Key Quotes
Something our industry has to do a better job of is emphasizing the need for thoughtful criticism of our approaches and methods when we apply modeling to different business problems. I think the things that we hear now about many social media platforms and how they apply analytics, and is causing significant concern. I believe in data and analytics we need be challenging each other when we're thinking of these things, because I've had a lot of experience with very astute data scientists who can build wonderful models and who really understand technology and they're super gifted with mathematics. They can achieve a really great outcome, but sometimes the consequence of that outcome is not what we ever had intended, and we didn't take the time to take a step back and say, huh, what? What could happen here if, you know this really was successful beyond, the variable I'm trying to influence.
The skills and things that would make someone successful and durably successful throughout their career are really in: How do you work with others? How do you influence change? How do you help people be successful to use data? I do think data and analytics are still a huge challenge for people because they’re not intuitive to those in the workforce right now. If you can have solid communication skills and a willingness to put yourself in someone else's shoes and try and help them figure out how data can help their daily to day lives at work and so on. That's a huge skill. I mean, that's super powerful. It has a compounding effect. So I think if you focus on soft skills like that are really centered around driving change, that will put you in good stead.
Key Takeaways
As more becomes possible with data and analytics, the need for thoughtful criticism increases as well. Data teams need to carefully consider the potential unintended consequences of new data projects and models.
Being a strong communicator as a data scientist is a timeless skill, as there will always be a need for great communication regardless of how technology and processes evolve over time.
One of the biggest challenges in the insurance industry is figuring out how to unlock data trapped in old technology and processes so that it works within existing systems and can be utilized by relevant parties.
