Top data & analytics trends shaping business in 2025

Top trends in AI, automation and analytics every business leader needs to know for 2025.

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Jerome Smail
Jerome Smail
07/29/2025

Data trends

Across industries, artificial intelligence (AI), advanced analytics and the relentless pursuit of operational excellence are converging to fundamentally transform how organizations use data

As noted by Forbes, the AI revolution has democratized access to powerful analytical tools that were once the domain of specialists. As a result, a whole new world of possibilities has opened up. The business world has responded accordingly and, according to Gartner, organizations are moving beyond traditional reporting and an increasing number are now embedding AI-powered analytical insights directly into their decision-making processes.

The trends of 2025 are reinforcing a long-term shift: positioning data as an asset that powers strategy, rather than simply a tool for historical analysis.

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Augmented analytics and natural language interfaces

Augmented analytics uses technologies like machine learning and natural language processing to take the heavy lifting out of data analysis. Instead of relying solely on specialists, everyday users can now access powerful insights with just a few clicks or simple questions. With natural language interfaces becoming more common, people can explore complex datasets by using plain business terms – no coding or technical skills required.

This shift is helping to level the playing field. Smaller, more agile businesses can now access the kind of advanced analytics that were once only available to large enterprises, breaking down traditional barriers and opening up new opportunities for data-driven decision making.

Agentic analytics and intelligent automation

Unlike traditional robotic process automation, intelligent automation uses AI and machine learning in combination with advanced analytics to turn business processes into sophisticated autonomous systems.

The technology enables AI agents to transform entire workflows – processing insights, executing tasks and interacting with natural language interfaces to make data more actionable. 

For example, logistics company Maersk implemented intelligent automation within its cash collections process, using algorithms to learn from customer behaviors and predict payment patterns. This enabled both risk assessment and cash flow prediction based on behavioral insights.

Real-time analytics

Organizations are shifting focus from purely historical analysis to instantaneous insights, with AI enabling split-second decision-making to capitalize on critical market opportunities.

For example, to enable real-time inventory management and demand prediction, fashion retailer Zara embedded microchips into garment security tags, added RFID tags to packaging and employed AI-powered analytics to process the information. With the real-time insights that followed, the company was able to ensure that its most popular items remained available. 

This kind of real-time data collection and analysis is quickly shifting from a nice-to-have advantage to a must-have for businesses that want to stay competitive. 

Predictive maintenance and quality control

While real-time data drives immediate decisions, historical data remains just as important – especially when paired with AI. 

By learning from past patterns, machine learning models can anticipate future outcomes, such as when equipment is likely to fail or require maintenance. This predictive capability allows businesses to act early, preventing costly downtime and optimizing performance.

Toshiba has successfully put this into practice, using AI-driven insights to enhance equipment reliability, extend asset lifespans and reduce both energy consumption and operating costs.

Regulatory frameworks and data sovereignty

A new wave of AI oversight is reshaping how organizations handle data and deploy AI systems. The EU AI Act, for example, is forcing companies to rethink their data practices globally. The situation remains in a state of flux, with other regions looking to either follow suit with strict controls or maintain lighter regulations to foster innovation.

As AI systems become more sophisticated, fundamental questions about data ownership are becoming business-critical issues. Who owns the insights generated by AI? What happens to personal data used to train these systems? These aren't just legal questions anymore – they're strategic business decisions that affect everything from partnerships to revenue models.

The result is a new competitive landscape where data governance capabilities are becoming just as important as the AI technology itself. Organizations that can navigate these regulatory complexities while maintaining innovation speed will gain significant advantages over competitors still struggling with compliance.

Synthetic data and privacy-preserving analytics

Real-world data isn’t always easy to come by. It can be incomplete, sensitive, or – as we have seen – locked behind strict regulatory barriers, especially in industries like financial services. That’s where synthetic data is making a real impact. 

By generating artificial datasets that closely resemble real ones, organizations can train AI models and test new systems without compromising privacy or breaching compliance rules.

Debasmita Das, a data scientist at Mastercard, sees synthetic data as a powerful tool for solving some of the banking sector’s biggest challenges. “We can get rid of the conventional compliance obstacles and silos that come with working with sensitive data by using financial synthetic data,” she explains.

Das also highlights its value in testing extreme or rare scenarios. “We don’t always have the data from these circumstances,” she says. “These gaps can be filled with synthetic data generations, which can also assist organizations in creating plans of action for situations of this nature.”

By removing traditional barriers to data access and enabling safe experimentation, synthetic data is helping financial institutions increase innovation, improve efficiency and respond with agility to a fast-changing market – all while staying on the right side of regulation.

Data-centric AI and quality focus

AI is now helping organizations automatically improve their data quality in ways that would have taken teams of people months to accomplish. Machine learning algorithms can scan through massive datasets to identify errors, inconsistencies and gaps, while automated systems clean and organize information without human intervention. 

AI-powered data validation tools can spot problems in real-time and suggest corrections, ensuring that the data feeding into business decisions is accurate and reliable.

At the same time, many businesses are moving away from large, resource-heavy AI models toward smaller, specialized ones designed for specific tasks. These models often perform better for particular business needs while using less computing power and costing less to run.

This approach reflects a broader trend: in AI, quality and precision are proving more valuable than size and complexity.

Keeping pace with a data-driven future

The trends shaping analytics in 2025 mark a deep shift in how organizations turn information into value. It's no longer just about having data; it's about how effectively you use it. That means integrating AI, strengthening data governance and making insights accessible across the business. Those that fail to evolve risk falling behind in an economy that's increasingly driven by data.

The organizations that embrace this shift – while staying mindful of privacy, ethics and regulation – will be the ones that lead in a more intelligent, automated future.

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