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Data Science Leaders Say Companies Focus on Short-Term Payoffs

Almost all say data science is crucial to success but companies lack the staff, skills and tools to be effective longer-term.

Note: TDWI’s editors carefully choose press releases related to the data and analytics industry. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of its contents.

The world’s most sophisticated companies are counting on data science as a key to their long-term success, but flawed investments in people, processes, and tools are leading companies to fail in their best efforts to develop, deploy, monitor, and manage models at scale.

According to a new study commissioned by Domino Data Lab, provider of an enterprise MLOps platform and produced by Wakefield Research, 97 percent of U.S. data executives polled say data science is crucial to maintain profitability and boost the bottom line. However, nearly as many say that flawed approaches to data science strategy, execution and staffing make achieving that goal difficult. 

Expectations outpace investment, with “splashy” short-term investments outnumbering sustained commitments.  For example, although 71 percent of data executives say their company leaders expect revenue growth from their investment in data science, nearly half (48 percent) 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 percent) of respondents said their employers have no trouble pouring money into “splashy” investments that yield only short-term results.  

Companies struggle to scale data science, the report notes. Two-thirds of data executives (68 percent) report it’s at least somewhat difficult to get models into production to impact business decisions, and over a third (37 percent) say it’s very to extremely difficult to do so. Nearly 2 in 5 data executives (39 percent) say a top obstacle to data science having a great impact are the inconsistent standards and processes found throughout their organization.

Skills and Tools

Almost half (48 percent) of data executives say they have inadequate data skills among employees; 44 percent say they are not able to hire enough talent to scale data science.  More than 2 in 5 data executives (42 percent) say their data science resources are too siloed off to build effective models, and nearly as many (41 percent) say they have not been given clear roles.

It’s not just a people issue; 37 percent of data science executives name outdated or inadequate tools to build and manage models as a key factor leading to reduced data science impact on the business. This may explain why a third of data executives say not improving models can result in loss of productivity or rework.

“We found that while executives have enormous expectations for revenue growth from their investments in data science, they are not making investments in the right places to truly unleash the power of data science,” said Nick Elprin, CEO and co-founder at Domino Data Lab. “To properly scale data science, companies need to invest in cohesive, sustainable processes to develop, deploy, monitor, and manage models at scale.”

Growing Risk for Misguided Models

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.  For example, 82 percent of those polled say their company leadership should be concerned that bad or failing models could lead to severe consequences for the company; 44 percent 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 percent) 
  • Faulty internal KPIs for staffing or compensation decisions (45 percent)
  • Security and compensation risks (43 percent)
  • Discrimination or bias in modeling (41 percent)

The Domino Survey was conducted by Wakefield Research among 300 U.S. executives in data science roles with a minimum seniority of senior director at companies with a minimum annual revenue of $1B+ USD and have invested in data science initiatives, between June 16th and June 28th 2021, using an email invitation and an online survey. The full results are available here.

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