best practices and adequate safeguards that allow the use of such data without impinging on individuals ’ privacy. bis central bankers ’ speeches second, we should be aware of the risk of drowning in a sea of data. as nobel laureate herbert simon put it : ‘ a wealth of information creates a poverty of attention ’. the rather obvious characteristic of micro data is their size : micro data by construction are an overwhelming amount of information. it is important to look not only at the benefits of micro but also at the risks for policy makers. the risk is that policy makers are bombarded with too many numbers and, as a result, decision making is delayed. a simple example can elaborate this point. consider one of the key variables monetary policy monitors, m3 growth. the aggregate data on m3 is just one number or one time series, say 12 monthly observations. what are the corresponding micro data? one possible disaggregation of the aggregate is to look at the 6, 000 numbers describing the evolution of specific types of deposits in every single credit institution. what can policy makers do when confronted with, say, 6, 000 numbers? not much. perhaps they will set up a task force to examine the data and come back with a report in a month or two. it is important to note that policy makers do not and should not really use micro data. consider, for example, the micro data collected through the household consumption and finance survey i have mentioned before. are policy makers supposed to dig into these mass of data? of course not. the idea is to channel these data into statistical models that can produce interesting and useful summary statistics and uncover relationships which are important for policy success. policy makers should ask to see a picture of the whole forest, not a list of trees and should transfer to statisticians and economists the task of developing models and summary statistics that will follow developments and provide answers to their policy concerns. in order then not to lose sight of the forest, economists and statisticians must have clever tools that allow the proper manipulation of the micro data. policy makers must demand useful information squeezed out of the micro data so as to gain a better insight on the aggregate data. is m3 growth for real, or just the artifact of regulatory arbitrage? what is driving its growth? is it a widespread phenomenon or a few special outliers? what is behind a surge in credit? a few large corporations or many smes? the point is that statisticians have their work cut

data with new data sets of better quality. micro data can enhance the versatility of aggregate data, as they can be adjusted to accommodate financial innovation, regulatory changes or behavioral reactions to a changing economic environment. let me start by discussing how granular or disaggregated data can promote the flexibility of the data used for aggregate macroeconomic and monetary analysis. the main method for collecting monetary statistics is what we could name the ‘ balance sheet ’ method. statisticians ask policy makers to specify their data needs. once these needs are recorded, statisticians prepare templates that look similar to the balance sheets that banks publish. users have to decide on these dimensions. for example, a template on loans can depict economic sectors on the columns and maturities on the rows. users have to decide on the specific maturity bands they are interested in, enshrine them in regulations on the collection of statistical data and live with this decision for a long period of time. changing the reporting templates is a difficult and costly process not only for central banks but for the industry as well. the eurosystem is thus committed to keeping reporting templates fixed for at least five years. what can we do if we have reasons to believe that the instruments we include in monetary aggregates have to change, if the cut - off maturity band for money is not 24 months but 18 months? with aggregate data the only thing we can do is have patience, wait for the next round of amendments to regulations to take effect, by which time new changes may start becoming necessary. a good bis central bankers ’ speeches example of this occurred in my country in the years after 2000, when for various reasons, depositors moved away from ordinary bank deposits and started using repo agreements to an increasing degree. eventually, this issue was addressed and corrected when repos were rerecognised as deposits, but there still remains an apparent break in the data that complicates the analysis and occasionally leads, even careful economists, to erroneous conclusions. but with granular data, policy needs can be met immediately. let me give some examples, where i expect the move to micro granular data to make a positive contribution to the quality and timeliness of monetary policy analyses and decisions. let me discuss specific areas where micro - data can enrich our analysis and improve the quality of decision making. micro - data can be a way out of goodhart ’ s law. goodhart ’ s law states that as soon as