Data and the Liar’s Paradox

Data and the Liar’s Paradox

Apr 04, 2012 by in Data Quality, Master Data Management, Metadata

“This statement is a lie.”

That is an example of what is known in philosophy and logic as the Liar’s Paradox because if “this statement is a lie” is true, then the statement is false, which would in turn mean that it’s actually true, but this would mean that it’s false, and so on in an infinite, and paradoxical, loop of simultaneous truth and falsehood.

I have never been a fan of the data management concept known as the Single Version of the Truth, and I often quote Bob Kotch, via Tom Redman’s excellent book, Data Driven: “For all important data, there are too many uses, too many viewpoints, and too much nuance for a single version to have any hope of success.  This does not imply malfeasance on anyone’s part; it is simply a fact of life.  Getting everyone to work from a Single Version of the Truth may be a noble goal, but it is better to call this the One Lie Strategy than anything resembling truth.”

I think “this is the Single Version of the Truth” qualifies as another example of the Liar’s Paradox because the single version of the truth is true from a single point of view, which simultaneously makes it false from all other viewpoints, but its truth is often a convenient lie that is necessary due to the crisis and the continuum inherent in the complex relationship among metadata, data, and information.

So, perhaps your organization’s answers to questions such as “how many customers do we have?” or “how much revenue did we generate this quarter?” are truths that are also lies that are also truths that are also lies, and so on in an infinite, and paradoxical, loop of simultaneous truth and falsehood, since the answers depend on your point of view regarding the definitions of customer and revenue.

As Obi-Wan Kenobi taught you a long time ago: “You’re going to find that many of the truths we cling to depend greatly on our own point of view.”

Read this related Tom Redman blog
post: I Don’t Trust Your Data

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