Data has already assumed the status of a capital asset—integral to the operations of all businesses and, in many ways, as important to them as financial and human capital.
Data insights have given companies such as Amazon and Flipkart the tools to upend the rules of the game so that even well-established retail chains have been forced to alter age-old business practices to remain competitive. It is the secret sauce that feeds Uber’s surge pricing and car pool algorithms, and the reason why app-based ride-hailing companies have stolen a march over the traditional taxi industry. Data-driven decision-making will distinguish successful businesses from those that will not survive the decade.
As an increasing number of enterprises begin to recognize data as a capital asset, they have begun to make a concerted effort to capture as much data as possible. Large industrial enterprises such as GE are building the Industrial Internet—introducing sensors into every part of the enormous machines they build so that they can capture as wide a range of data indicators as possible. This will allow them to use data analytics to optimize performance, reduce equipment downtime and offer unprecedented quality of service.
Online advertising engines (and the retail companies that they serve) have begun to deploy advanced browser fingerprinting tools to track user behaviours online so as to be able to target their products more accurately at potential consumers. The wellness industry has started to leverage the proliferation of wearable devices to collect vast amounts of health data from always-on, body-proximate sensors and run analytics that offer solutions in the assisted living and fitness spaces. Almost daily we hear of new data collection businesses that use technology in unique ways to collect data sets that have, so far, been beyond our reach. This includes businesses such as Saildrone—a fleet of driverless sailboats packed with sensors that patrol the oceans collecting data that can be of use to the fishery industry, marine science and even global warming research.
It is not always immediately evident what use we can put these data sets to, but if there is one thing that we have learned it is that when you have the ability to cross-reference large volumes of data against hundreds of other previously unrelated data points, the patterns that begin to emerge will, more likely than not, offer game-changing insights.
Governments have been relatively slow to appreciate the value of data as an asset class. But as more of our modern cities are being retrofitted to become “smart”, local governments are beginning to discover the benefits of data-driven decision-making in municipal governance. They have realized that once the sensors are turned on and vast volumes of data start to inevitably accumulate, tools can be built to identify trends and infer patterns in municipal data in much the same way as we have seen with consumer technology.
At this stage of its development, the benefits of this data-centric approach are not immediately evident, but in time, government decision-making could well evolve from merely using data to make the decision into a form of predictive policing that uses the vast data sets being collected to populate complex statistical models in order to pre-emptively address and solve municipal issues.
The first example of this sort of shift will most likely be in using data to predict where a crime is most likely to occur and who is most likely to commit it. Even though this is almost exactly the plot line of Minority Report, the dystopian science fiction short story by Philip K. Dick, it has already passed into reality in various cities in the US. The police department of Fresno is using a program called Beware to analyse billions of relevant data points so that it can generate “threat scores” about individuals. It then uses these scores to reach out to individuals who are highly likely to commit an offence and warns them, well before they have done anything, that they are under surveillance, hoping to pre-empt the occurrence of the crime.
While this Big Brother approach to governance is unnerving, there is something else about these developments that excites me. If data-driven decisions can improve criminal law enforcement, could the same principles not be applied to other areas of governance as well? Could we not, for instance, use automatic data collection technologies to replace the myriad statutory obligations that large industrial enterprises need to comply with? Instead of requiring businesses to self report, it should be possible to deploy smart sensors and other data collection tools to allow businesses to automatically report compliance. This would do away with the tyranny of inspections and licence renewals and do more for improving India’s ease of business ranking than any legislative amendments.
Rahul Matthan is a partner at Trilegal. Ex Machina is a column on the intersection of technology and law.