Anomalo Named One of Most Innovative Artificial Intelligence Startups in the World by CB Insights
PALO ALTO, Calif., May 19, 2022 (GLOBE NEWSWIRE) -- Anomalo, the complete data quality platform company, today announced that it was named to CB Insights’ Annual AI 100 ranking that showcases the 100 most promising private artificial intelligence companies in the world: https://www.cbinsights.com/research/report/artificial-intelligence-top-startups-2022.
"This is the sixth year that CB Insights has recognized the most promising private artificial intelligence companies with the AI 100. This year's cohort spans 13 industries, working on everything from recycling plastic waste to improving hearing aids," said Brian Lee, SVP of CB Insights’ Intelligence Unit. "Last year's AI 100 companies had a remarkable run, raising more than $6 billion, including 20 mega-rounds worth more than $100 million each. We’re excited to watch the companies on this year’s list continue to grow and create products and services that meaningfully impact the world around them.”
Utilizing the CB Insights platform, the research team picked 100 private market vendors from a pool of over 7,000 companies, including applicants and nominees. They were chosen based on factors including R&D activity, proprietary Mosaic scores, market potential, business relationships, investor profile, news sentiment analysis, competitive landscape, team strength and tech novelty. The research team also reviewed thousands of analyst briefings submitted by applicants.
Anomalo addresses the data quality problem by monitoring enterprise data and automatically detecting and root-causing data issues, allowing teams to resolve any hiccups with their data before making decisions, running operations or powering models. Anomalo leverages machine learning to uncover a wide range of data failures with minimal human input. If desired, enterprises can fine-tune Anomalo’s monitoring through the no-code configuration of metrics and validation rules. This is in contrast to legacy approaches to monitoring data quality that require extensive work writing data validation rules or setting limits and thresholds.
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