Etiqueta: Data mining

Big Data, Crisis, Crisis management, Data mining, Privacy, Privacy, Social media, Trust

The Myth of Transparency: Facebook´s ugly face (2/4)

A crisis about transparency (or lack of), we could summarise the Facebook reputation nightmare. Or, as the Times magazine puts it  brilliantly: “All this has prompted sharp criticism of the company, which meticulously tracks its users but failed to keep track of where information about the lives and thinking of those people went.” In this apparent paradox lies the first point I would like to highlight in this 4-part analysis: The Myth of Transparency.

If you read books such as Jeff Jarvis’ Public Parts (2011), you know how social media has successfully created a hype about the virtues of living life under the public sphere, in a continuous Self Big Brother. Although back then Jarvis agreed with some sort of protection to people’s privacy, such as the ones proposed by then European commissioner Viviane Reding, he was defending a libertarian, perhaps utopian, view of transparency that disregarded a basic impulse behind the “publification” of our lives by tech companies: data has economic value and social media thrives on marketing data.

What this crisis has brought to the surface and to the attention of regulators was the culmination of a series of privacy issues and breaches involving Internet and more famously Facebook. It is perhaps the beginning of the “end of the innocence” and the realisation by the users that transparency is good when it happens on both ways: from the part of the producer of the data (i.e. us) and the marketer of the data (social media companies). The market has become more mature and people starts to realise that there has never been a truly “free service” by Google or Facebook. As Viviane Reding poses it: we pay the service with our data.

To be fair, these companies never have said that they didn’t use people’s data for different purposes, including making tons of money. However, what people are noticing now is how obscure and careless firms have been in the management of this data – and how vulnerable they are when their minds can be read by data mining companies such as Cambridge Analytic with the controversial, and at the same time, brilliant experiment conducted by Kosinski et al (2015).

People are also realising how social media creates a subtle form of surveillance, by letting unknown organisations to access their view of the world, their relationships and their tastes. By impacting serious decisions like votes in a general election, for example, or referendums, the public opinion starts realising the risks of manipulation in this cycle of data transparency – data mining – campaign management.

[clickToTweet tweet=»With the Facebook #reputation #crisis people are also realising how social media creates a subtle form of #surveillance, by letting unknown organisations to access their view of the world, relationships and tastes. » quote=»Click to Tweet»]

Big Data, Data mining, Research

Why are mistakes made in decisions?

A recent study by researchers Ashton Anderson of Microsoft Research in New York, Jon Kleinberg of Cornell University in Ithaca, and Sendhil Mullainathan of Harvard University has shed light on the issue of decision-making and its errors. Using chess as a laboratory, and a dataset of 200 million chess games played by amateurs and another dataset with nearly one million games between grandmasters, the authors started from the premise that the error in a decision is related to three factors: the skill of the player, the time available for a play/ decision and the inherent difficulty of the decision.

After exhaustive analysis of the data, the researchers came to the conclusion that the most reliable prediction factor is the inherent degree of difficulty of the decision. The study invites us to discuss how errors occur in other fields. For example, is it the doctor’s experience or the difficulty of the case that can lead to an error in the diagnosis? Or would distracted drivers more prone to accidents than experienced drivers facing roads in dangerous conditions?

We could also ask: if the current environment is more difficult for those who make marketing and communication decisions (and for all leaders, in general) would it be necessary to create new strategic models to less complex the battlefield where the digital competition unfolds and, therefore, avoid mistakes? And what is the role of executive training and capability programs in this sense?

Despite the experience you have, and the time you can take to make your decisions (and nowadays there is not much time …) new technologies create a much more difficult and uncertain scenario than usual. Consequently,  I believe that the amount of errors in times of uncertainty must be high – to the point of taking a business to bankruptcy. This situation may explain the fatalistic approaches of Silicon Valley as the «fail fast, fail often» mantra popularised by Facebook, which for some is nothing more than an irresponsible hype. Or, simply, learn by trial and error. A lot of errors.

There is nothing wrong in accepting risks and all entrepreneur knows that. But, that does not mean looking for failures as a learning strategy. Maybe there are more effective and less painful methods. The use of data mining and predictive models in marketing could surely reduce the complexity of decision making, and perhaps a way to avoid easily identifiable failures. Perhaps, much better would be «be successful quickly, and stay in front,» as this article comments.