At Mode, we believe everyone is analytical. Any time you look for patterns in previous experiences, or make a tradeoff between one option and another, you're analyzing. Improving these skills can only be good for your career; the demand for analytical talent in the workforce is at an all-time high.

What makes an analytics team great? To find out, we interviewed the leaders of three. Our discussion ranged from team structure, to mission-driven motivation, to measurement of success. Their responses can help other folks steering analytics ships of their own form high-level philosophies to keep their teams on track.

Between the thousands of data scientists, analysts and others who use Mode every day, and the hundreds of companies that run their analytical operations on Mode, we hear from both sides of the data talent market. From these conversations, we've noticed some common reasons that companies struggle to hire data talent:

Jasmine Tsai got her first taste of data engineering through a common gateway: she was tasked with a data project as a software engineer. Tsai was part of a team that rewrote Change.org’s non-profit subscriptions system from scratch, to turn it into a stream processing system. This project introduced her to working with data problems.

When Jake Klamka founded Insight Data Science in 2011, the term “data science” had a much different understanding in the private sector than it does today.