As a thought experiment, try replacing the word data with surveillance, and observe whether common phrases still sound so good. How about this: “In our surveillance-driven organization we collect real-time surveillance streams and store them in our surveillance warehouse. Our surveillance scientists use advanced analytics and surveillance processing in order to derive new insights.”
This thought experiment is unusually polemic for these notes, Designing Surveillance-Intensive Applications, but strong words are needed to emphasize this point. In our attempts to make software “eat the world”, we have built the greatest mass surveillance infrastructure ever seen. We are rapidly approaching a world in which every inhabited space contains at least one internet-connected microphone, in the form of smartphones, smart TVs, voice-controlled assistant devices, baby monitors, and even children’s toys that use cloud-based speech recognition. Many of these devices have a terrible security record.
What is new compared to the past is that digitization has made it easy to collect large amounts of data about people. Surveillance of our location and movements, our social relationships and communications, our purchases and payments, and our health data has become almost unavoidable. A surveillance organization may end up knowing more about a person than that person knows about themselves—for example, identifying illnesses or economic problems before that individual is aware of them.
Even the most totalitarian and repressive regimes of the past could only dream of putting a microphone in every room and forcing every person to constantly carry a device capable of tracking their location and movements. Yet the benefits that we get from digital technology are so great that we now voluntarily accept this state of total surveillance. The difference is just that the data is being collected by corporations to provide us with services, rather than government agencies seeking control.
Not all data collection necessarily qualifies as surveillance, but examining it as such can help us understand our relationship with the data collector. Why are we seemingly happy to accept surveillance by corporations? Perhaps you feel you have nothing to hide—in other words, you are totally in line with existing power structures, you are not a marginalized minority, and you needn’t fear persecution. Not everyone is so fortunate. Or perhaps it’s because the purpose seems benign—it’s not overt coercion and conformance, merely better recommendations and more personalized marketing. However, combined with the discussion of predictive analytics from the last section, that distinction seems less clear.
We are already seeing behavioral data on car driving, tracked by cars without drivers’ consent, affecting their insurance premiums, and health insurance coverage that depends on people wearing a fitness tracking device. When surveillance is used to make decisions that hold sway over important aspects of life, such as insurance coverage or employment, it starts to appear less benign. Data analysis can also reveal surprisingly intrusive things—for example, the movement sensor in a smartwatch or fitness tracker can be used to work out what you are typing (e.g., passwords) with fairly good accuracy. Sensor accuracy and algorithms for analysis are only going to get better.