Data quality – Data Big and Small

New publications on big data and official statistics

National Statistical Institutes (NSIs) have long been the recognised repositories of all socio-economic information, mandated by governments to collect and analyse data on their behalf. The development of big data is shaking this world. New actors are coming in and commercially-oriented, privately-produced information challenges the monopoly of NSIs. At the same time, NSIs themselves can tap into digital technologies and produce “big” data. More generally, these new sources offer a range of opportunities, challenges and risks to the work of NSIs.

The Statistical Journal of the IAOS, the flagship journal of the International Association for Official Statistics, has published a special section on big data – of particular interest to the extent that it is free of charge!

Fride Eeg-Henriksen and Peter Hackl introduce this special section by defining big data and emphasising its interest for official statistics. But it is crucial, albeit admittedly not easy, to separate the hype around big data from its actual importance.

The other papers are concrete examples of how big data may be integrated into official statistics:

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Big Data and social research

Data are not a new ingredient of socio-economic research. Surveys have served the social sciences for long; some of them like the European Social Survey, are (relatively) large-scale initiatives, with multiple waves of observation in several countries; others are much smaller. Some of the data collected were quantitative, other qualitative, or mixed-methods. Data from official and governmental statistics (censuses, surveys, registers) have also been used a lot in social research, owing to their large coverage and good quality. These data are ever more in demand today.

Now, big data are shaking this world. The digital traces of our activities can be retrieved, saved, coded and processed much faster, much more easily and in much larger amounts than surveys and questionnaires. Big data are primarily a business phenomenon, and the hype is about the potential gains they offer to companies (and allegedly to society as a whole). But, as researcher Emma Uprichard says very rightly in a recent post, big data are essentially social data. They are about people, what they do, how they interact together, how they form part of groups and social circles. A social scientist, she says, must necessarily feel concerned.

It is good, for example, that the British Sociological Association is organizing a one-day event on The Challenge of Big Data. It is a good point that members must engage with it. This challenge goes beyond the traditional qualitative/quantitative divide and the underrepresentation of the latter in British sociology. Big data, and the techniques to handle them, are not statistics, and professional statisticians have trouble with it too. (The figure below is just anecdotal, but clearly suggests how a simple search on the Internet identifies Statistics and Big Data as unconnected sets of actors and ties). The challenge has more to do with the a-theoretical stance that big data seem to involve.

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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: 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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