According to CDOs, the expectations of business leaders for AI/ML applications are too high

A new survey details the potential risks of data science teams not having the skilled staff, funding and technology resources to implement AI/ML initiatives, and how leaders can close the gap.

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According to a new report, Build A Winning Data Analytics Offense: C-Level Strategies for a Model, data and analytics leaders say they are unable to meet business leaders’ high expectations for AI and machine learning initiatives because there is little their number and equipment. The powered revenue engine has appeared.

In a survey of 100 U.S. data management and data analytics executives conducted by Wakefield Research on behalf of Domino Data Lab, 95% of respondents said that company management expects to see a return on investment in AI and ML applications through increased revenue. A third (33%) expect a double-digit percentage increase in revenue.

According to the study, only 19% of CDOs and CDAOs surveyed said they had the resources to meet their bosses’ expectations, and 29.4% said there was a “meaningful shortfall” in staffing, funding and in technological resources. growth with AI and ML.

A lack of technical skills was identified as a key issue, with 87% of respondents saying their inability to recruit and fill data science roles is hindering their organization’s ability to innovate in this area.

Similarly, 81% of respondents reported that their current tools are unable to fully measure the revenue impact of their AI/ML initiatives, leaving data teams “flying blind” with their applications.


Why do CDOs and CDAOs want more purchasing power?

The budget, and more specifically those responsible for the budget, has been identified as one of the biggest sticking points for CDOs and CDAOs.

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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 involved in only about 56% of purchases.

CDOs and CDAOs cited competing priorities for technology spending between data and analytics teams and IT: 99% said it was difficult to convince IT to spend budgets on data science, ML and Focus on AI initiatives as opposed to traditional IT areas such as security, interoperability and governance.

Data managers suggested that the lack of procurement controls had an impact on recruitment and hiring, with 99% of CDOs and CDAOs reporting that their inability to ensure data and analysis teams with the chosen tools, had a negative impact on their ability to hire. retain and develop technical talent.

Transition from “defensive” application to “offensive” application

The study found that CDOs and CDAOs are feeling even more pressure to manage their organization’s AI/ML initiatives now that business leaders are looking to use their data more innovatively.

Two-thirds (67%) of respondents said their strategy has shifted from a “defensive” position focused on data governance, governance, compliance and business intelligence modernization to a more “offensive” strategy that creates new business value through innovative artificial intelligence. and ML applications.

Accordingly, 98% of data leaders agreed that the speed with which organizations can develop, operate and improve AI/ML applications “will determine who survives and thrives amid persistent economic challenges.”

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As a result, another 67% of CDOs and CDAOs felt it was time to take the reins from IT to prevent their organization from falling behind. Domino Data Lab concluded that IT departments[do] has no power to promote AI/ML innovation.”

Risks of underfitting data groups

In addition to falling behind rivals and missing out on new data-driven revenue streams, ill-equipped data teams also face more immediate risks: 46% of CDOs and CDAOs surveyed admitted they lack the necessary governance tools to prevent data teams from introduce risks 44% felt that failure to properly manage their AI/ML applications could result in up to $50 million in lost revenue.

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“Today’s vast and rapidly evolving regulatory environment, coupled with the high stakes of many enterprise data science initiatives, means that a lack of reliable AI can cost tens of millions,” the report said.

Kjell Carlsson, head of data science strategy and evangelism at Domino Data Lab, said the results were “sobering” and cautioned against putting pressure on data leaders to do more with less.

“Executives are faced with the constant challenges of hiring and retaining data science talent to prioritize IT investments in AI/ML over traditional priorities such as data management and weak capabilities to manage and govern AI/ML models” Carlsson said. “CDAO and CDO roles are already notorious for rapid turnover, and the ever-widening gap between expectations and ability to deliver does not bode well for their life expectancy.”

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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 across multiple parts of the business.

Additionally, in order to attract and retain talent, organizations should invest in data scientists with a “wide variety of tools” they are trained to use, as opposed to 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 entire ML model lifecycle from development to deployment, monitoring and retraining,” said Carlsson. “To achieve this, CDAOs and CDOs must develop alignment and close collaboration with IT. If this is not possible, then they have no choice but to implement these platforms themselves.”

Survey methodology

The Domino Data Lab survey was conducted by Wakefield Research among 100 chief data officers and data analytics officers between December 5 and 18, 2022 at US companies with more than $1 billion in annual revenue, using an email invitation and online survey. According to Domino Data Lab, the margin of error for the study was roughly 9.8%.

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