The US print edition of the Financial Times carried an interesting article today that highlights the importance of data and data gathering to the value and meaning of analytics and decision making. The article is called, “Japan Back into Growth after Data Revision“. Just a short while ago data released in Japan suggested that it’s Q3 GDP had contracted by 0.8% and thus, technically, Japan was in recession. This was a political blow for the ruling party and specifically Shinzo Abe, prime minister. In fact the data was giving fuel to his political enemies that were calling his long term economic recovery program a failure. If left to itself this data might have triggered all manner of resulting changes that could have harmed the Japanese economy.
GDP numbers are revised all the time so that is not new. But the article calls out that Japan revised it’s Q3 GDP data and the change was staggering. The new figure for GDP in Q3 was a growth of 1.0%. This revision was far in excess of anything expected and suggests, in fact, that the economy is growing quite nicely. The cause of the revision was mainly driven from a misread in the level of investment in the economy. Now the economy watchers, bankers and pundits are lauding over the results, and the policies of Shinzo Abe. Not two days before he was being lambasted.
This story highlights a classic problem with data, data gathering, and how analytics are used to drive decision making. The raw data was not in a state (for whatever reason) that enabled effective data gathering. The analytic (GDP) was itself a roll-up of a series of other analytics, and so it’s inherent biases and weaknesses were amplified. The resulting understanding of the performance of the Japanese economy was unsound. Of course, statistical units have been working for years trying to remove bias and gaps and risks in their work. Hence market watchers are used to revisions, but not of this size and with this impact. Sch large revisions bring into question the confidence of the work to publish the data and their processes. At such crucial times, on such crucial issues, falling confidence in the data, or the process, wont’ help anyone.
One way to cope with fluid confidence levels is to train the market watchers and politicians to change their processes. In other words, instead of reacting to the initial data point, wait for actual decision taking until later, once revisions have ‘settled down’. This is possible, but flies in the face of what we really want – real time, insightful and accurate updates from our processes. So it seems there remains an everlasting trade-off between timeliness and accuracy. So what’s new?
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