A new study details the potential risks of data science teams lacking the necessary skilled staff, funding, and technical resources to deliver on AI/ML initiatives, and how leaders can close this gap.
According to a new report titled Build A Winning Data Analytics Offense: C-Level Strategies for a Model-Driven Revenue Engine Unveiled.
In a survey of 100 U.S. Chief Data Officers and Chief Data Analytics Officers conducted by Wakefield Research on behalf of Domino Data Lab, 95% said company leaders expected investments in AI and ML applications to pay off in the form of sales growth. A third (33%) expect a double-digit increase in sales.
According to the study, only 19% of CDOs and CDAOs surveyed said they had the resources needed to meet their bosses’ expectations, while 29.4% said there was a “significant shortage” of staff, funding and technology resources they needed to monetize growth using AI and ML.
A shortage of technical skills was identified as a major problem, with 87% of respondents saying their inability to recruit and fill data science roles was hindering their organization’s ability to innovate in the field.
Similarly, 81% of respondents reported that their current tools were unable to fully measure the revenue impact of their AI/ML initiatives, leaving data teams blind to their applications.
Why CDOs and CDAOs want more purchasing power
Budgets – and more specifically those responsible for budgets – were identified as one of the biggest bottlenecks for CDOs and CDAOs.
Nearly two-thirds (64%) of respondents reported that their company’s IT department controlled the majority of data platform spending decisions, with data and analytics teams having a say in only about 56% of purchases.
CDOs and CDAOs alluded to competing priorities between data and analytics teams and the IT department when it came to technology spending: 99% said it was difficult to convince IT to focus budgets on data science, ML and AI initiatives as opposed to to traditional IT areas such as security, interoperability and governance.
Data leaders suggested that the lack of procurement control had an effect on staffing and hiring, with 99% of CDOs and CDAOs reporting that being unable to provide data and analytics teams with their tools of choice negatively impacted their ability to to assume. retaining and upskilling technical talent.
Moving from ‘defensive’ to ‘offensive’ applications
CDOs and CDAOs feel even more pressure to relinquish control of their organizations’ AI/ML initiatives as business leaders look to make more innovative uses of their data, the study found.
Two-thirds (67%) of respondents said their strategy was moving from a “defensive” stance focused on data management, governance, compliance and enterprise intelligence modernization to a more “offensive” strategy focused on driving new business value through of innovative AI and ML applications.
As such, 98% of data leaders agreed that the speed at which organizations can develop, operationalize, and improve AI/ML applications “determines who survives and who thrives amid ongoing economic challenges.”
As a result, a further 67% of CDOs and CDAOs felt it was “time to take control of IT” to prevent their organization from falling behind, with Domino Data Lab concluding that IT departments “[do] are not mandated to drive AI/ML innovation.”
The risks of under-armed data teams
In addition to falling behind rivals and missing out on new data-driven revenue streams, poorly equipped data teams face more immediate risks: 46% of CDOs and CDAOs surveyed admitted they lacked the necessary governance tools to prevent data teams from introducing risk into the organization, while 44% believed that failing to properly manage their AI/ML applications could result in revenue loss of $50 million or more.
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“Today’s vast and rapidly evolving regulatory landscape, coupled with the high stakes of many enterprise data science initiatives, means that a lack of reliable AI could cost tens of millions,” the report said.
Kjell Carlsson, head of data science strategy and evangelism at Domino Data Lab, said the findings were “sobering” and cautioned against pressuring data leaders to do more with less.
“Leaders are grappling with the ongoing challenges of hiring and retaining data science talent, leading IT to prioritize investments in AI/ML over traditional priorities such as data governance, and weak capabilities to manage and govern AI/ML models,” said Carlsson. “CDAO and CDO roles are already notorious for their rapid turnover, and this widening gap between expectations and ability to perform does not bode well for their life expectancy.”
How business leaders can close this gap
Carlsson urged business leaders to invest in their organizations’ ability to scale the development and deployment of new AI/ML-based applications in more parts of the business.
Additionally, to attract and retain talent, organizations need to invest in providing data scientists with the “broad range of different tools” they’ve been trained to use, rather than just a handful of proprietary tools dictated by IT.
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“To accelerate time-to-value and impact, they need to invest in MLOps platforms that span the end-to-end ML lifecycle from development to deployment, monitoring and retraining,” said Carlsson. “To achieve this, CDAOs and CDOs need to build alignment and close collaboration with IT. If that is not possible, they have no choice but to implement these platforms themselves.”
The Domino Data Lab survey was conducted by Wakefield Research of 100 chief data officers and chief data analytics officers at U.S. companies with annual revenues exceeding $1 billion between December 5 and December 18, 2022, using an email invitation and an online survey. According to Domino Data Lab, the margin of error for the study was about 9.8%.
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