Every organization today talks about AI. Fewer talk about data foundations. And even fewer manage to connect the two.
The latest global Data, BI and Analytics Trend Monitor 2026 makes one thing clear: while AI innovation is accelerating, the real differentiator remains surprisingly traditional: trustworthy data, strong governance, and a culture that knows how to turn insight into action.
This year’s results reveal a powerful duality shaping modern enterprises: on one side, growing excitement around generative AI, decision intelligence and automation. On the other, a renewed focus on fundamentals like data quality, security, literacy and governance.
Why data quality matters
For the first time in several years, data quality management has reclaimed the top position among global priorities, ahead of data security and privacy.
Organizations are realizing that AI models, dashboards and forecasts are only as reliable as the data behind them. Poor data quality leads to flawed predictions, biased outcomes and low trust, especially as companies start experimenting with AI agents and automated decision-making.
High-performing organizations treat data quality as continuous discipline, not a one-off cleanup project. They invest in monitoring, anomaly detection, standardization and clear ownership, ensuring that business users can rely on data with confidence.
The human side of analytics
Beyond technology, three trends consistently rank among the most critical success factors:
- Data-driven culture
- Data and AI governance, literacy
Together, they form the organizational backbone of analytics maturity. Best-in-class companies don’t just deploy tools; they build shared understanding. Employees at all levels are empowered to work with data responsibly, while governance frameworks ensure transparency, compliance and accountability as AI becomes more embedded in daily operations.
Data literacy is no longer a specialist skill. It’s becoming a core business capability. This shift reflects a broader truth: analytics transformation is as cultural as it is technical.
Self-service and AI are changing decision making
Self-service analytics continues to be a major enabler of agility. Business users increasingly expect direct access to insights without waiting for IT.
Generative AI is accelerating this trend dramatically. Natural language interfaces now allow users to ask questions conversationally, generate visualizations automatically and uncover insights faster. At the same time, AI is helping data teams automate repetitive engineering tasks like SQL generation, pipeline scripting and data preparation. A természetes nyelvi interfészek lehetővé teszik, hogy a felhasználók beszélgetésszerűen tegyenek fel kérdéseket, automatikusan generáljanak vizualizációkat és gyorsabban tárjanak fel összefüggéseket. Eközben az AI segíti az adatcsapatokat az ismétlődő mérnöki feladatok automatizálásában is, például SQL-generálásban, pipeline-scriptingben vagy adat-előkészítésben.
The result? Faster time to insight and lower barriers to entry for analytics across the organization. But democratization doesn’t remove the need for structure. In fact, it increases the importance of governance, data quality and platform-level consistency.
From analytics to action: decision intelligence
One of the most notable developments in 2026 is the growing maturity of decision intelligence.
Rather than stopping at dashboards, organizations are embedding analytics directly into operational workflows, enabling automated or semi-automated decisions in areas like pricing, supply chain, fraud detection and planning.
Employees increasingly design, supervise and refine models instead of making every decision manually. That makes transparency, explainability and ethical oversight essential components of modern data strategies.
What best-in-class companies do differently
Across industries and regions, top performers share a consistent pattern, they balance strong foundations with forward-looking innovation.
They prioritize:
- Data quality management
- Data-driven culture
- Data security and privacy
- Data and AI governance
- Self-service analytics
And they move faster in adapting:
- Advanced analytics and AI
- Decision intelligence and automation
- Data valuation and monetization
In other words, they don’t chase trends in isolation. They build integrated data ecosystems that turn insight into measurable business impact. They build integrated data ecosystems that turn insight into measurable business impact.
Where Strategy fits in
As organizations move toward AI-powered, decision-centric operating models, the role of enterprise analytics platforms becomes more critical than ever. This is where Strategy plays a key role.
Strategy provides a unified analytics layer that connects governed enterprise data with business intelligence and AI, enabling organizations to deliver consistent metrics, trusted semantics and real-time insights at scale.
By combining a strong semantic foundation with embedded analytics and AI capabilities, Strategy helps enterprises bridge the gap between data and decisions, supporting everything from executive dashboards to operational applications and intelligent automation.
In a landscape where fragmented tools often create conflicting truths, platform-level consistency becomes a strategic advantage.
Five takeaways for Business Leaders in 2026
- AI success starts with data quality. Without trustworthy data, automation amplifies errors.
- Culture matters as much as technology. Literacy and governance enable sustainable analytics adoption.
- Self-service is becoming the default but only works with strong foundations.
- Decision intelligence is moving analytics from insight to action.
- Integrated platforms outperform disconnected tools when scaling AI across the enterprise.
Summary
The future of analytics is not about choosing between foundations and innovation. Organizations that invest in data quality, governance and literacy, while embracing AI, automation and decision intelligence are building the next generation of intelligent enterprises.
