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Data: The New Black Gold?

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Estimated reading time 5–8 minutes

Last week I attended a seminar organised by The British Screen Advisory Council and Intellect, the technology trade association, and hosted by the law firm SNR Denton. The panellists included Derek Wyatt, internet visionary and former politician, Dr Rob Reid, Science Policy Adviser, Which?, Nick Graham, of SNR Denton, Steve Taylor, creative mentor, Donna Whitehead, Government Affairs Manager, Microsoft, Theo Bertram, UK Policy Manager, Google, David Boyle, Head of Insight, Zeebox, and Louisa Wong, Aegis Media.

Data as oil

The event was chaired by Adam Singer, BSAC chairman, who explored the metaphor of “data as oil”. Like oil, raw data is a valuable commodity, but usually needs processing and refining before it can be used, especially by individual consumers. Like oil, data can leak and spill, and if mishandled can be toxic.

It struck me through the course of the evening, that just like oil, we are in danger of allowing control of data to fall into the hands of a very small number of companies, who could easily form cartels and lock out competition. It became increasingly obvious during the seminar that Google has immense power because of the size of the “data fields” it controls, with Facebook and others trying to stake their claims. All the power Big Data offers – through data mining, analytics, etc. – is dependent on scale. If you don’t have access to data on a huge scale, you cannot get statistically significant results, so you cannot fine tune your algorithms in the way that Google can. The implication is that individual companies will never be able to compete in the Big Data arena, because no matter how much data they gather on their customers, they will only ever have data on a comparatively small number of people.

How much is my data worth?

At a individual level, people seemed to think that “their” data had a value, but could not really see how they could get any benefit from it, other than by trading it for “free” services in an essentially hugely asymmetrical arrangement. The value of “my” data on its own – i.e. what I could sell it for as an individual – is little, but when aggregated, as on Facebook, the whole becomes worth far more than the sum of its parts.

At the same time, the issue of who actually owns data becomes commercially significant. Do I have any rights to data about my shopping habits, for example? There are many facts about ourselves that are simply public, whether we like it or not. If I walk down a public street, anybody can see how tall I am, guess my age, weight, probably work out my gender, social status, where I buy my clothes, even such “personal” details as whether I am confident or nervous. If they then observe that I go into a certain supermarket and purchase several bags of shopping, do I have any right to demand that they “forget” or do not use such observations?

New data, new laws?

It was repeatedly stated that the law as it stands is not keeping up with the implications of technological change. It was suggested that we need to re-think laws about privacy, intellectual property, and personal data.

It occurred to me that we may need laws that deal with malicious use of data, rather than ownership of data. I don’t mind people merely seeing me when I walk down the street, but I don’t want them shouting out observations about me, following me home, or trying to sell me things, as in the “Minority Report” scenario of street signs acting like market hawkers, calling out your name as you walk by.

What sort of a place is the Internet?

Technological change has always provoked psychological and political unease, and some speakers mentioned that younger people are simply adapting to the idea that the online space is a completely open public space. The idea that “on the Internet, no-one knows you are a dog” will be seen as a temporary quirk – a rather quaint notion amongst a few early idealists. Nowadays, not only does everyone know you are a dog, they know which other dogs you hang out with, what your favourite dog food is, and when you last went to the vet.

The focus of the evening seemed to be on how to make marketing more effective, with a few mentions of using Big Data to drive business process efficiencies. A few examples of how Big Data analytics can be used to promote social goods, such as monitoring outbreaks of disease, were also offered.

There were clear differences in attitudes. Some people wanted to keep their data private, and accept in return less personalised marketing. They also seemed to be more willing to pay for ad-free services. Others were far more concerned that data about them should be accurate and they wanted easy ways of correcting their own records. This was not just to ensure factual accuracies, but also because they wanted targeted, personalised advertising and so actively wanted to engage with companies to tell them their preferences and interests. They were quite happy with “Minority Report” style personalisation, provided that it was really good at offering them products they genuinely wanted. They were remarkably intolerant of “mistakes”. The complaint “I bought a book as a present for a friend on Amazon about something I have no interest in, now all it recommends to me are more books on that subject” was common. Off-target recommendations seemed to upset people far more than the thought of companies amassing vast data sets in the first place.

Lifting the lid of the Big Data black box

The issue that I like to raise in these discussions is one that Knowledge Organisation theorists have been concerned about for some time – that we build hidden biases so deeply into our data collection methods, our algorithms, and processes, that our analyses of Big Data only ever give us answers we already knew.

We already know you are more likely to sell luxury cars to people who live in affluent areas, and we already know where those areas are. If all our Big Data analysis does is refine the granularity of this information, it probably won’t gain us that many more sales or improve our lives. If we want Big Data to do more for us, we need to ask better questions – questions that will challenge rather than confirm our existing prejudices and assumptions and promote innovation and creativity, not easy questions that merely consolidate the status quo.