Jim Harris (@ocdqblog) asks, Are your business decisions affected by the Decision Wobegon Effect?
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What's the best strategy to make change happen when it comes to data quality? Jim Harris (@ocdqblog) presents his theory.
Are your data quality metrics making the important measurable instead of making the measurable important?
Jim Harris (@ocdqblog) on bursting your filter bubble.
Jim Harris (@ocdqblog) explains why you should channel your inner David Lee Roth and include a No Brown M&M's Clause.
Last week, Phil Simon blogged about being wary of snake oil salesman who claim to be data scientists. In this post, I want to explore a related concept, namely being wary of thinking that you are performing data science by mimicking what data scientists do.
In Nudge: Improving Decisions About Health, Wealth, and Happiness, Richard Thaler and Cass Sunstein recounted the story of the campaign to reduce littering on Texas highways called Don’t Mess with Texas. Prior to launching it, Texas officials were enormously frustrated by the failure of their previous, well-funded, and highly publicized advertising campaigns,
In the 19th century, the harnessing of electricity brought about the means to transmit signals via electrical telegraph. The term STOP was used in telegrams to mark the end of a sentence because punctuation cost extra. Therefore, a telegram requesting an end to poor data quality would literally have been sent as “Stop Poor
Imagine a political debate between two candidates where one candidate answers every question quickly, beaming with confidence, and the other candidate answers every question slowly, and with less assertiveness in their response.
In my previous post, with help from Alex Bellos, I explained that measuring is intrinsically fuzzy. A comment by Dave Chamberlain raised the point that there are times when a measurement is absolute on an individual datum by datum basis or, as I prefer to phrase it, accurate relative to the time