Top 118 Statistical Analysis Software SaaS Companies in May 2026
As of May 2026, there are 118 SaaS companies in Statistical Analysis Software. They have combined revenues of $1.3B and employ 7K people. They have raised $431M and serve 20.1M customers combined.
Statistical Analysis Software refers to tools designed to analyze, interpret, and visualize datasets. These software applications provide a collection of statistical algorithms and functionalities that help users discover patterns and insights within the data they are working with. The primary use cases include descriptive statistics, inferential statistics, and predictive modeling, catering to fields such as research, finance, healthcare, and marketing.
Typical features of statistical analysis software include data manipulation capabilities, graphical representations of data, advanced computational statistics, and reporting tools. Workflows often involve data importation, transformation, analysis through various statistical techniques, and exporting results in accessible formats. Common buyers of these solutions range from data scientists and researchers to business analysts and operational teams within organizations, all seeking to make data-informed decisions based on rigorous statistical insights.
Qureight is a clinical data curation and analytics company that accelerates drug development through AI-powered data curation. The company works with biopharma, contract research organizations, and healthcare institutions.
Developer of financial software designed to help auditors, accountants and other finance professionals to perform data analysis, improve audits and identify control breakdowns. The company's software automates the financial reporting process and creates a streamlined approach for managing and conducting audits, reviews and compilations and also perform detailed analysis and fraud detection tests and support overall practice management, enabling accounting firms, governments and corporations manage audit and financial reporting processes more efficiently and with less risk.
KPM Analytics is a global leader in scientific instrumentation focused primarily on analyzing critical quality parameters in the food, agricultural, industrial, environmental, and clinical industries.
Founded by Stephen Wolfram in 1987, Wolfram Research is one of the world's most respected computer, web, and cloud software companies—as well as a powerhouse of scientific and technical innovation. As pioneers in computation and computational knowledge, we have pursued a long-term vision to develop the science, technology, and tools to make computation an ever-more-potent force in today's and tomorrow's world.
Over the course of more than a quarter of a century, we have progressively built an unprecedented base of technology that now makes possible our broad portfolio of innovative products. At the center is the revolutionary Wolfram Language, which defines a unique convergence of computation and knowledge.
Cognida is an AI-first technology consulting and software engineering company focused on product engineering, data analytics, data science, and machine learning. They deliver practical AI solutions to drive digital transformation across various industries.
Developer of an open source and enterprise ready professional software designed to offer statistical computing services. The company's software makes it easy for data scientists to perform their analysis and share them as interactive web applications, enabling clients to scale and share work and make efficient analysis.
Analytics India Magazine is an online platform chronicling advances specializing in analytics, big data, artificial intelligence, and data science. Since its founding in 2012, it has been dedicated to passionately promoting the analytics ecosystem in India.
Alfasoft produces software for fields such as mathematics, statistics, laboratories, teaching and databases.
Revenue
$23.1M
Customers
-
Year founded
1989
Funding
$100K
Team size
71
Growth
380.57%
Inclusion Criteria
- Must provide tools for both descriptive and inferential statistical analysis
- Should include data visualization capabilities to represent findings effectively
- Must support data import and export in various formats
- Should offer functionalities for advanced statistical modeling and analytics
- Not merely data collection tools; must also enable comprehensive data analysis
- Should allow for user customization and scripting to enhance analysis flexibility
- Must cater to a wide range of industries, supporting diverse statistical applications
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