Top data quality tools in 2025: Options compared

Data quality is an essential cornerstone of enterprise success. With the proliferation of AI-driven analytics, real-time decision-making, and complex data ecosystems, ensuring the accuracy, consistency, and reliability of data is more critical than ever. 

Over time, data quality tools have evolved to meet these rising demands by offering advanced features that cater to the needs of modern data management professionals.

In this article, we will examine the top data quality tools of 2025, focusing on key players in the space and how they can be integrated with data governance management platforms to create a unified solution for data-driven organizations.

What is data quality?

Data quality refers to the condition of a dataset based on factors such as accuracy, completeness, reliability, and relevance. 

High-quality data is essential for effective decision-making, operational efficiency, and compliance with regulatory standards. Poor data quality can lead to erroneous conclusions, financial losses, and reputational damage.

Some dimensions of data quality can include:

How do I improve my data quality?

Improving data quality isn’t a one-and-done project—it’s an ongoing, evolving process.

Here’s how successful organizations approach it:

1. Define data quality metrics specific to your business context 
   What makes data of good quality depends entirely on how you’re using it. Define what that looks like for each use case and each department using the information.
   
   
2. Establish baselines & targets 
   You can’t improve what you don’t measure. Start by understanding your current quality levels, then set improvement goals that are achievable over time.
   
   
3. Implement quality controls at multiple points 
   Don’t wait until data reaches dashboards to check quality. Build controls into collection forms, ETL processes, and transformation logic to keep everyone on track toward your goals.
   
   
4. Automate monitoring & testing
   Manual quality checks don’t scale. Using automated data quality tools can help continuously validate data against defined expectations.
   
   
5. Create clear ownership & accountability
   Define owners for key domains so you know who's working on data quality issues when they arise. This way, you reduce the number of unexpected errors.

How is data quality monitored?

Data quality management (DQM) - a discipline that ensures data is accurate, reliable, and actionable - integrates advanced tools like data catalogs and metadata management to ensure modern enterprises are better equipped to maintain and elevate their data standards.

Monitoring data quality involves continuously assessing data against defined standards and rules. Advanced data quality tools automate these processes, providing real-time insights and alerts to ensure data remains trustworthy and actionable.

This process typically includes some of the following steps:

Top data quality monitoring platforms in 2025

SAP Data Services

SAP Data Services is a comprehensive data integration and transformation tool designed to improve data quality across the enterprise.

SAP key features:

• Data integration: Integrates data from various sources, including SAP and non-SAP systems
• Data cleansing: Identifies and corrects data errors to ensure consistency and accuracy
• Data profiling: Analyzes data to uncover quality issues and patterns

Soda

Soda is a modern data quality platform that empowers data teams to detect, prevent, and resolve data issues proactively. It combines automated monitoring with collaborative workflows to ensure data reliability.

Soda key features:

• Data quality monitoring: Continuously monitors data pipelines for anomalies and quality issues
• SodaCL: A domain-specific language that allows users to define data quality checks in a human-readable format
• Collaborative data contracts: Facilitate agreements between data producers and consumers on data quality expectations

Bigeye

Bigeye is an enterprise-grade data observability platform that helps data teams ensure their organizations can trust the data they use for analytics, reporting, and machine learning. 

It combines data lineage, anomaly detection, data quality rules, data reconciliation, incident management, and other tools into a single platform, creating complete visibility into data pipeline performance and quality.

Bigeye key features include:

• Automated monitoring: Automatically monitors data pipelines for freshness, volume, and schema changes
• Incident management: Offers tools for tracking and resolving data quality incidents efficiently
• Integration with BI tools: Integrates with business intelligence platforms to ensure data reliability in reporting

DataGalaxy: Unifying data quality & governance

For high-level data management professionals, investing in comprehensive solutions that combine data quality monitoring with governance, lineage, and collaboration tools is essential for ensuring data integrity and maximizing the value derived from data assets.

DataGalaxy is a robust data & AI governance platform that integrates data quality monitoring with a suite of tools designed to enhance data understanding and collaboration. 

By providing a centralized platform for data cataloging, lineage, and quality assessment, DataGalaxy enables organizations to:

By integrating data quality monitoring into its broader data governance framework, DataGalaxy empowers organizations to manage their data assets more effectively, ensuring that data remains a trusted and valuable resource.

Conclusion

As data continues to drive business innovation and efficiency, maintaining high data quality is paramount. 

Tools like SAP, Soda, and Bigeye offer specialized capabilities to address various aspects of data quality management.

However, integrating these capabilities within a unified data governance platform like DataGalaxy provides a more holistic approach to managing data assets.