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.

As an academic discipline, data science is bigger than ever, and still growing. Inside Higher Ed recently reported that 303 new accredited data science programs have been founded in the U.S. since 2010, an increase of 52 percent. A 2017 report from the Business-Higher Ed Forum found a huge demand for graduates with data science and analytics skillsets, and recommended that universities make data science courses a requirement for all students.

Getting your first job in data science can be a full-time job all on its own. Simply finding a job post worth applying to can be a chaotic pursuit (though we've tried to make that part easier with our Data Jobs Board). Once you've found a job posting that looks like it could be a fit, you need to make sure you stand out from the crowd of other applicants.