Thaler’s Apples and Data Quality Oranges

In the opening chapter of his book Carrots and Sticks, Ian Ayres recounts the story of Thaler’s Apples:

“The behavioral revolution in economics began in 1981 when Richard Thaler published a seven-page letter in a somewhat obscure economics journal, which posed a pretty simple choice about apples.

Which would you prefer:

(A) One apple in one year, or

(B) Two apples in one year plus one day?

This is a strange hypothetical—why would you have to wait a year to receive an apple?  But choosing is not very difficult; most people would choose to wait an extra day to double the size of their gift.

Thaler went on, however, to pose a second apple choice.

Which would you prefer:

(C) One apple today, or

(D) Two apples tomorrow?

What’s interesting is that many people give a different, seemingly inconsistent answer to this second question.  Many of the same people who are patient when asked to consider this choice a year in advance turn around and become impatient when the choice has immediate consequences—they prefer C over D.

What was revolutionary about his apple example is that it illustrated the plausibility of what behavioral economists call ‘time-inconsistent’ preferences.  Richard was centrally interested in the people who chose both B and C.  These people, who preferred two apples in the future but one apple today, flipped their preferences as the delivery date got closer.”

What does this have to do with data quality?  Give me a moment to finish eating my second apple, and then I will explain . . .


Data Quality Oranges

Let’s imagine that an orange represents a unit of measurement for data quality, somewhat analogous to data accuracy, such that the more data quality oranges you have, the better the quality of data is for your needs—let’s say for making a business decision.

Which would you prefer:

(A) One data quality orange in one month, or

(B) Two data quality oranges in one month plus one day?

(Please Note: Due to the strange uncertainties of fruit-based mathematics, two data quality oranges do not necessarily equate to a doubling of data accuracy, but two data quality oranges are certainly an improvement over one data quality orange).

Now, of course, on those rare occasions when you can afford to wait a month or so before making a critical business decision, most people would choose to wait an extra day in order to improve their data quality before making their data-driven decision.

However, let’s imagine you are feeling squeezed by a more pressing business decision—now which would you prefer:

(C) One data quality orange today, or

(D) Two data quality oranges tomorrow?

In my experience with data quality and business intelligence, most people prefer B over A—and C over D.

This “time-inconsistent” data quality preference within business intelligence reflects the reality that with the speed at which things change these days, more real-time business decisions are required—perhaps making speed more important than quality.

In a recent Data Knights Tweet Jam, Mark Lorion pondered speed versus quality within business intelligence, asking: “Is it better to be perfect in 30 days or 70% today?  Good enough may often be good enough.”

To which Henrik Liliendahl Sørensen responded with the perfectly pithy wisdom: “Good, Fast, Decision—Pick any two.”

However, Steve Dine cautioned that speed versus quality is decision dependent: “70% is good when deciding how many pencils to order, but maybe not for a one billion dollar acquisition.”

Mark’s follow-up captured the speed versus quality tradeoff succinctly with “Good Now versus Great Later.”  And Henrik added the excellent cautionary note: “Good decision now, great decision too late—especially if data quality is not a mature discipline.”


What Say You?

How many data quality oranges do you think it takes?  Or for those who prefer a less fruitful phrasing, where do you stand on the speed versus quality debate?  How good does data quality have to be in order to make a good data-driven business decision?


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