By Bartolome Martin

Fashion is about innovation and flair. But to a great extent it is also about the ability to anticipate customer tastes and needs.

Indeed, talent, inspiration and ingenuity may be not enough to succeed in the ultracompetitive fashion environment.  Delivering the right products, at the right price and at the right time, can be key for success.  However, adopting the right strategy is not easy if you lack reliable information upon which to make commercial decisions.

It is in this context that Big Data is starting to play a central role in the fashion industry.  In fact, a number of specialized firms already offer fashion-oriented Big Data services.  But what is Big Data, and what are the legal and ethical challenges arising from it?

Big Data is a business intelligence sub-concept that stands for the processing of an immense amount of information (presumed to be reliable), from very heterogeneous sources, at high speed, in order to obtain some value.  This definition is based upon the so-called 5 Vs: Volume (data size generated every second), variety (various sources of the data), veracity (uncertainty on the significance of the data), velocity (speed of change of the data) and value.  Complexity is also part of the equation (these are very complex processes).

The goal is to analyse dissociated data from a massive amount of people (obtained from many different sources such as social networks, business apps, public bodies’ websites or search engines) in order to compare trends and find patterns, and to put this information in connection with the internal information produced by the company, for such outcomes as predictive analyses, behavioural analyses or profiling.

These techniques have their critics, mostly from the science domain, where absolute accuracy is critical, however the truth is that Big Data is becoming a popular tool in many business sectors, including fashion.

From a legal standpoint, Big Data, as far as the data to be analysed is anonymised, should not pose privacy or intimacy issues.  Other questions are relevant: does this data have an intrinsic value?  Do users deserve compensation for the use of their information (once anonymised) for these purposes? Is there an unjust enrichment arising from this?  Are users actually aware of the use of their information for these purposes?  Similarly, from an ethical point of view, is it ethical to use these techniques to influence consumers?  What if they were voters, for instance?  A legislative answer to these and other questions may perhaps condition the growth and borders of Big Data services. However, until then, Big Data services seem to be much in demand – a resource fashion firms are no longer blind to.