work together to increase data transparency and understanding, so that we can have greater market confidence and make better decisions. it is elementary, my dear colleagues. 1 john c. williams, monetary policy : a'data dependent'approach, remarks at the new jersey bankers association's economic leadership forum, somerset, new jersey, january 18, 2019. 2 alternative reference rates committee, aarc statement on the last 30 days before u. s. dollar libor panels end, may 31, 2023. 3 john c. williams, 901 days, remarks at securities industry and financial markets association ( sifma ), new york city, july 15, 2019. 4 international organization of securities commissions, statement of compliance with the iosco principles for financial benchmarks, july 28, 2023. 5 federal reserve bank of new york, survey of primary dealers. 6 federal reserve bank of new york, survey of market participants. 7 federal reserve bank of new york, center for microeconomic data, survey of consumer expectations. 8 inter - agency working group for treasury market surveillance, recent disruptions and potential reforms in the u. s. treasury market : a staff progress report, november 8, 2021. 9 u. s. department of the treasury, remarks by under secretary for domestic finance nellie liang at the 2022 treasury market conference, november 16, 2022. 4 / 5 bis - central bankers'speeches 10 john c. williams, measure twice, cut once, remarks at sofr symposium : the final year ( part ii ) - delivered via videoconference, may 11, 2021. 5 / 5 bis - central bankers'speeches

few aspects of understanding what's going on in markets. 1 / 5 bis - central bankers'speeches and policymakers like me aren't the only ones who are data dependent. businesses, households, journalists, and so many others rely on data to support their understanding of the world around them. capital mistake to be clear, having access to an abundance of data is a wonderful problem to have. think about the plethora of data you can pull from a terminal and the endless ways to use it. many of us have spent more hours than we'd like to admit analyzing, plotting, and running regressions on data. and while we assume that most data are " good, " there are, unfortunately, " bad " data, too. the very same terminal that gives you access to numbers that are supported by millions of transactions could also give you access to numbers that have not seen a transaction in quite some time, if ever. and it can be very hard - too hard, in fact - to spot the difference. this is dangerous. i will echo the warning of our detective friend sherlock holmes : " it is a capital mistake to theorize before one has data. " i'll go a step further and say that it is a capital mistake to theorize before one fully understands the origins of the data one has downloaded. libor no more ( but still important ) of course, the prime example of a capital mistake of this nature is libor. which, by the way, officially ended 139 days ago, not that i'm still counting. 2 3 good riddance! ok, i had to slip in one music reference, but that's all. the lesson of libor is that there are times when what we think of as strong financial market data is only a mirage. libor was one of the most widely used benchmarks in the world, underpinning hundreds of trillions of dollars - that's trillions, with a " t " - of financial instruments and contracts. we all know how that ended : libor was inherently fragile and subject to manipulation. it is astonishing to think about the entire global financial system relying on the small set of transactions underlying libor. in fact, during periods of financial market stress, libor was based on no transactions, which is what opened the door to the fraud and bad behavior we now associate with it. libor was a wake - up call. it led to the collective realization that things we think