82 percent investing splashy, limited-impact investments

97% say data science “crucial” to success but companies lack the staff, skills and tools needed to be effective longer-term

The world’s most sophisticated companies are overwhelmingly 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.

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According to a new study commissioned by Domino Data Lab, provider of the leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, and produced by Wakefield Research, 97% 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:

• 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 are the inconsistent standards and processes found throughout their organization.

Companies 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.
• Further complicating the issue, 37% of data science executives name outdated or inadequate tools to build and manage models as a key factor leading to reduced data science impact on the business.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 (33%) 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.”

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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:

• 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%)

The Domino Data Lab Maturity Index

Beyond revealing significant barriers to scaling data science, this survey introduces the Domino Data Lab Maturity Index: a framework for assessing an organization’s data science maturity based on years of working closely with top performing companies.

The Maturity Index assessment was given to each survey respondent as an independent measure of organizational processes, analytical agility, and cohesion. Its results were then compared to the survey results and what appeared was strong validation for the Maturity Index as a valuable self-assessment for organizations – a blueprint for achieving success with enterprise data science initiatives.

The Domino Data Lab Maturity Index is outlined in detail within a new report, “Data Science Needs to Grow Up: The 2021 Domino Data Lab Maturity Index.” Top findings of the index include:

• Discipline Delivers Rewards: A powerful 82% of High Maturity companies have had data science make a great deal or fair amount of impact on sales or revenue, including 50% who said a great deal, while just 14% of Low Maturity companies can say the same.
• Maturity Moves Markets: On average, an impressive 69% of data models at High Maturity companies impact business decisions, compared to just 49% at Low Maturity Companies.
• Meaningful Data Science Requires Discipline: Executives at 65% of High Maturity companies say their companies treat data science as a first-class discipline, the same as finance or marketing.

Methodology

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.

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