I've just completed a major new Constellation Research report looking at how today's privacy practices cope with Big Data. The report draws together my longstanding research on the counter-intuitive strengths of technology-neutral data protection laws, and melds it with my new Constellation colleagues' vast body of work in data analytics. The synergy is honestly exciting and illuminating.
Big Data promises tremendous benefits for a great many stakeholders but the potential gains are jeopardised by the excesses of a few. Some cavalier online businesses are propelled by a naive assumption that data in the "public domain" is up for grabs, and with that they often cross a line.
For example, there are apps and services now that will try to identify pictures you take of strangers in public, by matching them biometrically against data supersets compiled from social networking sites and other publically accessible databases. Many find such offerings quite creepy but they may be at a loss as to what to do about it, or even how to think through the issues objectively. Yet the very metaphor of data mining holds some of the clues. If, as some say, raw data is like crude oil, just waiting to be mined and exploited by enterprising prospecters, then surely there are limits, akin to mining permits?
Many think the law has not kept pace with technology, and that digital innovators are free to do what they like with any data they can get their hands on. But technologists repeatedly underestimate the strength of conventional data protection laws and regulations. The extraction of PII from raw data may be interpreted under technology neutral privacy principles as an act of Collection and as such is subject to existing statutes. Around the world, Google thus found they are not actually allowed to gather Personal Data that happens to be available in unencrypted Wi-Fi transmission as StreetView cars drive by homes and offices. And Facebook found they are not actually allowed to automatically identify people in photos through face recognition without consent. And Target probably would find, if they tried it outside the USA, that they cannot flag selected female customers as possibly pregnant by analysing their buying habits.
On the other hand, orthodox privacy policies and static user agreements do not cater for the way personal data can be conjured tomorrow from raw data collected today. Traditional privacy regimes require businesses to spell out what personally identifiable information (PII) they collect and why, and to restrict secondary usage. Yet with Big Data, with the best will in the world, a company might not know what data analytics will yield down the track. If mutual benefits for business and customer alike might be uncovered, a freeze-frame privacy arrangement may be counter-productive.
Thus the fit between data analytics and data privacy standards is complex and sometimes surprising. While existing laws are not to be underestimated, we do need something new. As far as I know it was Ray Wang in his Harvard Business Review blog who first called for a fresh privacy compact amongst users and businesses.
The spirit of data privacy is simply framed: organisations that know us should respect the knowledge they have, they should be open about what they know, and they should be restrained in what they do with it. In the Age of Big Data, let's have businesses respect the intelligence they extract from data mining, just as they should respect the knowledge they collect directly through forms and questionnaires.
I like the label "Big Privacy"; it is grandly optimistic, like "Big Data" itself, and at the same time implies a challenge to do better than regular privacy practices.
Ontario Privacy Commissioner Dr Ann Cavoukian writes about Big Privacy, describing it simply as "Privacy By Design writ large". But I think there must be more to it than that. Big Data is quantitatively but also qualitatively different from ordinary data analyis.
To summarise the basic elements of a Big Data compact:
- Respect and Restraint: In the face of Big Data's temptations, remember that privacy is not only about what we do with PII; just as important is what we choose not to do.
- Super transparency: Who knows what lies ahead in Big Data? If data privacy means being open about what PII is collected and why, then advanced privacy means going further, telling people more about the business models and the sorts of results data mining is expected to return.
- Engage customers in a fair deal for PII: Information businesses ought to set out what PII is really worth to them (especially when it is extracted in non-obvious ways from raw data) and offer a fair "price" for it, whether in the form of "free" products and services, or explicit payment.
- Really innovate in privacy: There's a common refrain that "privacy hampers innovation" but often that's an intellectually lazy cover for reserving the right to strip-mine PII. Real innovation lies in business practices which create and leverage PII while honoring privacy principles.
The report: "Big Privacy" Rises to the Challenges of Big Data"