7 best data quality tools for modern data teams

When data pipelines break, schemas drift, or null values slip through, your downstream analytics and machine learning (ML) models fail without warning. As data stacks grow more complex, ensuring data reliability has become a massive engineering challenge.

That reality has led to an explosion in data quality management software, but navigating the market is confusing. Vendors use terms like observability, testing, and governance interchangeably, making it difficult to understand what each tool actually does. 

This guide cuts through the confusion. Learn what data quality software is, how the best tools compare, and how to choose the right one for your business.

What is data quality software?

Data quality software is a category of tools designed to ensure that data remains accurate, complete, consistent, timely, and reliable across systems.

In a modern data stack, organizations ingest raw data from dozens of SaaS apps and databases into a centralized warehouse. Without data quality management tools, data teams are left flying blind. 

For instance, a critical table can go stale for days or an upstream API change might break a downstream dashboard without anyone noticing. The result: incorrect business decisions, hallucinating ML models, and hours of engineering time spent debugging preventable issues.

While specific features vary, the best data quality software typically provides five core capabilities:

• Data profiling: Automatically scanning datasets to understand their structure, distribution, and completeness before analysis begins.
• Data validation and testing: Running specific, rule-based checks (e.g., “this column cannot contain nulls”) to catch known errors.
• Monitoring and alerting: Continuously tracking data pipelines and notifying teams when something breaks.
• Anomaly detection: Using machine learning to identify unexpected patterns or spikes in data volume that rule-based checks might miss.
• Data observability: Providing end-to-end visibility into data health, including automated lineage and root-cause analysis.

7 best data quality tools

“Data quality tools” is an umbrella term that covers three functional categories: monitoring, testing, and management.

Modern platforms increasingly blend these capabilities into unified solutions. A data observability platform might include testing features, and a testing tool may offer basic monitoring. 

Here are seven of the best data quality software solutions available today.

1. Monte Carlo

Monte Carlo is an end-to-end data observability platform that uses machine learning to automatically detect anomalies in data freshness, volume, and schema.

‍Best use case: Large-scale data teams that need broad, automated coverage across complex pipelines without writing thousands of manual tests.

‍Capabilities: Monte Carlo excels at automated monitoring and anomaly detection. It also supports data management through column-level lineage and root-cause analysis, while offering custom testing rules to navigate complex data landscapes.‍

2. Great Expectations

Great Expectations is an open-source, Python-native framework for data validation. It allows data engineers to define “expectations” (assertions) about what their data should look like.

Best use case: Engineering teams that prefer code-first, highly customizable data quality checks integrated directly into their CI/CD pipelines.

Capabilities: Great Expectations is a pure data validation tool. It handles proactive testing brilliantly but doesn’t provide automated monitoring or broader data management features.

3. Soda

Soda is an open-source command-line tool and Python library that uses SodaCL — a human-readable YAML-based language — to define data quality checks.

‍Best use case: Teams that want declarative, easy-to-read data quality checks that both engineers and analysts can write and understand.

‍Capabilities: Soda Core focuses heavily on testing and validation. It offers basic monitoring capabilities through its cloud offering, but lacks the deep, automated anomaly detection of an observability platform.

4. Talend Data Quality

Now part of Qlik, Talend Data Quality is a data quality tool for enterprises that provides advanced data profiling, cleansing, standardization, and enrichment capabilities.

‍Best use case: Large enterprises that need end-to-end data quality governance, particularly for customer data or master data management (MDM) initiatives.

‍Capabilities: Talend is a heavy-duty data quality tool for enterprises, handling testing, monitoring, and deep data management and cleansing.

5. Informatica Data Quality

Informatica Data Quality is an AI-powered enterprise platform within the broader Informatica Intelligent Data Management Cloud (IDMC). 

‍Best use case: Highly regulated industries (finance, healthcare) that require strict compliance, deep data profiling, and comprehensive governance.

‍Capabilities: Informatica covers all bases: monitoring, testing, and data management. It uses AI to automate rule generation and anomaly detection at scale.

6. OvalEdge

OvalEdge is primarily a data catalog that integrates data quality capabilities directly into its governance framework. 

‍Best use case: Organizations that want data cataloging, lineage, and data quality metrics unified in a single, governance-focused tool.

‍Capabilities: OvalEdge leans heavily into data management and governance, providing testing and basic monitoring. Its core strength is making data quality visible to business users through the catalog.

7. dbt Core

dbt Core is primarily a data transformation framework, but it includes built-in testing capabilities that have become an industry standard.

‍Best use case: Analytics engineers who already use dbt to transform data in the warehouse and want to test that data within the same workflow.

‍Capabilities: dbt Core handles testing by running SQL-based assertions (like unique or not_null) during the transformation process. It doesn’t provide automated monitoring or anomaly detection.

How to choose the best data quality software

Selecting the right tool depends entirely on your organization’s data maturity and architecture.

If you’re a startup managing only a few core tables, an open-source testing framework like dbt Core or Great Expectations might be all you need. Larger organizations managing thousands of tables across a massive data lakehouse typically need the automated anomaly detection of an observability platform like Monte Carlo.

As you evaluate data quality solutions, consider:

• How easily the tool integrates with your existing warehouse and orchestrator
• Whether you need proactive testing (catching known errors) or automated monitoring (catching unknown anomalies)
• Whether your team has the bandwidth to maintain a heavy enterprise suite

What data quality metrics should you track?

Regardless of which data quality assessment tools you choose, you need a way to measure success. Track these five core metrics:

• Accuracy: Does the data reflect reality? (e.g., the customer’s correct address)
• Completeness: Are there missing values or nulls in critical fields?
• Consistency: Does a data point match across different systems? (e.g., Salesforce revenue vs. Stripe revenue)
• Timeliness: Is the data fresh enough to be useful for decision-making?
• Validity: Does the data conform to the required format or schema? (e.g., dates stored in YYYY-MM-DD format)

How Fivetran strengthens your data quality foundation

Data quality software is only as effective as the data it receives. If errors, missing records, or inconsistent schemas are introduced at the ingestion layer, those problems will propagate downstream. Pipeline reliability is a prerequisite for any data quality program.

That’s where Fivetran fits. It serves as the foundation layer that ensures your downstream data quality tools receive clean, trustworthy inputs. 

Fivetran improves data reliability at the ingestion layer through automated pipelines and cursor-based recovery. If a sync fails, Fivetran picks up exactly where it left off, preserving data integrity without creating duplicates. Its automated schema management applies data-type promotion and validation for consistent casting across all 750+ pre-built connectors, delivering standardized, normalized data into your warehouse.

Fivetran also includes built-in governance features, such as role-based access controls, metadata sharing, and a rigorous compliance posture. It maintains SOC 2, GDPR, HIPAA BAA, ISO 27001, PCI DSS Level 1, and HITRUST certifications to ensure secure data handling. 

Fivetran and data quality software are complementary layers. One without the other leaves massive gaps in your data strategy.

‍Request a demo today to see how Fivetran provides ingestion reliability and schema consistency.

FAQ

What are some open-source data quality tools?

Popular open-source options include Great Expectations (Python-native validation), Soda Core (YAML-based checks), dbt Core (built-in SQL testing), and Deequ (an AWS-developed tool for testing large datasets in Apache Spark).

What are data quality assurance tools?

Data quality assurance is the proactive process of preventing bad data from entering a system. Great Expectations or dbt Core act as data quality assurance tools by running tests in the pipeline and failing the build if the data doesn’t meet predefined rules.

What is the difference between data quality monitoring and testing tools?

Data quality testing tools run specific, rule-based checks to catch known issues (e.g., checking for nulls). Data quality monitoring tools continuously scan pipelines to detect unknown anomalies (e.g., a sudden 50% drop in row volume) using ML and automated alerts.

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