Economically connected data can clearly benefit not only private commerce but also national economies and their citizens. For example, the judicial analysis of data can provide the public sector with a whole new world of performance potential. In a recent report, consultancy firm McKinsey suggested that if US healthcare were to use big data effectively, the sector could create more than $300 billion in value every year, while in the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone.
It is understandable that many citizens around the world regard the collection of personal information with deep suspicion, seeing the data flood as nothing more than a state or commercial intrusion into their privacy. But there is scant evidence that these sorts of concerns are causing a fundamental change in the way data is used and stored.
That said, we must all have a care. As public understanding increases, so will concerns about privacy violation and data ownership. If it is discovered that companies are exploiting data that has been collected without genuine permission and are using it in ways that have no societal benefit, there is a considerable risk of a public backlash that will limit opportunities for everyone. The shelf life of the don’t- know-so-don’t-ask approach to data collection will be short.
Some in the industry believe governments need to intervene to protect privacy. In Britain, for instance, the Information Commissioner’s Office is working to develop new standards to publicly certify an organisation’s compliance with data-protection laws. But critics think such proposals fall short of the mark—especially in light of revelations of America’s National Security Agency (NSA) ran a surveillance programme, PRISM, which collected information directly from the servers of big technology companies such as Microsoft, Google and Facebook.
From a marketing perspective, detailed awareness of customer habits will enable technology to discriminate in subtle ways. Some online retailers already use “predictive pricing” algorithms that charge different prices to customers based on a myriad of factors, such as where they live, or even whether they use a Mac or a PC.
Transport companies provide another interesting use case for connected data. Instead of simply offering peak and off-peak pricing, they can introduce a far more granular, segmented model. Customers can see the cost of catching a train, and the savings that can be made by waiting half an hour for the next one. They can also see the relative real-time costs of alternative transport to the same destination, and perhaps decide to take a bus rather than a train. They have the ability to make informed, value-based judgments on the form of travel that will best suit their requirements. Such dynamic systems will provide greater visibility of loading and so allow the use of variable pricing to nudge passengers into making alternative choices that can improve the efficiency of the overall network. Benefits all round. That said, although there may be innocuous reasons for price discrimination, there are currently few safeguards to ensure that the technology does not perpetuate unfair approaches.
Open access to data is reaping its own rewards. London’s Datastore makes information available on everything from crime statistics to tube delays to, as their website states, “encourage the masses of technical talent that we have in London to transform rows of text and numbers into apps, websites or mobile products which people can actually find useful.” Many are taking up the challenge, and are delivering real social benefits.. A professor at UCL, for example, has mapped how many people enter and exit Tube stations, and how this has changed over time. This information has now been used by Transport for London to improve the system.