), mid-tier pricing, requires cloud data warehouse.
Best for: Cloud data warehouse users wanting automatic monitoring.
12. Anomalo
What it does: ML-based data quality monitoring and validation.
Best for: Teams with Snowflake, Databricks, or BigQuery needing smart monitoring.
Key features:
- Unsupervised ML for anomaly detection
- Automatic checks (no rules needed)
- Root cause analysis
- Integration with data catalogs
- Historical trending
Pricing: Starts around $40,000/year
Implementation time: 2-4 weeks
Code required: Minimal
Pros: Intelligent detection, no manual rules, good integrations, helpful root cause features.
Cons: Premium pricing, monitoring not fixing, requires modern data stack.
Best for: Modern data teams wanting intelligent monitoring without rule maintenance.
13. Databand
What it does: Pipeline observability and data quality monitoring.
Best for: DataOps teams monitoring complex data pipelines.
Key features:
- Pipeline execution tracking
- Data quality alerts
- Airflow integration
- Cost monitoring
- Impact analysis
Pricing: Contact for pricing
Implementation time: 2-4 weeks
Code required: Minimal
Pros: Good pipeline visibility, cost tracking, strong Airflow integration.
Cons: More observability than quality focused, requires orchestration tool.
Best for: Teams running Airflow or similar orchestration needing pipeline monitoring.
14. OpenRefine
What it does: Free, open-source tool for cleaning messy data in small to medium datasets.
Best for: One-off data cleaning projects, small datasets, budget-conscious teams.
Key features:
- Visual interface
- Faceting and filtering
- Reconciliation services
- Completely free
- Desktop application
Pricing: Free (open source)
Implementation time: Same day
Code required: No
Pros: Free, visual, easy to learn, good for exploration.
Cons: Desktop only, doesn’t scale to large datasets, no automation, manual process.
Best for: Small datasets (<100K rows), one-time cleaning projects, individuals or small teams.
15. Trifacta (now part of Alteryx)
What it does: Visual data wrangling and preparation with AI suggestions.
Best for: Business analysts needing visual data preparation.
Key features:
- Visual interface
- AI-suggested transformations
- Interactive profiling
- Recipe-based approach
- Cloud and desktop versions
Pricing: Starts around $10,000/year
Implementation time: 2-4 weeks
Code required: No
Pros: Visual and accessible, good AI suggestions, works for non-technical users.
Cons: Now part of Alteryx (integration unclear), performance issues with large data, mid-tier pricing.
Best for: Teams wanting visual data prep for analysts but already invested in Alteryx ecosystem.
How to choose the right tool
If you’re a business analyst who needs self-service: Choose: Mammoth Analytics, Trifacta, or OpenRefine (for small data)
If you’re a data engineer comfortable with code: Choose: Great Expectations, dbt, or Soda
If you need monitoring and alerting: Choose: Monte Carlo, Bigeye, Anomalo, or Databand
If you’re an enterprise with compliance requirements: Choose: Informatica, Ataccama, or Collibra
If you want unified data integration + quality: Choose: Talend
If you’re testing data transformations: Choose: Datafold or dbt
Key evaluation criteria
- User expertise required: Can business analysts use it, or only data engineers?
- Implementation timeline: Days, weeks, or months before seeing value?
- Scalability: Will it handle your data volume in 2 years, not just today?
- Total cost: License + implementation + training + ongoing maintenance?
- Fix or monitor: Does it actually clean data, or just tell you what’s broken?
- Integration: Works with your existing data sources and destinations?
What to test during evaluation
- Upload your ugliest production data (not vendor demo data)
- Have actual users try it (analysts, not just IT)
- Run a full quality assessment (see what it finds)
- Try fixing data quality issues (how hard is it actually?)
- Check performance (can it handle your data volume?)
- Calculate true cost (implementation + training + ongoing)
Common mistakes to avoid
- Choosing based on features instead of your actual problems
- Letting only IT evaluate tools that business users will need to use
- Skipping POC with real messy data
- Ignoring implementation timeline (6 months delay = 6 months of ongoing problems)
- Forgetting ongoing maintenance costs
Bottom line
For business analyst self-service: Mammoth Analytics offers the fastest path to value with zero learning curve at just $16/month—making it the most affordable option for teams needing self-service data cleaning.
For technical teams with Python: Great Expectations provides free, flexible quality testing.
For monitoring modern data stacks: Monte Carlo, Bigeye, or Anomalo deliver automatic detection.
For enterprise compliance needs: Informatica remains the proven choice despite high costs.
The right tool depends on whether your business analysts or data engineers will use it, how fast you need results, and whether you need monitoring or actual data fixing.
Learn more about data quality best practices and data quality standards to build a strong foundation for your data management program.
Try Mammoth free: https://mammoth.io/signup
