Twitter deserves praise for giving millions of people access to real-time information from people, companies and other organizations. But the more people you follow, the harder it is to spot the important tweets. Dataminr’s software solves this problem by cruising through hundreds of millions of tweets a day and sending alerts on the most important ones. That way, customers don’t need to spend hours keeping their eyes glued to the screen.
The model, which lets customers plug in specific information they care about in order to get tailored results, has already proven attractive to investors, and on Wednesday the company announced a new $30 million round of venture funding. Institutional Venture Partners and Venrock led the round, and Deep Fork Capital, GSV Capital and Wharton Equity Parnters also contributed. The company has now raised $46.5 million.
When the New York-based company showed its stuff last year, it had already signed up banks and hedge funds as customers. The Securities and Exchange Commission’s approval of using social media outlets for announcing company news has only broadened adoption.
The use case is pretty clear in financial services, where getting reports as soon as they’re available could provide the justification for trades that could make plenty of money, perhaps more than what slower-informed traders might be able to make. Dataminr’s algorithms take into account factors such as a Twitter user’s location, the user’s reputation, the appearance of news from external sources, market volume and market prices. But much of these back-end calculations happen behind the scenes, so customers see just the important content when they receive alerts.
Dataminr beat major news services by 16 minutes last month following a freight-train derailment near Baltimore. The stock of the freight train company fell after media outlets reported on the incident, and that’s just the sort of thing that shows how Dataminr can come in handy.
However, preventing the reporting of false positives is critical, and Dataminr knows that. Twitter can be self-correcting in the sense that people weigh in very quickly, and that activity informs the algorithms Dataminr looks to as it sends alerts to customers, Ted Bailey, the company’s founder and CEO, told me.
In addition to the financial-services area, Dataminr has also picked up a number of government customers. Now the company wants to get its product into more industry verticals.
Dataminr, founded in 2009, is one of a handful that process the contents of the Twitter firehose and sell services to other companies. Others in that category are DataSift, Gnip and Topsy. It’s one thing to have lots of data coming onto servers and another to get actionable insights on it in real time, and this thinking extends to the Twitterverse, where drowning in tweets doesn’t do much good. That’s why tools such as Dataminr should come in handy much more as the Twitter user count keeps climbing.
Feature image courtesy of Shutterstock user muratart.