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Dec 2022 How to fuel innovation with agile data governance

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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? 

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Does our understanding of data bias our analytics outcomes?

About this episode

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.

This episode features
  • 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?

Key takeaways
  • 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.

Mentioned in this episode

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Special Guests

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Ciaran Dynes Matillion