As of September 2026, Latka tracks 7 data quality tools companies. They have combined revenues of $858.4M and employ 4.5K people. They have raised $568.1M and serve 3.2K customers combined.
Every company below sells data quality tools to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
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.
Sorting: Highest -> Lowest
Showing 7 of 7 companies ranked by annual revenue.
Palo Alto, California, United States
Provider of cloud infrastructure technology designed for data protection, search, analytics, archival and copy data management.
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.
Houston, Texas, United States
At Buurst, we're thinking about your data differently, so you can continue to grow and move your business forward. You need to move fast, and so does your data. Our nimble, cost-effective data migration and performance management solutions open new opportunities and capabilities that continually prepare you for success. Get all the tools you need, so day one happens faster, and you can be amazing on day two, month two, and even year two. We make your cloud decisions work for you - and that means providing you data control, data performance, cost-management with storage tiering, and security.
Addison, Texas, United States
VSI is a Storage Analytics on Demand- SaaS that provides a visual health analysis of your complex SAN environment in minutes.
Cambridge, England, United Kingdom
Operator of a technology company intended to develop genomics data compression and acceleration software. The company's compression software addresses challenges caused by growing volumes of genomics data and transparently integrates it with existing storage infrastructure and bioinformatics pipelines, offering clients with a set of software tools that reduce the size and cost of NGS data for storage and transfer.
Latka tracks 7 data quality tools companies with reported revenue. Together they generate $858.4M in annual revenue and employ 4.5K people.
Rubrik is the largest, with $538M in annual revenue, founded in 2014.
The average data quality tools company in this list makes $122.6M a year, across 7 companies with reported revenue. They serve 3.2K customers combined.
Ranked by annual revenue, the leaders are Rubrik, Denodo Technologies Inc, Stae, Alachisoft and Buurst.
The 7 data quality tools companies tracked here have raised $568.1M in disclosed funding between them.
- 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.
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