Why Data Streaming Platforms are no longer optional in the AI era?

In the data economy of 2025, one thing has been clear: data streaming platforms (DSPs) have crossed the threshold from promising technology to business-critical infrastructure. Confluent’s latest Data Streaming Report 2025, which surveyed over 4,000 IT leaders globally, makes a compelling case for why real-time data is not just nice to have; it is essential for innovation, ROI, and staying competitive.

The age-old mantra of "data is the new oil" has evolved. In today's hyper-connected, AI-driven digital economy, real-time data is the new oxygen. And DSPs are the circulatory system.

According to the report, 89% of IT leaders believe DSPs ease AI adoption by addressing foundational hurdles like data access, quality assurance, and governance. An overwhelming 86% have prioritised data streaming in their 2025 tech roadmaps, up significantly from previous years. Hence, this is not hype; it is a strategic realignment.

The need for speed is evident: 44%  IT leaders reported 5x ROI on data streaming investments, and 52% of those at maturity Levels 4 and 5, utilising advanced streaming adoption report exponential returns. But even beginners (Level 2) are not far behind, 74% report at least 2x to 5x ROI.

From integration nightmares to streaming nirvana

One of the most persistent challenges in data architecture is fragmented systems and data silos. The report finds 68% of organisations are struggling with inconsistent data sources, 63% of fragmented ownership, and 67% of data quality issues.

In such scenario, DSPs help streamline access, enforce governance, and unify fragmented architectures. This ability to "connect and clean in motion" is vital not just for IT operations but for business agility and regulatory compliance.a 

The thought very well aligns with what McKinsey and Gartner have also been highlighting: enterprise data maturity is shifting from batch-heavy legacy systems to real-time pipelines that support federated architectures, AI readiness, and data mesh strategies.

The real enabler of AI

AI is only as good as the data it learns from and how quickly that data arrives. Confluent’s report reveals 87% of IT leaders now use DSPs to feed AI systems, with particular emphasis on GenAI, AI-enhanced analytics, and agentic AI.

Yet, the AI journey is still hampered by systemic issues like fragmented ownership, infrastructure constraints, and limited ability to integrate new data sources.

A smarter way to architect data pipelines

The concept of “shifting left,” borrowed from DevOps, is gaining ground in data integration. Instead of fixing bad data downstream, organisations are embedding data quality checks, governance, and transformations closer to the source.

This shift results in reduced latency, lower processing costs, better data discoverability, and faster innovation. 93% of IT leaders recognise at least four key benefits of this approach. DSPs, with capabilities like inline policy enforcement, embedded processing, and discovery mechanisms, are the linchpin to making shift-left real.

Confluent’s report also touches on the rise of data products, reusable, governed data streams designed to serve multiple internal consumers. This approach is instrumental in dismantling siloed ownership and building a shared enterprise view of data.

Other reports from Snowflake, Databricks, and Forrester echo this evolution. The focus has moved beyond “big data” to "good data", trusted, contextual, and universally accessible. In a world where AI agents and real-time decision-making are the new competitive edge, data streaming is no longer just middleware; it is strategy.

As organisations invest heavily in AI, cloud, and edge architectures, DSPs are proving to be the connective tissue of this new world. They are not just pipelines; they are turning out to be platforms for intelligence.

It seems data streaming is no longer a choice. It’s an imperative. The question is not if you will adopt it, but how fast you can mature along the streaming curve before the competitors do.