Miracle Food for Thought

We all know the headlines in the media about food and drink and your health. One day something is healthy, the next day it will kill you. You are struck with horror when you learn that even a single drop of alcohol will harm your body until you are relieved by the wise words saying that a glass (or two) of red wine a day keeps the doctor away.

These misleading, exaggerated and contradictory headlines are now documented in a report called Miracle Food, Myth and the Media.

It’s the same with data quality, isn’t it?

Sometimes some data are fit for purpose. At another time at another place the very same data are rubbish.

As said as an excerpt from the Miracle Food report:

“The facts about the latest dietary discoveries are rarely as simple as the headlines imply. Accurately testing how any one element of our diet may affect our health is fiendishly difficult. And this means scientists’ conclusions, and media reports of them, should routinely be taken with a pinch of salt.”

It’s about the same with data quality, isn’t it?

Accurately testing how any one element of our data may affect our business is fiendishly difficult. So predictions of return of investment (ROI) from data quality improvement are unfortunately routinely taken with a big spoon of salt.

Bon appétit.

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2 thoughts on “Miracle Food for Thought

  1. Jaime Fitzgerald 18th February 2011 / 03:26

    Dear Henrik,

    Your post builds on one of my favorite concepts: “fitness for purpose.”

    Unfortunately a lot of business planning, even about technical subjects, falls prey to the linear thinking you are describing: “if a grain of salt helps, a spoonful of salt is even better…”

    As you point out however, it’s risky to be too simplistic in our interpretation of technical information, whether it be in scientific research,in data asset management, in data analytics, or in technology strategy.

    • Henrik Liliendahl Sørensen 18th February 2011 / 07:38

      Thanks Jaime. Indeed many of the same truisms and approaches are applied to data quality and a lot of other things.

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