Does our understanding of data bias our analytics outcomes?
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 will also cover:
- 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?