Flawed investments in people, processes, and tools are crushing potential business impact.
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 doing 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 clear conclusions:
- Short-term investment thwarts growth expectations
- The role of data science is unclear
- More revenue requires better models
- Unimproved models bring higher risk
- Teams must clear the obstacles to achieve goals
A new report, Data Science Needs to Grow Up: The 2021 Domino Data Lab Maturity Index, unravels a set of findings that show how and why companies struggle to scale data science, despite their best efforts to do so.
Here’s a preview of the survey’s findings.
Expectations outpace investment, with “splashy” short-term investments outnumbering sustained commitments
While 71% of data executives say their company leadership expects revenue growth from their investment in data science, a shocking 48% say their company has not invested enough to meet those expectations.
Instead, they say organizations seem focused on short-term gains. In fact, more than three-quarters (82%) of those polled said their employers have no trouble pouring money into “splashy” investments that yield only short-term results.
Companies struggle to execute on the best-laid plans to scale data science
More than 2 in 3 data executives (68%) report it’s at least somewhat difficult to get models into production to impact business decisions—and 37% say it’s very to extremely difficult to do so.
Nearly 2 in 5 data executives (39%) say a top obstacle to data science having a great impact is the inconsistent standards and processes found throughout their organization.
Leaders face shortages of skilled, productive employees and the tools they need
48% of data executives complain of inadequate data skills among employees, or not being able to hire enough talent to scale data science in the first place (44%).
More than 2 in 5 data execs say their data science resources are too siloed off to build effective models (42%), and nearly as many (41%) say they have not been given clear roles.
Misguided models present a growing risk
The study also explored what keeps data science leaders up at night. The results deliver a stark warning for companies cutting corners with data science.
A shocking 82% of those polled say their company leadership should be concerned that bad or failing models could lead to severe consequences for the company, and 44% report a quarter or more of their models are never updated. Respondents name several shocking consequences of model mismanagement, including:
- Bad decisions that lose revenue (46%)
- Faulty internal KPIs for staffing or compensation decisions (45%)
- Security and compensation risks (43%)
- Discrimination or bias in modeling (41%)