Nouvelle cuisine meets big data?

After the post Christmas/holidays eating binge and the inevitable New Year's resolution of dieting, it seems like this article that appears in the Visual Business Intelligence blog by Stephen Few is more than a little apt.

An analogy is made between the fast food and slow food movements (hence the 'apt' earlier) and he article presents the argument that taking a much more measured, "slower" approach to data collection is the way forward.

As the slow food movement emphasizes the preparation, cooking, eating and enjoyment of food (as opposed to the idea of fast food), slow data emphasizes the same when collecting, storing, processing and analysing data. Given the rush to collect as much data as possible with often scant regard to the content, identification and value I can see the appeal of this. Regarding this latter point, an article in The Register: Craptastic analysis turns 2.8 zettabytes of Big Data into 2.8 ZB of FAIL - dramatically explains this.

Taking a moment to really think about what we are gathering, why we are gathering and what we intend to do with the data, other than the attempt construct the perfect advertisement, then we will see that we actually need very little data. This is almost an antithesis to the 'capture and collect everything everywhere just in case we might need it later' approach.

And then with that carefully chosen, semantically well-defined data we are able to process and analyse and enjoy the results of our analysis. The enjoyment of data here being that what is understood from the data and that this understand be more relevant and useful to our business and to our customers.

Taking a slow approach to digesting our data has a number of other side-effects, a few being that the amount of data we store is less, the amount of analytical infrastructure will be less and the value to the consumer (and the business) will be significantly greater as we will not have to sift through billions of uninteresting and irrelevant data-points. The effects upon areas such as privacy should be self-explanatory...indeed isn't this the true goal of the privacy advocates?

A slow data approach might just solve many of the issues we see with data: semantics, isolation, privacy, data storage, analytics, just to mention a few.

Indeed as the slow food movement has as its objectives to enjoy food, so slow data might just be the way through which we appreciate the information, knowledge and wisdom in our big data.

Are we in effect embodying the ideals of nouvelle cuisine as applied to data? A rejection of excessive complication, reducing the processing to preserve the natural information content, the freshest and best possible ingredients, smaller data sets, modern processing techniques, innovation and invention as being drivens because of the data collection (and not because they might happen if we collect data) - the analogies between slow food, nouvelle cuisine and slow data are abundant.

Food for thought...almost literally.