Gartner’s top 5 data & analytics predictions for 2025

Did you know that Gartner estimates that by 2025, 90% of current analytics content consumers will become content creators enabled by AI?

Leading analytics, research, and expert guidance firm, Gartner recently shared their thoughts on the future of the data and analytics industry, including understanding how to work with emerging AI tools while ensuring high-quality data management.

Keep reading to discover Gartner’s top predictions for the future of data & analytics as AI tools grow in adoption and the amount of data collected grows in quantity.

More and more, data and analytics leaders worldwide are seeking ways to better work with leading AI tools, invest in their data products, and continue using their data as an organizational asset rather than a setback.

The following are Gartner’s top D&A predictions for 2025:

CDAOs must step up or step out

Chief Data & Analytics Officers will see an evolution in their role as 2025 approaches. It’s important these leaders understand and listen to their peers, create a common success story, share in those successes, effectively collaborate, and learn to know the organization intimately.

This means CDAOs will need to not only focus on data governance and quality but also become key strategists and communicators who drive the organization’s data agenda forward. They must bridge the gap between technical teams and business leaders, ensuring data initiatives align with broader business goals and deliver measurable value.

Augmented analytics will become mainstream

Augmented analytics, which leverages machine learning and AI to automate data preparation, insight generation, and insight explanation, will become more widespread.

By 2025, it is predicted that a majority of analytics processes will be augmented, making advanced analytics accessible to a broader audience.

This democratization of analytics will empower more employees across different levels of the organization to derive insights from data, fostering a more data-driven culture.

Data fabric will be the foundation of work

Data fabric, a design concept that serves as an integrated layer of data and connecting processes, will become the foundation for managing an increasingly complex data environment.

As data sources proliferate and data volumes grow, organizations will adopt data fabric architectures to ensure seamless data integration, accessibility, and governance.

This approach will enable a more agile and resilient data management strategy, supporting real-time analytics and decision-making.

More AI means more investment in people

As AI technologies become increasingly integrated into business operations, organizations must prioritize investment in their people.

This includes developing skills in understanding, interpreting, and acting upon AI-generated insights. Critical thinking and data literacy training will become essential for all team members, ensuring they can effectively leverage AI tools and the insights they provide.

Developing skills in understanding, interpreting, and acting upon AI-generated insights, critical thinking, and data literacy training for all team members will help plan for the future of using AI tools and their implementation.

Data literacy as a core competency

Data literacy, or the ability to read, understand, create, and communicate data as information, will become a core competency for employees at all levels.

Gartner predicts that organizations will invest heavily in data literacy programs to ensure that their workforce can effectively use data to drive decision-making and innovation.

This shift will help bridge the gap between data specialists and business users, fostering a more collaborative and data-driven organizational culture.

Data governance will be rebranded as a strategic business need

As organizations recognize the critical role of data governance, it is increasingly being rebranded as a strategic business function. This shift involves tracking data governance investments against business value, assessing the shortcomings of current D&A governance approaches, and fostering a collaborative mindset centered on strategic objectives.

Tracking governance investments against business value and assessing where current D&A governance approaches are failing are good first steps. Organizations must also remember to ask not “What do I need?” but “How can we work together?”

Finally, data governance leaders should remember to insert data governance practices into existing business-led activities to ensure data-driven activities benefit the entire organization.

Conclusion

These predictions highlight the evolving landscape of data and analytics, where AI and advanced technologies play a central role in driving business value.

Organizations that adapt to these changes and invest in the right tools, strategies, and talent will be well-positioned to thrive in the data-driven future.

In conclusion, the integration of AI into business operations necessitates a significant investment in people. By focusing on skill development, strategic planning, continuous learning, collaboration, and strong leadership, organizations can ensure they are well-prepared to harness the power of AI.

This holistic approach will enable businesses to make the most of AI-generated insights, driving smarter decisions and achieving sustainable competitive advantages.