Dataminr discovers high-impact events and critical breaking information long before it’s in the news. Recognized as one of the world’s leading businesses in AI and Machine Learning innovation, Dataminr has pioneered groundbreaking technology for detecting, classifying, and determining the significance of public information in real-time.
Dataminr specializes in identifying black-swan events before the market reacts. It uses machine learning and cross-references several other data sets ranging from maps to patent data to identify tweets and trends with impact.
The World’s Real-Time Information Discovery Company
Dataminr focuses its efforts within the Corporate Security, Finance, Public Sector, News and PR/Communications sectors. It uses machine learning and AI technology to scour public social media feed, especially Twitter, to identify high-impact events and critical breaking information. It identifies news before it starts to trend on social media channels and then transforms these early signals into real-time alerts that are tied with its clients’ top priorities. Its solutions are used to manage workflows for corporate security, help finance teams learn about market-moving events earlier and discover trading signals, alert first responders, deliver breaking news to newsrooms, and relay company-impacting events and stories for brand management. For instance, in 2015, it alerted the New York Fire Department of a fire in New York City even before they received the first 911 call about the incident.
Dataminr serves hundreds of clients in over 70 countries. Clients include Twitter, NBC, Facebook, NYC Emergency Management, USA Today, CNN, and the Huffington Post.
Funding and Valuation
The company raised $392 million of Series E venture funding from IVP, Valor Equity Partners, The Yard Ventures, Alumni Ventures Group and Cross Creek Advisors on June 29, 2018, putting the pre-money valuation at $1.2 billion. Notable investors include MSD Capital (Michael Dell), Vulcan (Paul Allen), Goldman Sachs, Morgan Stanley and other institutional names.
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