Big data – Page 4 – Data Big and Small

The fuzziness of Big Data

Fascinating as they may be, Big Data are not without posing problems. Size does not eliminate the problem of quality: because of the very way they are collected, Big Data are unstructured and unsystematized, the sampling criteria are fuzzy, and the classical statistical analyses do not apply very well. The more you zoom in (the more detail you have), the more noise you find, so that you need to aggregate data (that is, to reduce a “big” micro-level dataset to a “smaller” macro one) to detect any meaningful tendency. Analyzing Big Data as they are, without any caution, increases the likelihood of finding spurious correlations – a statistician’s nightmare! In short, processing Big Data is problematic: Although we do have sufficient computational capacity today, we still need to refine appropriate analytical techniques to produce reliable results.

In a sense, the enthusiasm for Big Data is diametrically opposed to another highly fashionable trend in socioeconomic research: that of using randomized controlled trials (RCTs), as in medicine, or at least quasi-experiments (often called “natural experiments”), which enable collecting data under controlled conditions and facilitate detection of causal relationships much more clearly and precisely than in traditional, non-experimental social research. These data have a lot more structure and scientific rigor than old-fashioned surveys – just the opposite of Big Data!

This is just anecdotal evidence, but do a quick Google search for images on RCTs vs. Big Data. Here are the first two examples I came across: on the left are RCTs (from a dentistry course), on the right are Big Data (from a business consultancy website). The former conveys order, structure and control, the latter a sense of being somewhat lost, or of not knowing where all this is heading… Look for other images, I’m sure the great majority won’t be that different from these two.

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Big Data redefine what “markets” are

The growth of “big data” changes the very essence of modern markets in an important sense. Big data are nothing but the digital traces of a growing number of people’s daily transactions, activities and movements, which are automatically recorded by digital devices and end up in huge amounts in the hands of companies and governments. Payments by debit and credit cards record timing, place, amount, and identity of payer and payee; supermarket loyalty cards report purchases by type, quantity, price, date; frequent traveler programs and public transport cards log users’ locations and movements; and CCTV cameras in retail centers, buses and urban streets capture details from clothing and gestures to facial expressions.

This means that all our market transactions – purchases and sales – are identifiable, and our card providers know a great deal about our economic actions. Our consumption habits (and income and tastes) may seem more opaque to scrutiny but at least to some extent, can be inferred from our locations, movements, and detail of expenses. If I buy some beer, maybe my supermarket cannot tell much about my drinking; but if I never buy any alcohol, it will have strong reasons to conclude that I am unlikely to get drunk. As data crunching techniques progress (admittedly, they are still in their infancy now), my supermarket will get better and better at gauging my habits, practices and preferences.

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Big data: Quantity or quality?

The very designation of “Big” Data suggests that size of datasets is the dividing line, distinguishing them from “Small” Data (the surveys and questionnaires traditionally used in social science and statistics). But is that all – or are there other, and perhaps more profound, differences?

Let’s start from a well-accepted, size-based definition. In its influential 2011 report, McKinsey Global Institute depicts Big Data as:

“datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze”.

Similarly, O’Reilly Media (2012) defines it as:

“data that exceeds the processing capacity of conventional database systems”.

The literature goes on discussing how to quantify this size, typically measured in terms of bytes. McKinsey estimates that:

“big data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes)”

This is not set in stone, though, depending on both technological advances over time and specific industry characteristics.

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