Imagine a world where machines are building companies and finding trends in data undetectable by humans. There was a recent post on Medium, “How our startup was bought by Google without even trying” that sheds light on this vision. I don’t know if these events actually happened. However, this story caused me to consider the future impact of data and artificial intelligence on decision making. This is not science fiction. We are much closer to software making complex decisions than we may realize.
In the not so distant future, machines could be gathering and analyzing data, spotting trends, making decisions, and eventually buying companies. Depending on your perspective, this can be thrilling or disturbing.
For the most part, data has been contained in silos within companies and rarely utilized to its fullest extent. This is due to the lack of technology needed to properly maintain large databases as well as the high cost of the software used to access the data. Additionally, the cost of storing data has decreased drastically over the last decade with the rise of cloud infrastructure providers such as Amazon Web Services. Now companies are beginning to see data as an asset to decision making instead of a cost and liability.
Venture Capital Quantified
The venture capital industry is just one example of how data is disrupting industries. Companies like Mattermark, AngelList, and CrunchBase are drastically disrupting venture capital. AngelList and CrunchBase are collecting large amounts of data on privately held companies and making it available to the public.
Mattermark takes this data one step further adding in additional signals of growth from across the web. Algorithms then analyze the data in a way that allows venture capitalists to find potentially hidden gems to invest in.
We are just beginning to see how data can be used for decision making. All industries including healthcare, software, retail, hospitality, and security are beginning to support their big data players.
Company Funding Analysis
I have choosen 15 big data companies to analyze. The following data on these 15 companies comes from AngelList, Mattermark, & CrunchBase. The average valuation according to AngelList for big data companies is $4.4 M which is $0.6 M higher than average valuations across sectors on AngelList. This is a solid indicator that big data companies are getting significant traction in the market and this trend will continue into 2015.
Three companies were acquired out of the 15 companies analyzed. GNIP was acquired by Twitter for $134M in 2014. Freshplum was acquired by TellApart for an undisclosed amount in July of 2014. Identified was acquired by Workday for an undisclosed amount in February 2014.
Palantir stands out in the Mattermark Growth & Mindshare scores. The gap between the two scores was more than two fold. Mindshare score conveys public sentiment and quantifies how top of mind the company is in the eyes of the public. This aligns as Palantir deals with highly confidential data from its customers which include the NSA (National Security Agency), CIA (Central Intelligence Agency), and FBI (Federal Bureau of Investigation).
Risks Of Big Data
There’s another side of big data. We are already starting to see events that show us the potentially dark side of massive amounts of data. As companies begin to look beyond their initial core business model, they are finding that by-products can potentially be extremely profitable.
Companies such as Uber, the unicorn ridesharing company are beginning to experience the backlash from customers when the company crosses the line and begins acting as “God”. There have been accusations that Uber is tracking user movement outside of when the user has the application open on their phone. With this location data, Uber reportedly increased serge pricing when areas are densely populated. Uber has also been reported invoking serge pricing during natural disasters.
Whether these accusations are true is not important. What is important is that companies understand and take seriously the moral and civil duties they have with users data. As users, we are giving up our information in hope of a greater benefit…not being subject to our data being misused or sold.
The point where human confidence and artificial intelligence accuracy intersect is when we will see the first true wave of automated decision making.
The idea of computers stepping into our role is not new, however, as they encroach closer, the basic human instinct is to ask, “Am I becoming dispensable?”.
The answer is yes. The culminating factor for us as a population is what side of the data do we want to be on. The one that is pushing back and fighting the inevitable or on the side pushing the envelope of innovation forward.
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