Real-Time Data is the Bottleneck for AI: Lessons from Gartner D&A Summit 2025

AI deployments are scaling across organizations, but most still lack the trustworthy, real-time data these systems need to work. At the Gartner Data and Analytics Summit in Orlando, thousands of data leaders gathered to address this gap.

The keynote focused on an underappreciated variable: trust. Gartner introduced the concept of a trust model, an inventory that categorizes data by value and risk. Each dataset gets a trust rating based on lineage and curation. With the right trust models, organizations can govern data more effectively and accelerate AI adoption.

Why Real-Time Data Breaks AI Pipelines

At the conference, Conduktor presented jointly with Cloudera. Quentin Packard (SVP of Sales at Conduktor) and Dr. Ian Brooks (Principal Solutions Engineer at Cloudera) discussed the critical role of clean, real-time data for AI.

Two use cases stood out:

Retrieval-augmented generation (RAG): Automatically adds real-time contextual data to prompts to produce more accurate outputs.

Agentic AI: Agents share real-time data to automate complex, multi-step processes.

Bus operators, banks, and airlines depend on these systems for revenue-critical functions: rebooking delayed passengers, blocking credit card fraud.

But managing real-time data at scale is hard. Data originates from multiple sources, arrives in different formats, and contains inconsistencies, stale information, and errors. This low-quality data causes model drift and inaccurate outputs. Poor results drag down productivity, worsen decisions, and erode trust from both internal users and customers.

Beyond technical problems, organizational issues compound the challenge. Teams and data systems are siloed. Documentation is missing. Ownership of data processes like collection and validation is unclear.

How Conduktor and Cloudera Solve the Real-Time Data Problem

Conduktor and Cloudera together provide a consistent flow of clean, streaming data for AI initiatives.

Conduktor enables a shift-left approach: enforce data quality at ingestion and fix problems at the source. This removes duplicates and subpar data, provides visibility into streams, and makes real-time AI cheaper and easier to operate.

Cloudera's Kafka-compatible Open Data Lakehouse combines the flexibility of a data lake with the structure of a data warehouse. Teams can run batch and streaming data in one place, optimize structures for fast retrieval, and accelerate testing and deployment of RAG and real-time inferencing.

As AI covers more real-time use cases, data trust becomes the differentiator. Combining Conduktor's data controls with Cloudera's lakehouse ensures high-quality data, rapid iteration, and AI deployments that actually work.

To learn more about what Conduktor and Cloudera can do for your real-time AI applications, sign up for a free trial today.