If so, I think there are high returns to economics and finance making a further intellectual investment in Big Data and accompanying analytical techniques in the period ahead, the path less followed. The Definition of Big Data If Big Data holds promise, it is probably useful to start by defining just what it is. This is not entirely straightforward. Like beauty, what counts as Big Data lies in the eye of the beholder. It is also a fluid concept. For example, it is clear that data no longer means just numbers; it means words too. Indeed, growth in research on semantics has taken off over recent years, including in economics and finance. What is less contentious is that there has been the most extraordinary revolution in the creation, extraction and capture of data, broadly defined, over the course of the past decade or so. This has been, in part, the 12  Bacon (1620) summarises this well: “Those who have handled sciences have been either men of experiment or men of dogmas. The men of experiment are like the ant, they only collect and use; the reasoners resemble spiders, who make cobwebs out of their own substance. But the bee takes a middle course: it gathers its material from the flowers of the garden and of the field, but transforms and digests it by a power of its own.” 13 Carney (2015). 14 Obstfeld and Rogoff (2001) discuss six major puzzles in international macroeconomics, such as the excess volatility of exchange rates relative to fundamentals.
Understanding the joint behaviour of employment and wages remains one of the central issues in modern macro-economics. These dynamics have been complicated recently by changes in the world of work, with automation changing both the nature and structure of work. Recent Bank research has used granular data on advertised job vacancies to shed light on these 42  dynamics. The research analyses around 15 million job vacancies over a ten year period. Instead of  classifying jobs by sector, occupation or region, it uses machine learning techniques on the text describing jobs to classify and cluster vacancies. What results is a more “job description-based” classification scheme for labour demand. This approach provides a different way of classifying and describing how the world of work is evolving – for example, the types of skills required in the face of automation. The classification scheme has also been useful when identifying the relationship between labour demand and wages. Using the job description-based classification helps identify a clearer link between labour demand and offered and agreed wages. 40 41 42  Cielinska et al (2017). Other recent research papers using trade repository data include Abad et al (2016) and Bonollo et al (2016). See, for example, Duffie, Gârleanu and Pedersen (2005) and Lagos, Rocheteau and Weill (2011). Turrell et al (forthcoming). 9  All speeches are available online at www.bankofengland.co.uk/speeches  9  Variety One of the potentially most productive avenues for Big Data research, in the macro and finance sphere, involves using not numbers but words as data.