Gartner’s 4 Emerging Data and Analytics Trends of 2024

Research and advisory firm Gartner has identified the four key trends in data and analytics (D&A) for 2024.

Ramke Ramakrishnan, VP Analyst at Gartner, highlights the significance of these trends, emphasizing their potential to reshape how organizations operate and leverage technology for strategic advantages. “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.”

1. Betting the business on AI

The integration of AI in business strategies is more than just a technological upgrade; it is becoming a central pillar in enterprise decision-making. D&A leaders are encouraged to not only develop AI-driven capabilities but also align them directly with core business objectives. This alignment is crucial for demonstrating value and ensuring that AI initiatives are not just experimental but fundamental to the organization’s success.

Gartner stresses the importance of D&A leaders becoming trusted advisors within their organizations, particularly in collaboration with chief financial officers, to turn data and analytics into a driver of growth.

2. Managed complexity

Today's D&A systems are inherently complex and prone to redundancies that can spiral into chaos and inefficiency. Gartner advises that acknowledging and managing this complexity is key.

By 2025, it is predicted that Chief Data and Analytics Officers (CDAOs) will have embraced technologies such as data fabric to address these challenges effectively. This approach will help simplify data management and allow D&A teams to focus more on strategic priorities that add value to the business.

3. Establishing trust

The rise of generative AI has brought data reliability and trustworthiness to the forefront of business concerns. Establishing trust is paramount in an era where data manipulation and bias are significant risks. D&A leaders are tasked with implementing robust decision intelligence practices and AI governance frameworks.

These measures are essential for ensuring that data is ethically managed, secure, and unbiased, thus making it reliable for decision-making processes.

4. Empowering the Workforce

An empowered workforce is critical to navigating the complexities of AI in data and analytics. Gartner emphasizes the importance of fostering AI literacy across all levels of the organization. 

By 2027, it is expected that a significant number of CDAOs will have secured funding for extensive data and AI literacy programs. These initiatives are crucial for helping employees adapt to AI technologies, thereby enhancing their productivity and reducing resistance to new tools.