Thriving data cultures prioritize inclusion, collaboration, and transparency over command and control. They work iteratively vs. trying to "boil the ocean." But of course, this is easier said than done, right?
Dec 2022 How to fuel innovation with agile data governance
Why do some data-driven decisions seem to go so disastrously wrong? Ironically, the answer to this question likely isn’t found in the data at all, but rather our subconscious. In a time when companies have never been more data rich, it’s often our inherent information biases that doom critical analytics and data science work.
Is it possible to take the bias out of data work? That’s the question we ponder in this episode featuring Ciaran Dynes, chief product officer at Matillion.
What responsibilities companies and people have to curb information bias
How hypothesis testing and experimentation can improve data work
What’s the most egregious example of information bias in the wild?
Look at the data, but you need People, Context and Relationships to deal with information bias.
Does the data support the hypothesis/conclusions?
Maybe there is tacit collective knowledge.
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