Nick Beim

Thoughts on the Economics of Innovation

Dataminr and the Science of Real-Time Information Discovery

Today Dataminr announced a $130m round of financing from a group of leading financial institutions and prominent financial thought leaders including John Mack, Vikram Pandit, Tom Glocer and Noam Gottesman.  

A number of friends have asked me about the company and what I find most interesting about it. This seemed like a good opportunity to highlight a few thoughts. 

What I find most interesting about Dataminr is that in addition to building a business, it is pioneering a new science. The science is real-time information discovery, and it involves sifting through the ever-growing tidal wave of real-time public data to identify and determine the significance of breaking events by their nascent digital signatures, as they happen. Sometimes these events are well-wrapped, for example by someone witnessing an event and tweeting about it, with others providing corroboration. Sometimes they aren’t, with algorithms figuring out what is happening by seeing thousands of facets of something larger. The company has a deep strategic partnership with Twitter that makes this kind of discovery possible. 

This new science is, without a doubt, very cool. It enables one to discover news before it’s news and market-moving information before markets move. It provides a kind of X-ray vision into what is going on in the world in real-time with a filter for what is significant, and to whom. All on the basis of publicly available data.

In a period of five months, Dataminr has become the real-time wire service used almost universally by major news organizations, beating out the next best service by over an hour and discovering troves of unknown unknowns that would never have otherwise come to light. It has become adopted by the lion’s share of leading financial institutions to have access to the frontier of breaking information in real time.  

What’s also interesting is how Dataminr will change the world. In my view most industries that rely on real-time information — an ever-increasing number — will be influenced by it, and some will be transformed by it. The wave of change began in the fields of finance, news and public safety, and I think will move quickly to risk management, security and PR. And undoubtedly to other verticals in ways that are difficult to predict. I am particularly excited about what the company and its technology can do to help save lives in the fields of public safety and humanitarian assistance.  

Dataminr is in the early days of a long journey, but it is already impacting the world in significant ways, and it’s exciting to be a part of.


Are Venture Capitalists Biased Against Female Entrepreneurs?

In her article Taking a Hammer to the Silicon Ceiling, Amanda Bennett hits on a real problem in the venture industry where spoken and unspoken biases have a significant impact: it is harder for women to raise money than it is for men. However hopeful one’s outlook, this is an uncomfortable and inescapable truth that the industry should acknowledge.

What’s the reason for it? I’ve been in the venture business for 14 years, and rarely, but sometimes, I’ve seen it come from unabashed bias about women’s ability to do as good a job as men. Generally this relates to the subject of women already having or potentially having children. I’ve heard people remark: “Wouldn’t that be a big distraction for the company, and how could they possibly be as productive as men in those circumstances?” This particular kind of bias is rarely expressed in a public manner but certainly affects the thinking of some. The good news is that as younger generations of investors assume more prominent roles in the industry, I think it will substantially diminish.

More often, I’ve seen the challenges female entrepreneurs face in raising money result from a bias that is rooted in the primary way venture capitalists make decisions, which is through pattern recognition. In a private conversation, a successful west coast venture capitalist expressed the issue to a friend of mine in a backward-looking empirical fashion that was an attempt to be unbiased: “look at the numbers – most successful startups are started by men in their 20’s and 30’s; the number of successful startups founded by women is much smaller.” Yes, but most startups in any historical timeframe were started by men in their 20’s and 30’s. This doesn’t speak to the likelihood of women succeeding, particularly since a significantly larger number of women are starting companies today than in the past.

Social scientists call this logical flaw selecting on your dependent variable: determining that A is a principal cause of B by looking only at cases of B. Used as the primary lens for evaluating new investment opportunities in venture capital, it creates all sorts of intellectual distortions and inertia and is the principal reason most venture capitalists are late to promising new trends and only jump on board when there is a significant pattern of success. I think this is the cause of the biggest challenge that female entrepreneurs face in raising money. Most venture capitalists have not internalized the success of female entrepreneurs to a sufficient degree to have it influence their intuitive pattern recognition, partly due to what they perceive as a lack of a large enough n and partly no doubt due to the fact that they have not worked with female entrepreneurs directly. It was also the cause of challenges that entrepreneurs faced in raising money in a variety of pioneering new fields, from personal computers to the internet to digital animation. Success by entrepreneurs in these fields was not yet a large enough historical pattern to influence investors’ thinking.

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