Data Wildcatters: The Oligarchs of the Data Economy (Abstract)
Welcome to the age of the Data Wildcatters. Where companies set out to tap the most lucrative data around the world. Companies like Facebook, LinkedIn, Google, Amazon, IBM and GE just to name a few are placing dragnets around the globe to scrape every piece of useful data from private citizens, employees, public and private companies in real time. They use this data to develop new products and services, offer us things we might be interested in, make technological advances in Artificial Intelligence, Deep Learning and many other things that are indeed useful to everyone. But most importantly they’re using this data to make fortunes and stockpile massive amount of wealth (data is money) that they are essentially stealing from consumers who don’t understand the relationship they have entered into with many of these companies.
The largest technology companies have amassed huge data libraries from the data we’ve generated for them to use and gain proprietary critical insights into our public and private lives. They have a macro and micro view of the world and they have such a huge advantage over the small and medium sized companies. We should all take notice. While they do release data set for the public to use, they only release the data that is relatively bland in comparison to the data they use for their own in-house projects. The best and most useful data is kept in-house for only their eyes and projects only. From a security standpoint this might be beneficial, but from an innovations stand point it has limited the real innovations happening outside the big tech companies ecosystems. This is detrimental to the future of innovation in the 4th Industrial Revolution because it keeps the data in the hands of a few very powerful players who don't like sharing.
This paper will focus on:
· The standardization of data collection
· The standardization of the data refinement process
· Data rights for private citizens
· Usage rights for companies
· How data sets should be used
This paper will be released in late December 2017. If you would like to receive a copy when it is published please enter your information below.