As more and more organizations deploy AI to power key business functions and processes, the need for trustworthy, real-time data has never been more important. At last week’s Gartner Data and Analytics Summit in Orlando, Florida, thousands of senior leaders in the data space gathered to learn about data architectures, governance, and the future of AI. 

This year’s keynote was centered around AI, specifically the intersection of data, machine learning, people, and processes. One key takeaway from the keynote was how some unexpected variables—such as trust—could affect data value, and thus, create obstacles for implementing AI.

In addition, the Gartner team introduced the concept of a trust model, an inventory that could categorize data by value and risk and ultimately, assign a trust rating based on the lineage and curation of specific data and datasets. By implementing the right trust models, the Gartner team argued, organizations could more effectively govern data, deliver valuable, trustworthy data products, and streamline the road to AI.

Why using real-time data in AI is so difficult

For Conduktor, the Gartner D&A Conference was a great opportunity to explain how customers are using our solutions to effectively secure and govern streaming data, reduce risk, and provide a continuous flow of trustworthy, contextualized real-time data to fuel AI initiatives, promoting trust—in data as well as the products powered by said data.

One highlight was our joint presentation with Cloudera, one of today’s leading cloud data providers, featuring Quentin Packard, SVP of Sales at Conduktor, and Dr. Ian Brooks, Principal Solutions Engineer at Cloudera. 

Quentin and Dr. Ian Brooks discussed the importance of clean, real-time data for AI in use cases such as retrieval-augmented generation (RAG), which automatically adds real-time contextual data to prompts in order to produce more trustworthy outputs; and agentic AI, where agents share real-time data to accurately automate complex, multi-step processes. Organizations like bus operators, banks, or airlines use these real-time AI applications to carry out vital business functions (such as rebooking delayed passengers or blocking credit card fraud) that directly impact revenue and reputation.

But managing real-time data (especially at speed and scale) can be difficult, given that data may originate from various sources, be configured in diverse formats, and contain inconsistencies, outdated information, and errors. This low quality data is the Achilles heel of AI, leading to model drift and inaccurate outputs. In turn, these poor results drag down productivity and profits, worsen decision making, and ultimately decrease trust from internal users and customers alike.

Aside from the technical obstacles, there may also be organizational issues. For instance, both teams and data systems may be siloed, lacking documentation, communication, and standardization. Teams may also be unclear on who owns data processes like collection or validation, or even how to govern data in the first place.

Ensuring clean, real-time data streams for AI with Conduktor+Cloudera

The solution? Using Conduktor and Cloudera together to ensure a consistent flow of clean, streaming data for all AI initiatives, improving accuracy and trust in the outputs. 

As a data management platform for streaming, Conduktor can unlock the full potential of real-time data—and fast track AI adoption in an organization. With Conduktor, teams can take a proactive, shift-left approach, enforcing data quality at the point of ingestion and addressing data problems at the source. This removes duplicate or subpar data, provides visibility into data streams, and most importantly—makes running effective, real-time AI cheaper and easier.

As the other piece of the puzzle, Cloudera’s Kafka-compatible Open Data Lakehouse provides the flexibility and distribution of a data lake alongside the rigorous structure of a data warehouse, resulting in an intuitive, inexpensive platform that can run on any cloud. Teams can run batch and streaming data in one place, optimize data structures for fast retrievals and analysis, and ultimately, facilitate the testing and deployment of features like retrieval-augmented generation and real-time inferencing.

As AI begins to cover more and more real-time use cases, considerations like trust become increasingly important. By combining Conduktor’s data controls alongside Cloudera’s data lakehouse, teams can ensure high-quality data, build and test rapidly and cheaply, and roll out AI initiatives without compromising accuracy or trust.

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