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Top 7 Data Quality Tools SaaS Companies in May 2026

As of May 2026, there are 7 SaaS companies in Data Quality Tools. They have combined revenues of $858.4M and employ 4.5K people. They have raised $568.1M and serve 3.2K customers combined.

Data Quality Tools are specialized software solutions designed to help organizations assess, improve, and maintain the integrity of their data. These tools enable users to identify data quality issues such as inaccuracies, inconsistencies, or incompleteness, which are crucial for reliable decision-making. Common use cases include data cleansing, validation, monitoring, and profiling, ensuring that organizations operate with accurate and trustworthy data. Typical features of Data Quality Tools encompass data profiling, which analyzes data's structure and content; data cleansing functionalities that standardize and correct data; and deduplication services that eliminate duplicate entries. Buyers of these solutions commonly include IT departments, data management professionals, and data analysts who are responsible for ensuring that accurate data is available across various business functions. As reliance on data increases across industries, the need for robust data quality solutions has become more critical than ever.

Companies
7
Revenue
$858.4M
Funding
$568.1M
Employees
4.5K

Filters

Sorting: Highest -> Lowest

Filters

Top Data Quality Tools Companies

Showing 10 of 1 companies ranked by annual revenue.

1
Stae

Detroit, Michigan, United States

Provider of a digital infrastructure platform designed to offer open access to the world's civic data in one place, in real-time and with a uniform API. The company's digital infrastructure platform offers universal tool for data management and collaboration, integrate data from any source, map different data types and create real-time data feeds, enabling city managers to city workers to make better data-driven decisions, drive performance and manage vendors.

Revenue
$23.5M
Customers
-
Year founded
2016
Funding
$3M
Team size
53
Growth
14.59%

Inclusion Criteria

- These tools must provide data profiling capabilities to analyze data quality. - They should include data cleansing features to correct inaccuracies and inconsistencies. - Deduplication functions are essential to remove duplicate entries from datasets. - Data Quality Tools must support ongoing monitoring of data quality over time. - They should facilitate data validation against established standards or rules. - Not just data storage solutions; must also offer active data quality improvement functionalities.