Ten Signs of Data Science Maturity

Ten Signs of a Mature Data Science Capability

If you want to build a ship, don’t drum up people to collect wood, and don’t assign them tasks and work, but rather teach them to long for the endless immensity of the sea.

Antoine de Saint-Exupéry

Over the years in working with US government, commercial, and international organizations, we have had the privilege of helping our clients design and build a data science capability to support and drive their missions. These missions have included improving health, defending the nation, improving energy distribution, serving citizens and veterans better, improving pharmaceutical discovery, and more.

Often, our engagements have turned into exercises in transforming how the organization operates—“building a capability” means building a culture to support and make the most of data science. In many cases, this culture change has delivered significant insights into big challenges the world faces—poverty, disease outbreaks, ocean health, and so forth. We have encountered a wide variety of successful organizational structures, skill levels, technologies, and algorithmic patterns.

Based on those experiences, we share here our perspective on how to assess whether the data science capability that you are developing within your own organization is achieving maturity. In no particular order, here are our top ten characteristics of a mature data science capability.

A mature data science organization…

1. …democratizes all data and data access.