The world is changing and so is the way it is measured. For decades, policymakers and the private sector have relied on data released by official statistical institutions to assess the state of the economy.

With an esteemed line-up of international speakers, drawn from across Europe, Asia and the Middle East, the forum will inform and provide insight to all participants during the one-day programme.

Deep learning – an advanced form of artificial intelligence – can be more accurate in predicting outcomes, compared with conventional econometric approaches, according to research from Bank of Korea (BoK). The research paper tested predictions of monthly exports from Korea and daily Korean won-US dollar exchange rates. It found that deep learning approaches produced better results even with the sorts of non-granular data sets that are normally used for conventional econometric models.

The use of machine learning (ML) can produce forecasts that are more accurate than standard statistical methods, research published by the Reserve Bank of India finds. They focus on forecasting CPI inflation and its components.

In March 2018, members of the International Monetary Fund's (IMF's) executive board gave their blessing to a dramatic overhaul of the way the organisation gathers, governs and uses data. The Overarching strategy on data and statistics, the first of its kind, lays out how the fund plans to improve the quality of data, boost the ease with which it can be shared, and start making greater use of innovations in big data and artificial intelligence (AI). Key to the strategy is the "global data commons" – an ambitious, cloud‑based platform for gathering large quantities of data from IMF members. The aim is to bring all of the data together in one place in an readily comparable format, making use of common data standards and methodologies. Researchers, journalists and members of the public should no longer be required to trawl through an array of often-labyrinthine websites belonging to national statistics offices and instead be able to access all of the data through a single portal.

The concept of big data can be hard to pin down – how would you define it? Per Nymand-Andersen: Big data can be defined as a source of information and intelligence resulting from the recording of operations, or from the combination of such records. There are many examples of recorded operations – records of supermarket purchases, robot and sensor information in production processes, satellite sensors, images, as well as behaviour, event and opinion-driven records from search engines, including information from social media and speech recognition tools. The list seems endless, with more and more information becoming public and digital as a result – for example, the use of credit and debit payments, trading and settlement platforms, and housing, health, education and work-related records. Should central banks take advantage of big data?