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Gartner Predicts More Than 50% of CDAOs Will Secure Funding for Data Literacy and AI Literacy Programs by 2027

Analysts to Discuss the Evolving Role of Chief Data & Analytics Officers at Gartner Data & Analytics Summit 2024, March 11-13 in Orlando



By 2027, more than half of chief data and analytics officers (CDAOs) will secure funding for data literacy and artificial intelligence (AI) literacy programs, fueled by enterprise failure to realize expected value from generative AI (GenAI), according to Gartner, Inc.


“Few organizations are currently implementing AI literacy programs. While the skills and capabilities of AI are concentrated to highly technical roles, the status of AI is rapidly changing as industry executives begin to realize the importance of a workforce knowledgeable in data, analytics and AI,” said Melissa Davis, VP Analyst at Gartner. “To build such a workforce, organizations require data literacy and AI literacy as core competencies. Critical-thinking and problem-solving abilities may decrease as AI natives depend more heavily on AI for information and decision-making, diminishing their need to analyze situations independently.”


Improving Data and AI Literacy is Critical for Organizations to Reach the Expected Value of AI


GenAI solutions are less trustworthy due to the complexity and opaqueness of the current algorithms and models, as well as the information used to fuel the models not being adequately curated. This is why CDAOs must invest in their people to build strong data, analytics and AI skills. Without these skills, AI will fail to deliver the expected value and potentially introduce additional failure points.


“Improving data and AI literacy is critical to identifying relevant and value-adding AI use cases,” said Davis. “Yet, turning the general promise of AI into concrete business impact demands strong collaboration between business stakeholders and AI experts which requires a common ground in terms of understanding the main AI concepts and having realistic expectations about what AI can and cannot do.”


“Organizations should assess the AI readiness of their workforce for both data and AI literacy and be honest as to whether the workforce has the necessary skills to leverage AI techniques,” said Davis. “For any area of planned investment in GenAI technology or AI use-case implementation, organizations should fund corresponding data and AI literacy education as a core workforce capability.”


Chief Data and Analytics Officers Must Lead Change


Gartner predicts that by 2028, 25% of chief data and analytics officer vision statements will become “decision-centric,” surpassing “data-driven” slogans, as human decision-making behaviors are modeled to improve data and analytics.


According to David Pidsley, Senior Director of Research, Analytics of Gartner, data and analytics technical professionals can avoid visualization mistakes, improve content and ultimately support superior business decisions by taking the following actions:


  • Understand the audience for their dashboard, and storyboard the design based on their needs, purposes and levels of Data Literacy. Align metrics and charts to the use case and the business expectations of the dashboard. Resist the temptation to create all-inclusive dashboards or custom views into specific dashboard features.

  • Enhance the insights delivered through contextual visualizations by leveraging the Augmented Analytics features in analytics and BI tools. Using NLQ NLG and GenAI, these features can produce narratives that improve understanding and interpretation of insights derived from the data.

  • Contextualize and simplify data visualizations to deliver concise insights for busy users. Aligning the data visualization type to the metric’s data type makes the resulting analytics easier for dashboard consumers to understand quickly.

  • Broaden adoption by optimizing dashboards for User Experience UX and delivering them to Analytics Platforms that users prefer. In addition, review the key elements impacting performance, including excessive use of data visualizations and latency from available sorting, filtering and drill-down features.





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