Cassie Kozyrkov of Google Cloud – going beyond data inspiration

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What does it mean to be data-driven? Cassie Kozyrkov, Chief Decision Scientist at Google Cloud, who advises leadership teams on decision process, AI strategy, and building data-driven organizations believes that often organizations are more data inspired, than data-driven. Cassie also works to democratize statistical thinking and machine learning so that everyone – Google, its customers, the world! – can harness the beauty and power of data.

Leading up to her trip to TechChill 2019 in February, we asked Cassie to elaborate on what common issues she encounters around her work with data science and data-driven decision-making.

 

What does it mean to be data-driven? What are some common mistakes you encounter in data-driven organizations?

For a decision to be data-driven, it has to be the data — as opposed to something else entirely — that drive it. Seems so straightforward, and yet it’s so rare in practice because most decision-makers lack a key psychological habit, without it their decisions are at best inspired by data. There’s no math in the world that can fix that.

 

Could you elaborate on the psychological aspects that can lead to data-inspired decision-making?

Turns out many people only use data to feel better about decisions they’ve already made. We find the most convenient light in which to see evidence, and we don’t always know we’re doing it. Psychologists have a lovely name for this: confirmation bias.  The more ways there are to slice the data, the more your analysis is a breeding ground for confirmation bias. The result? Decision-makers end up using data to feel better about doing what they were going to do anyway. The antidote is setting your decision criteria in advance.

 

Turns out many people only use data to feel better about decisions they’ve already made.

 

What are some skills that are needed for data scientists today?

Not only must you have a deep understanding of decision-making and how information should drive actions, but you need a keen nose for the nuances of how to usefully impact your particular business domain and, as if that weren’t enough, you also need to understand the ecosystem of diverse skills that need to come together to make a large-scale data science project successful. And that’s just the minimum for entry into this game.

 

We often hear of the shortage of data scientist in the field, but you have gone to argue that the leadership talent shortage is far worse. Why is that?    

Most data science leaders today are what I like to call “transcended data scientists.” People who pursued formal training in science, engineering, or statistics and then, by some miracle, woke up one day to realize that they were more interested in making data useful than chasing mathematical complexity for its own sake. We can’t expect these data scientists to transcend and instantly know how to be good leaders and decision-makers. Who would have taught them that? You don’t learn it by writing code or proving theorems all day. Part of the solution is changing the fashion so that these skills are a not-negotiable part of being hardcore at something as attractive as raw data science.

 

Join us at TechChill 2019 to learn more from Cassie, get your pass here.

 

Prepared by Greta Babarskaite, Content Manager at TechChill

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