Three Trends Shaping the Future of Business Intelligence
As organizations mature in their data capabilities, three major trends are reshaping how people will work with BI in the coming years: the rise of AI, the shift toward outcome-driven BI, and the emergence of Data Mesh. Let’s explore what these trends mean and how they’ll influence the world of BI.
1. The Rise of AI impacts BI
Artificial Intelligence has already proven to be one of the most transformative forces in technology, and this also influences how we work with BI. From generative AI to predictive models, AI is changing how we gather insights, build reports, and recognize patterns.
Before organizations can fully benefit from AI, they need a robust BI layer in place: clean data, solid governance, and reliable reporting structures. Without this foundation, AI becomes a high-powered tool operating on shaky ground. Garbage in, garbage out.
Tools like Microsoft’s Copilot or other generative assistants already allow users to:
- Generate Power BI reports automatically
- Ask questions and get instant data-based answers
These tools aren’t perfect yet, but they significantly shorten the time between question and insight. What used to take hours may soon take minutes.
AI excels at identifying patterns that humans might overlook or spend days uncovering. This means analysts can spend less time digging and more time interpreting and acting on insights.
But the human element remains essential
Even with sophisticated automation, BI will continue to rely on human expertise. People must:
- Validate AI-generated conclusions
- Provide business context
- Translate insights into actions
In the coming years, AI won’t replace BI professionals. It will elevate their roles.
2. From traditional BI to outcome-driven BI
In the early days of Big Data, back in the 1990s and early 2000s, companies collected massive amounts of information simply because they could. The common question was: “We have all this data, now what?”
This is changing.Modern BI teams increasingly begin by defining the business outcome first:
- What decision do we want to support?
- What business challenge are we trying to solve?
- What metric do we want to improve?
Next, they determine what data products are needed to enable that outcome. Outcome-driven BI turns analytics from a data-dumping exercise into a purposeful, value-creating practice.
3. Data Mesh: the transition from IT-driven BI to business-driven BI
For years, BI was primarily an IT responsibility. IT teams collected the data, cleaned it, built the dashboards, and decided how everything fit together. This often led to bottlenecks, misunderstandings, or long wait times for business teams.
Data Mesh flips that model.
What is Data Mesh?
Data Mesh is an approach that distributes data ownership to the domains that know the data best, usually the business teams. Instead of one centralized data team, you have decentralized domains that are responsible for: defining their own datasets and managing data quality.
Data Mesh speeds up insights because business domains no longer rely on long IT queues. Data quality improves since domain experts manage the data they know best. It also scales more effectively by reducing pressure on central teams, while producing more relevant analytics created close to where decisions are made.
In a Data Mesh world, BI becomes a collaborative effort between business and IT. And the business takes a more active, empowered role.
The road ahead for BI
Yet through all this innovation, one constant remains: BI is still a human-centered discipline. The tools get smarter, but people will continue to shape the questions, decisions, and strategies that give BI its purpose.
The future of BI isn’t just about technology. It’s about enabling smarter, more empowered organizations on decision intelligence.
This blog was originally presented by Gert Jan Toering (Data strategist) at the ICT&Logistics expo in Utrecht.
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