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Dylan Jones 0
Ideas for justifying your data quality existence

Conference season is hotting up in the UK, and there are no doubt lots of practitioners putting the finishing touches to their data quality presentations. One interesting observation I’ve encountered is a high churn rate amongst data quality professionals, particularly within the leadership community. Their decision to quit is not

Daniel Teachey 0
Treat your data steward like a rock star

Every day of the year, there's a holiday celebrating one thing or another. In fact, you probably didn't know that Oct. 22 WAS CAPS LOCK DAY. Whoops. Or, if you're like me, you completely spaced on Oct. 26. It was Mother-in-Law Day. Boy, we'll be hearing about that for the

Jim Harris 0
Big data and omission neglect

In my previous post, I used the book Mastermind: How to Think Like Sherlock Holmes by Maria Konnikova to explain how additional information can make us overconfident even when it doesn’t add to our knowledge in a significant way. Knowing this can help us determine how much data our decisions need to be driven

Dylan Jones 0
Data quality - one dimension at a time

I was recently asked what I would focus on given limited funds and resources to kickstart a data quality initiative. This is a great question, because I’m sure many readers will find themselves in this position at some point in their career. My answer is to become ruthlessly focused on

Jim Harris 0
Big data and the treadmill of overconfidence

In her book Mastermind: How to Think Like Sherlock Holmes, Maria Konnikova discussed four sets of circumstances that tend to make us overconfident: Familiarity — When we are dealing with familiar tasks, we feel somehow safer, thinking that we don't have the same need for caution as we would when trying something

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