Gartner: Top Trends in Data and Analytics for 2024

Gartner, Inc. identified the top data and analytics (D&A) trends for 2024 that are driving the emergence of a wide range of challenges, including organizational and human issues.

"The power of AI, and the increasing importance of GenAI are changing the way people work, teams collaborate, and processes operate," said Ramke Ramakrishnan, VP Analyst at Gartner. "Amidst this technological revolution, organizations that fail to make the transition and effectively leverage D&A, in general, and AI, in particular, will not be successful."

Trend 1: Betting the Business

As AI continues to revolutionize industries on a strategic level, D&A leaders must demonstrate a bet-the-business skill set on AI and earn trust to lead the AI strategy within the enterprise.

"D&A leaders must demonstrate their value to the organization by linking the capabilities they are developing and the work they do to achieve the business outcomes required by the organization," said Ramakrishnan. "If this is not done, issues such as misallocation of resources and underutilized investments will continue to escalate, and D&A will not be entrusted with leading the AI strategy within the organization."

With AI changing the way businesses are run, enterprises are heading towards a cost avalanche. D&A leaders must act by implementing a FinOps practice to establish and enforce standards and decrease expenses.

Gartner predicts by 2026, chief data and analytics officers (CDAOs) that become trusted advisors to, and partners with, the CFO in delivering business value will have elevated D&A to a strategic growth driver for the organization.

Trend 2: Managed Complexity

Many D&A systems are delicate, and their redundancies can cause chaos and added costs. "Leading organizations are working to turn this chaos into something they can manage — complexity. Complexity is, by definition, not an easy place to be, but acknowledging it gives a realistic understanding of the dynamic environment and helps the D&A teams in taking appropriate actions," said Ramakrishnan.

D&A leaders need to embrace complexity by using AI-enabled tools to automate and improve productivity. This includes investing in augmented data management, decision automation, and analytics capabilities like natural language processing (NLP). Gartner predicts, CDAOs will have adopted data fabric as a driving factor in successfully addressing data management complexity, thereby enabling them to focus on value-adding digital business priorities by 2025.

Trend 3: Be Trusted

With the increasing accessibility and efficiency of GenAI, there is a challenge in navigating a world where data reliability is constantly questioned. Lack of trust within organizations, concerns about the value and quality of data, and regulations around AI are leading to a deluge of distrust.

"If data is not trusted, it may not be used correctly to make decisions," said Ramakrishnan.

"D&A leaders should use decision intelligence practices to build trust in data and monitor decision-making processes and outcomes. Additionally, implementing effective AI governance and responsible AI practices is crucial in establishing trust among stakeholders. It includes making data AI-ready which means it is ethically governed, secure and free from bias and is enriched to ensure more accurate responses."

Trend 4: Empowered Workforce

"It is important that employees feel empowered through the use of AI in D&A, rather than causing them to feel threatened or frustrated by it," said Ramakrishnan.

Organizations must invest in developing AI literacy among employees, use adaptive governance practices for effective governance, and implement a trust-based approach to managing information assets, helping individuals understand the provenance of information used by them.

"AI training is not just about quantity; it also requires a different approach. Recognize that the skill sets required for expert AI users will be very different from other users," said Ramakrishnan. "Gartner predicts, by 2027, more than half of CDAOs will secure funding for data literacy and AI literacy programs, fueled by enterprise failure to realize expected value from generative AI."

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Gartner: Top Trends in Data and Analytics for 2024

Gartner, Inc. identified the top data and analytics (D&A) trends for 2024 that are driving the emergence of a wide range of challenges, including organizational and human issues.

"The power of AI, and the increasing importance of GenAI are changing the way people work, teams collaborate, and processes operate," said Ramke Ramakrishnan, VP Analyst at Gartner. "Amidst this technological revolution, organizations that fail to make the transition and effectively leverage D&A, in general, and AI, in particular, will not be successful."

Trend 1: Betting the Business

As AI continues to revolutionize industries on a strategic level, D&A leaders must demonstrate a bet-the-business skill set on AI and earn trust to lead the AI strategy within the enterprise.

"D&A leaders must demonstrate their value to the organization by linking the capabilities they are developing and the work they do to achieve the business outcomes required by the organization," said Ramakrishnan. "If this is not done, issues such as misallocation of resources and underutilized investments will continue to escalate, and D&A will not be entrusted with leading the AI strategy within the organization."

With AI changing the way businesses are run, enterprises are heading towards a cost avalanche. D&A leaders must act by implementing a FinOps practice to establish and enforce standards and decrease expenses.

Gartner predicts by 2026, chief data and analytics officers (CDAOs) that become trusted advisors to, and partners with, the CFO in delivering business value will have elevated D&A to a strategic growth driver for the organization.

Trend 2: Managed Complexity

Many D&A systems are delicate, and their redundancies can cause chaos and added costs. "Leading organizations are working to turn this chaos into something they can manage — complexity. Complexity is, by definition, not an easy place to be, but acknowledging it gives a realistic understanding of the dynamic environment and helps the D&A teams in taking appropriate actions," said Ramakrishnan.

D&A leaders need to embrace complexity by using AI-enabled tools to automate and improve productivity. This includes investing in augmented data management, decision automation, and analytics capabilities like natural language processing (NLP). Gartner predicts, CDAOs will have adopted data fabric as a driving factor in successfully addressing data management complexity, thereby enabling them to focus on value-adding digital business priorities by 2025.

Trend 3: Be Trusted

With the increasing accessibility and efficiency of GenAI, there is a challenge in navigating a world where data reliability is constantly questioned. Lack of trust within organizations, concerns about the value and quality of data, and regulations around AI are leading to a deluge of distrust.

"If data is not trusted, it may not be used correctly to make decisions," said Ramakrishnan.

"D&A leaders should use decision intelligence practices to build trust in data and monitor decision-making processes and outcomes. Additionally, implementing effective AI governance and responsible AI practices is crucial in establishing trust among stakeholders. It includes making data AI-ready which means it is ethically governed, secure and free from bias and is enriched to ensure more accurate responses."

Trend 4: Empowered Workforce

"It is important that employees feel empowered through the use of AI in D&A, rather than causing them to feel threatened or frustrated by it," said Ramakrishnan.

Organizations must invest in developing AI literacy among employees, use adaptive governance practices for effective governance, and implement a trust-based approach to managing information assets, helping individuals understand the provenance of information used by them.

"AI training is not just about quantity; it also requires a different approach. Recognize that the skill sets required for expert AI users will be very different from other users," said Ramakrishnan. "Gartner predicts, by 2027, more than half of CDAOs will secure funding for data literacy and AI literacy programs, fueled by enterprise failure to realize expected value from generative AI."

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Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

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Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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