Telecoms operators and service providers are aware that they are sitting on a potential goldmine of information if only they can hack it free it from the separate silos in which it presently exists and transform it into a horizontal, organisation-wide resource that can be accessed and manipulated at will by many interested parties.

Telcos have been gathering and archiving subscriber data for many decades but, hitherto, have not had the computing and software power at hand to be able to turn it into information pertaining to individual consumers that can be used across the enterprise to increase customer satisfaction, sales and organisational efficiency.

Now, though, that is changing with a major industry focus being placed on Big Data, its uses and potential. However, organisations with the power and ability to use Big data to transcend current norms will need to be carefully controlled and policed to ensure they do not overstep the mark and find themselves crossing the boundary where customer sensibilities and privacy are concerned.

In the UK, the University of Cambridge has just published the results of research conducted into the enormous amount and extent of personal information that, routinely, is being vacuumed-up by computer programs employed by Facebook and its ilk to track subscriber behaviour.

The study, entitled "Private Traits and Attributes Are Predictable from Digital Records of Human Behaviour", was undertaken by the University of Cambridge’s Psychometrics Centre (which worked with other researchers' funded by Microsoft) and based on the Facebook profiles of 58,000 people in the US.

The new research is one of the biggest studies yet undertaken into the contentious subject of forecasting the likely future behavioural traits of individuals as extrapolated from their online preferences and shows how algorithms employed by social networking platforms can deduce, imply or infer the likely future actions of individuals based on information that has not been provided by those individuals in their personal profiles.

According to the evidence adduced in the report, Facebook's algorithms are capable of remarkable precision, proving to be 95 per cent accurate in predicting race and ethnicity, 88 per cent accurate in predicting male sexual orientation, 80 per cent accurate in predicting religious beliefs and political affiliations and 75 per cent accurate (shading down to 62 per cent accurate) in predicting personality types and emotional stability.

What's more, the research claims that such programs and algorithms can unveil deeper undisclosed information such as an individual's recreational drug or drugs of choice and if their parents divorced or separated when the individual was a child of teenager or even later in life.

One of the report's authors, Michal Kosinksi, says that the research team's methods could easily be replicated by for-profit companies to deduce or infer personal attributes about an individual. Even if that individual did not agree to disclose it voluntarily, it could be exhibited involuntarily via observable behavioural tics and traits on the web.

Mr. Kosinski adds, “We used very simple and generic methods. Marketing companies and internet companies could spend much more time and resources, and hence get much higher accuracy than we did.”

You bet they can, will and do. For example, only this month in the UK, the supermarket chain Tesco began to use the records it keeps of its Club Card members' shopping histories to sell targeted online advertising.

Elsewhere, the burgeoning payday loans companies, those usurers who routinely charge vulnerable individuals tens of thousands of per cent annual interest rates on small loans that can quickly become unmanageable big loans, make credit judgments based on hundreds of bits of data gleaned from, among other sources, many and various social networking sites.

This use of data freely provided by individuals who think that what they are providing in one place is completely unrelated to what they provide in another is insidious and potentially dangerous in that it is being applied to pre-categorise people in a way that is already questionable and is becoming downright sinister.

Much of the world's population has been seduced into providing all this information by routinely ticking the ubiquitous "like" box common to so many social networking sites to signal their approval of people, services and products. The more they do this the more of their own personal jigsaw they complete and hand over to to the corporate algorithms that will then go on to infer other and future behaviour.

Michal Kosinski again: ‘We believe that our results, while based on Facebook likes, apply to a wider range of online behaviours. Similar predications would be made from all manner of digital data, with this kind of secondary ‘inference’ made with remarkable accuracy and statistically predicting sensitive information people might not want revealed. Given the variety of digital traces people leave behind, it’s becoming increasingly difficult for individuals to control. I can imagine situations in which the same data and technology is used to predict political views or sexual orientation, posing threats to freedom or even life."

So can others. For example, the executive director of the lobbying organisation Privacy International, Dr. Gus Hosein says says, "This sort of technology that can be used to pre-categorise people threatens all aspects of individual's lives. It’s a nightmare scenario that Facebook are entirely responsible for setting up. It also creates the perfect surveillance state for governments, who will know what people are reading and their exact political persuasion. It is much more invasive than CCTV."

Meanwhile, when asked about the study and its conclusion Facebook simply kept schtum and refused to comment. Well it would, wouldn't it? It's one of its own observable and forecastable behavioural traits whenever it finds itself being criticised.

As Mr. Zimmerman pointed out, "You don't need a weatherman to show which way the wind blows."