, organizational, and budgetary challenges . The success of big data projects lies not in implementing a particular piece of technology, but rather in establishing an environment of people and processes that take big data innovations forward and put them to work. Given the diverse skills needed to deal with big data, it also provides an opportunity for organizations to break their internal silos, including between users and producers of data and statistics . From individual applications of big data to its incorporation into the systematic, regular, and large
well as distilling the most important elements for macroeconomic and financial analyses. 15. Although the three features may directly (1 and 2) or indirectly (3) provide valuable data for policy analysis, they are interlinked and as such cannot be completely separated . This paper provides examples but does not intend to give a comprehensive big data project inventory. 4 It rather seeks to illustrate how big data projects can potentially enhance policy analysis. However, moving forward, further exploration and proof of concepts are required to ensure their
part) or summaries thereof to parties outside the IMF other than agencies or instrumentalities of the TA recipient, World Bank staff, other technical assistance providers and donors with legitimate interest, shall require the explicit consent of the TA recipient and the IMF’s Statistics Department. Contents Glossary SUMMARY OF MISSION OUTCOMES AND PRIORITY RECOMMENDATIONS DETAILED TECHNICAL ASSESSMENT AND RECOMMENDATIONS A. Action Plan B. Introduction C. Residential Property Price Index D. The Big Data Project E. The Commercial Property
data to more broadly represent the structure of the market. The BI should continue the work on developing an alternative RPPI using Big Data (listings) from real estate web portals in response to challenges to the current RPPI . The Big Data project offers considerable potential for the compilation of robust asking price indexes for the secondary market, following a hedonic methodology. This is particularly the case given the unavailability of suitable price observation data from administrative sources. The BI should continue to follow the survey based approach for
deployment of big data projects (quality and governance aspects). These portend a better future but also raise fears of further widening the gap between the haves and the have-nots. Perhaps what is missing in this big picture is how the focus of policies can be changed from external values (such as competition, consumption, and profits) to internal values (such as cooperation, compassion, and happiness). The articles in this edition shed light on these issues and how, in the future, economic policies need to evolve to balance tradeoffs and be more supportive and inclusive
policy makers to inform monetary policy and inflation targeting. The use of RPPIs as an input to the national accounts is also an important, although secondary, application. 3. The GSO should continue the current data collection initiatives, the survey of property developers and the big data project on property listings (advertisements) in the short term, before making an informed selection of the most suitable data source within the next 12 months . The assessment underpinning the selection of the most suitable data source will require around 9–12 months of data and
roads to track GDP growth. The Netherlands also uses Facebook and Twitter data to estimate consumer sentiment. Along with Google and Data provider, it published a Big Data study to measure the size of the Dutch digital economy. Eurostat has partnered with European statistical offices to pilot Big Data projects and uses Big Data for macroeconomic nowcasting. Eurostat , in collaboration with the University of Bergamo, used the number of Wikipedia page views to predict tourism flows to three European cities. Central banks and other agencies also use Big Data for