Bad data costs more than people think. IBM puts the figure at $3.1 trillion annually in the US alone.

But for most teams, the problem isn’t knowing data quality matters. It’s that fixing it takes too long, requires too much technical skill, or falls to someone who already has a full-time job doing something else.

This guide covers the best data quality software available right now, who each tool is actually built for, and how to pick the right one.

Video Overview

What is data quality software?

Data quality software helps you find, fix, and prevent problems in your data. That includes:

• Profiling: Understanding what’s in your data before you use it
• Cleansing: Fixing duplicates, nulls, formatting issues, and inconsistencies
• Validation: Checking that data meets defined rules before it moves downstream
• Monitoring: Getting alerted when something breaks in an ongoing pipeline

Some tools do all four. Most specialize in one or two.

The best data quality software

1. Mammoth Analytics: Best for finding and fixing quality issues without code

Most data quality tools tell you what’s wrong. Mammoth tells you what’s wrong and lets you fix it in the same place, without writing a single line of code.

When you open a dataset in Mammoth, it automatically runs a data quality report based on the DAMA framework, the industry standard for data quality measurement. You get a quality score out of 100 and a breakdown across six dimensions: completeness, uniqueness, timeliness, validity, accuracy, and consistency.

The report identifies specific issues and suggests fixes. You click to apply them. The fix gets added to your pipeline automatically, so the same cleanup runs every time new data comes in.

What makes it different

Most data quality tools are built for data engineers who want to write tests and rules in code. Mammoth is built for analysts, finance teams, and operations managers who want to understand and clean their data without a technical bottleneck.

An analyst found Mammoth by searching for a tool that could handle data cleanup without requiring SQL. After a 15-minute trial, they had their first pipeline running and their first quality issues fixed.

What it checks for

• Completeness: Missing and null values by column
• Uniqueness: Duplicate detection and key candidate identification
• Validity: Data type mismatches, invalid formats (email, phone, postal codes)
• Accuracy: Out-of-range values, incorrect formats
• Consistency: Cross-column logic violations (order date before ship date)
• Timeliness: Data freshness and update frequency

Pricing

Pricing starts at $16/month billed annually. Enterprise pricing is custom. See the Mammoth pricing page for details.

Good fit if:

• Your analysts or finance team are the ones dealing with data quality issues
• You want to fix issues, not just flag them
• You need data quality built into your preparation pipeline, not bolted on separately

Not the right fit if:

• You need continuous observability monitoring across a complex data warehouse
• Your team wants to write quality rules as code
• You need master data management or entity resolution at enterprise scale

Request a demo

2. Great Expectations: Best open-source data testing

Great Expectations is the most widely adopted open-source data quality framework. You define “expectations” about your data in Python, like “this column should never be null” or “values should be between 0 and 100,” and the tool validates your data against them automatically.

It integrates with dbt, Airflow, Spark, and most modern data stacks. It’s free, flexible, and has a large community.

Pros:

• Free and open source
• Highly flexible, works with virtually any data environment
• Strong community and documentation
• Integrates well with dbt and orchestration tools like Airflow

Cons:

• Requires Python knowledge to set up and maintain
• No UI for business users
• Setup and configuration are time-consuming
• Needs a separate tool for fixing issues, only flags them

Bottom line: The go-to choice for data engineering teams that want code-first quality testing baked into their pipelines. Not usable without technical resources.

3. Monte Carlo: Best for data pipeline observability

Monte Carlo is a data observability platform. It monitors your pipelines and alerts you when something breaks, data volumes change unexpectedly, or freshness falls behind schedule.

It connects to your data warehouse, maps your data lineage, and uses machine learning to detect anomalies without requiring you to write rules manually. When something goes wrong, it tells you what broke and what downstream assets are affected.

Pros:

• Automated anomaly detection, no manual rule writing required
• Full data lineage visibility across your stack
• Fast time to value for observability use cases
• Strong integrations with Snowflake, BigQuery, Databricks, and dbt

Cons:

• Focused on monitoring, not fixing. You still need separate tools to resolve issues
• Expensive at enterprise scale
• Requires a cloud data warehouse to get the most value
• Overkill for teams that don’t have complex pipeline infrastructure

Bottom line: The strongest option for data engineering teams who need to monitor pipeline health at scale. Not a fit for teams who need to clean and fix data directly.

4. Talend Data Quality: Best for enterprise ETL with quality built in

Talend (now part of Qlik) offers data quality as part of its broader data integration platform. It covers profiling, cleansing, enrichment, and governance in one suite.

For organizations already using Talend for ETL, the data quality capabilities integrate naturally. For everyone else, it’s a significant investment to adopt.

Pros:

• End-to-end quality management integrated with data integration workflows
• Strong governance and compliance features for regulated industries
• Mature platform with a large enterprise customer base

Cons:

• Steep learning curve, requires specialist knowledge
• Expensive at enterprise scale
• Interface is complex for non-technical users
• Slower to implement than newer tools

Bottom line: Good for enterprises that are already in the Talend ecosystem or need tightly integrated ETL and data quality governance.

5. Informatica Data Quality: Best for large enterprises with MDM needs

Informatica has been in the data quality space for decades. Its DQ tooling is part of the Intelligent Data Management Cloud and is particularly strong for organizations that also need master data management, data cataloging, and compliance governance in the same platform.

Note: Informatica was acquired by Salesforce in November 2025. Organizations with concerns about pricing or roadmap direction may want to evaluate alternatives.

Pros:

• Comprehensive, mature tooling covering DQ, MDM, governance, and lineage
• Strong in regulated industries like healthcare and financial services
• AI-powered CLAIRE engine for automated recommendations
• Deep connector library for legacy and cloud systems

Cons:

• Expensive and complex to implement
• Licensing is opaque and often requires negotiation
• Overkill for teams without full enterprise data management needs
• Business users cannot operate it independently

Bottom line: The most feature-complete option for large enterprises that need DQ as part of a wider data governance program.

6. Ataccama ONE: Best for governance-heavy, regulated industries

Ataccama ONE combines data quality, governance, and master data management in a single platform with a focus on usability. It uses AI and ML for anomaly detection, matching, and deduplication, and its interface is more accessible than Informatica or Talend.

Pros:

• More accessible UI than most enterprise data quality tools
• Strong AI-powered matching and deduplication for MDM use cases
• Covers quality, governance, and mastering in one platform
• Good fit for regulated industries (banking, insurance, healthcare)

Cons:

• Still requires technical expertise to configure and maintain
• Enterprise pricing is significant
• Slower to implement than lighter-weight alternatives
• Not built for business user self-service

Bottom line: A strong Informatica alternative for organizations that need governance and MDM but want a more modern interface.

7. dbt tests: Best for transformation teams on cloud warehouses

dbt (data build tool) is primarily a transformation framework, but its built-in test functionality makes it one of the most widely used data quality tools for cloud data warehouse teams.

You write tests in YAML alongside your transformation logic. Tests check for things like null values, uniqueness constraints, and referential integrity. They run automatically as part of your transformation runs.

Pros:

• Free and open source
• Tests live alongside transformation code, so quality is built into the pipeline
• Huge community and rich ecosystem of third-party test packages
• Works natively with Snowflake, BigQuery, Redshift, and Databricks

Cons:

• Requires SQL and dbt knowledge to use
• No UI, entirely code-based
• Only works if your team is already using dbt for transformations
• No fixing capabilities, only flagging

Bottom line: If your team is already on dbt, using its built-in tests is the simplest way to add data quality checks. If you’re not on dbt, this isn’t where to start.

8. Soda: Best for collaborative data quality across teams

Soda is a modern data quality platform that combines automated monitoring with collaborative workflows. You write quality checks in SodaCL, a human-readable YAML-like language, and Soda runs them on a schedule and alerts the right people when something fails.

It’s designed for teams where data producers and consumers need to agree on quality standards and share responsibility for maintaining them.

Pros:

• Human-readable check syntax, more accessible than Python-based tools
• Free tier available for smaller teams
• Collaborative workflows with notifications and assignments
• Good integration with dbt and modern data stacks

Cons:

• Still requires technical setup and maintenance
• Less mature than Great Expectations or Monte Carlo for large-scale use
• Not designed for business users to operate independently

Bottom line: A good middle ground between the raw flexibility of Great Expectations and the cost of enterprise platforms. Worth evaluating for teams that want to share quality ownership across the organization.

How to choose the right data quality software

Do your users need to fix issues, or just find them?

Monitoring tools like Monte Carlo and Great Expectations are great at surfacing problems. They don’t help you fix them. If your team needs to clean data directly, look at tools like Mammoth that combine detection and remediation in one place.

Who’s going to operate the tool day to day?

If the answer is data engineers, any of the tools above can work. If the answer is analysts, finance leads, or operations managers, Mammoth is the only option on this list that was built for those users.

Do you need monitoring, cleansing, or both?

• Pipeline monitoring and alerting: Monte Carlo or Soda
• Code-first testing in your data pipeline: Great Expectations or dbt tests
• Enterprise quality + governance + MDM: Informatica or Ataccama
• Finding and fixing issues without code: Mammoth Analytics

What does your infrastructure look like?

• Already using dbt on a cloud warehouse: dbt tests, Great Expectations, or Soda
• Complex enterprise data warehouse with governance requirements: Informatica, Talend, or Ataccama
• Multi-source data with business user teams: Mammoth Analytics

Frequently asked questions

What are the six dimensions of data quality?

The DAMA framework defines six: completeness (no missing values), uniqueness (no duplicates), validity (correct format and type), accuracy (correct values), consistency (logical relationships between fields), and timeliness (data is fresh). Mammoth’s data quality report measures all six automatically.

What’s the difference between data quality and data observability?

Data quality is about whether your data is correct. Data observability is about whether your pipelines are healthy. Tools like Monte Carlo focus on observability. Tools like Mammoth and Great Expectations focus on quality. You often need both in a mature data operation.

Can non-technical users manage data quality?

With most tools on this list, no. Great Expectations, dbt tests, and Monte Carlo all require technical expertise to operate. Mammoth is the exception. It’s built specifically so analysts and business teams can identify and fix data quality issues without writing code or involving IT.

How does data quality affect analytics and AI?

Poor quality data produces unreliable reports and broken AI models. IBM identifies data quality as the number one challenge for generative AI adoption. Cleaning data at the source, before it reaches your BI tool or model, is far more effective than trying to account for errors downstream.

The bottom line

Data quality tools fall into two buckets. There are tools that help engineers monitor and test pipelines, and there are tools that help teams actually fix the data.

If your priority is pipeline monitoring, Great Expectations, dbt tests, Monte Carlo, or Soda are all solid options depending on your stack and budget.

If your priority is getting clean, reliable data into the hands of people who actually use it, without requiring a data engineer to sit in the middle, Mammoth Analytics is the only tool on this list built for that job from the ground up.

Request a Mammoth demo