Within text mining, dictionary techniques, vector space models and semantic  analysis are rapidly gaining traction. 22  All of these methods offer different means of teasing out information, and making robust inferences, in situations where empirical relationships may be complex, non-linear and evolving and where data may be arriving at different frequencies and in different formats. These approaches differ significantly from classical econometric techniques for inference and testing often used in economics and finance. This revolution in data provision, and in techniques to understand it, offers analytical riches. Mining those riches requires, however, considerable care. For example, issues of data privacy loom much larger with granular, in some cases personalised, data. These issues have, rightly, risen in prominence recently. At the same time as putting it to use, safeguarding Big Data is a key preoccupation of the Bank in its research. The Promise of Big Data To the extent Big Data can be characterised, this is usually done using the “three V’s”: volume, velocity and variety. Using the three V as an organising framework, let me discuss some examples of how these data 15  Moore (1965) noted the annual doubling in the number of components per integrated circuit. SINTEF (2013). 17 Ericsson Mobility Report (2017) 18 See https://www.statista.com/chart/10913/number-of-photos-taken-worldwide/ 19 Using dataset in Turrell et al (forthcoming). 20 Dwoskin (2015). The true number of data scientists worldwide is highly uncertain. Many individuals work on data science without necessarily using that job title, but the opposite is also true. 21 Chakraborty and Joseph (2017).

To date, they have focussed on items such as groceries and clothing. Although early days, the potential benefits in terms of increased sample sizes and granularity seem to be considerable. For example, the ONS have so far collected 7,000 price quotes per day for a group of 27  grocery items, which is larger than the current monthly collection for those items in the CPI. For GDP measurement, new sources and methods are also gaining ground. One recent study used satellite imaging to measure the amount of non-natural light emitted from different regions of the world. This has 28  been found to have a statistically significant relationship with economic activity. This approach could  potentially help in tracking activity in regions that are geographically remote, where statistical surveying techniques are poor or where mismeasurement problems are acute. A more down-to-earth example, used by the UK’s ONS and other statistical agencies, is so-called administrative data. This includes data collected by government agencies as part of their activities – for example, on tax revenue and benefit payments. In the UK, some of these data have recently become available for wider use through the government’s Open Data initiative, albeit subject to important checks. 23  Coyle (2014). Fouquet and Broadberry (2015). 25 Cavallo and Rigobon (2016). 26 Cœuré (2017). 27 See https://www.ons.gov.uk/economy/inflationandpriceindices/articles/researchindicesusingwebscrapedpricedata/august2017update 28 Henderson, Storeygard and Weil (2011).