Big Data and Macroeconomic Accounts Michael Davies, Division Head, Macroeconomic Statistics Division, Australian Bureau of Statistics September

There is a lot of hype around big data. Many are very excited - it seems to solve many of the problems of getting good data from households and businesses, saving a lot of trouble and expense. 2 Background

Initial reaction from methodologists who have spent their lives ensuring high quality survey and census data is to proceed carefully. Elegant solutions are proposed to bring the data up to the quality of survey and census data. 3 First Reaction: Methodology

The ABS has a lot of experience with administrative data - Customs trade data; tax data for businesses and people; and financial data from the banking regulator. The ABS is doing a lot of work on the combination of Government datasets e.g. health, welfare, employment. Big demand for social micro data 4 ABS History

Now The new territory is transactional data from private businesses e.g. supermarket scanner data. This is at a time when companies are being told that their data are their most valuable asset. 5

In the case of one big respondent - the boss saw benefits and said "make it happen" then lawyers got involved - took forever and we had to accept significant restrictions on what we can do with the data. Can we make them hand over detailed transaction data under existing laws? Open to debate and interpretation of laws intended to cover paper forms collecting a limited amount of aggregate data. 6 ABS Recent Experience

The Way Ahead. Lots of things to address in parallel - legal right to data/ contracts if buying - continuity of supply - interpretation of data - don't get hung up on methodology but give priority to measurement aspects. - consistency over time Haste to save money does not help. Take care stopping traditional data sources 7

The Way Ahead Cont. Need thoughtful and considered approach balanced with meeting expectations and cost reductions. 8

Risks More noise than signal Disrupted supply (depending on commercial arrangements) Bias (data manipulated by supplier) High cost to provider 9

For you may palm upon us new for old: All, as they say, that glitters, is not gold. John Dryden, 1687, The Hind and the Panther. 10