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3 Tips for Effective Data Science Processes

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Benn Stancil, Co-founder & Chief Analytics Officer

September 19, 2019

1 minute read


The more insights your data science team can uncover, the more informed decisions your business can make. Below are some quick notes on how to accelerate your data science team’s efforts for larger impact.

1.Don't over plan data initiatives

Too much central planning is one of the most common reasons why data initiatives fail. Businesses move quickly, and the questions that data scientists are trying to answer move even faster. Any project plan that counts on the world being the same tomorrow as it is today is going to fail. The best teams are often those that ship quickly, see what works, and iterate.

2.Keep processes agile to accommodate eclectic backgrounds

Analysts and data scientists come from all sorts of backgrounds. There’s no single process that will fit everyone. Some are former bankers and consultants who are most comfortable making presentations to execs in Powerpoint. Some are Ph.D.s who’ve written popular R packages but have little industry experience. Some are previous frontend engineers who got deep into data visualization. Others are still operations specialists who managed enterprise Oracle databases for the last five years. While you should be comfortable asking your team to adjust to a process, you should also be willing to adapt your processes to better fit your team.

3.Explore your data — “waste” time

Like venture investing, some of the best data science projects can be 10-100x more impactful than the everyday wins. Even if they’re unlikely to succeed, data science leaders should make sure their teams have time to investigate these opportunities that have huge upsides. While it’s tempting to demand gradual progress every day, (and often easier to justify to execs), you’ll have a bigger, longer-term impact by uncovering just a handful of big wins.

Best team practices can take a while to fine tune and sometimes require more buy-in to implement them. If you need more data points to build your case, check out our posts tagged “data teams” which expand on how to increase data science teams wins.

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