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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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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."

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...