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Top 1 Data Quality Tools Companies With $10M–$100M Revenue (September 2026)

As of September 2026, Latka tracks 1 data quality tools companies with $10M–$100M in annual revenue. They have combined revenues of $23.5M and employ 53 people. They have raised $3M.

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.

What Data Quality Tools Companies do

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
1
Revenue
$23.5M
Funding
$3M
Employees
53

Filters

Sorting: Highest -> Lowest

Filters
Revenue
All<$1M$1M-$5M$5M-$10M$10M-$100M$100M+
Funding (Locked)Growth (Locked)Team-Size (Locked)

Top Data Quality Tools Companies With $10M–$100M Revenue

Showing 1 of 1 companies ranked by annual revenue.

1Stae logo
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
Year founded
2016
Funding
$3M
Team size
53

Frequently asked questions about Data Quality Tools Companies With $10M–$100M Revenue

How many data quality tools companies with $10M–$100M in annual revenue are there?

Latka tracks 1 data quality tools companies with $10M–$100M in annual revenue. Together they generate $23.5M in annual revenue and employ 53 people.

Which data quality tools company with $10M–$100M in annual revenue is the largest?

Stae is the largest, with $23.5M in annual revenue, founded in 2016.

How much revenue does a typical data quality tools company with $10M–$100M in annual revenue make?

The average data quality tools company in this list makes $23.5M a year, across 1 companies with reported revenue.

Who are the leading Data Quality tools vendors with $10M–$100M in annual revenue?

Ranked by annual revenue, the leaders are Stae.

How much funding have data quality tools companies with $10M–$100M in annual revenue raised?

The 1 data quality tools companies with $10M–$100M in annual revenue tracked here have raised $3M in disclosed funding between them.

Related IT Infrastructure Software categories

All IT Infrastructure Software SaaS categoriesEvery category we track inside IT Infrastructure Software, each ranked by revenue.Browse every SaaS industryThe full A-Z of SaaS categories Latka tracks, from Data Quality to everything else.

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.