The world’s most sophisticated companies overwhelmingly count on data science as a key driver for their long-term success. But according to a new survey of 300 data science executives at companies with more than $1 billion in annual revenue, flawed investments in people, processes, and tools are causing failure to scale data science. These obstacles are evidence that operationalizing data science is hard, and progress requires a level-headed assessment of an organization’s “data science maturity” and associated resource needs for achieving the successful creation, deployment, and maintenance of production models at scale.
The survey revealed five conclusions:
Short-term Investment thwarts growth expectations
The role of data science is unclear
More revenue requires better models
Unimproved models bring higher risk
Must clear the obstacles to achieve goals
In addition to the survey results, this report debuts the Domino Data Lab Maturity Index: an independent assessment of organizational data science health.