The Three Trends Reshaping Enterprise Analytics in 2026

July 14, 2026

What 31,000 Fabric customers want next, and why your data estate is the bottleneck.

At FabCon 2026 in Atlanta, we sat down with Tamer Farag, Global Fabric Partner Lead at Microsoft, to discuss what customers are actually asking for right now. With 25 years at Microsoft and a front-row seat to the fastest-growing analytics platform in history (31,000 customers in just two and a half years) Tamer has a clear view of where enterprise analytics is heading. 

Real-Time Intelligence Goes Mainstream

Real-time analytics isn't new, but the range of industries asking for it has expanded dramatically over the past two years. 

"Originally when we used to talk about real-time intelligence, we only used to talk about manufacturing IoT scenarios," Tamer explained. "But in banking, in healthcare, and in financial services, we're seeing more and more demand for real-time intelligence."

Reasoning on historical data alone no longer cuts it, and organizations want to optimize operations as they happen rather than days or weeks later when a report lands on someone's desk.  

If your analytics strategy treats real-time as a niche capability reserved for sensor data and factory floors, you're already behind. Finance teams, clinical operations, and customer service organizations are all asking for the same thing: insight at the speed of the business. 

Chat With Your Data Replaces Static Reports

The second trend is conversational analytics. 

"Instead of just static reports or customers just looking at graphs that have data, they want to go and ask questions to the data," Tamer said. "Everybody's using ChatGPT and they're just asking questions."

ChatGPT reset user expectations across every category of software, and BI is no exception. The traditional model of requesting a report, waiting for IT, and interpreting a dashboard is giving way to direct dialogue, with users expecting to ask a question and get an answer in the moment. 

This is good news for BI teams, even if it doesn't feel that way at first. The role shifts from report production to enabling trusted, governed access to data that can support these conversations, and the underlying data estate becomes more important than ever because the AI answering those questions is only as good as the data it can reach. 

AI Is Accelerating Legacy Migration

The third trend is speeding up decisions that have lingered on roadmaps for years. 

"For a lot of the customers that were still on prem that were hesitant to move… AI has dramatically accelerated their move to the cloud and the move from legacy systems," Tamer observed. "They need to establish their data estate in order to get all the benefits from AI." 

For a long time, many organizations delayed cloud migration because the business case was incremental, the risk felt manageable, and the legacy systems worked well enough; then AI rewrote the business case entirely. 

Generative AI models deliver limited value when disconnected from enterprise data. As Tamer put it earlier in the conversation, "The real value from AI is when it gets connected to data." Organizations that postponed modernization are now accelerating it, not because the old reasons finally became compelling, but because a new reason emerged that demands action. 

The Common Thread 

Look at what all three trends have in common, and a clear pattern emerges. 

Real-time intelligence requires data infrastructure that can handle velocity. Conversational analytics requires data that's governed, trusted, and accessible to AI models. Legacy migration is happening because fragmented, siloed data estates can't support any of it. 

Every conversation about AI eventually becomes a conversation about data, and the organizations getting ahead are the ones that figured that out early. 

Hear the full conversation with Tamer Farag on the Adastra podcast >>>