When data software vendor Cloudera hosted a lunch for just under 20 corporates at the Gartner Data & Analytics Summit in June 2025, all of the participants put their hands up when they were asked if they were pursuing GenAI projects.

Then Cloudera’s chief technology officer for Australia and New Zealand, Vinicius Cardoso, asked how many were confident in their implementations.

“There was one hand,” says Cardoso.

“It was all about trust. Trust in the data, that it was the right data, and trust in the models they were using.”

In meeting this challenge of trust, Cardoso talks about two concepts, both beginning with the letter “h.”

Firstly, data needs to be “human centric,” which means that it ultimately needs to be controlled by humans “with the intent to produce a good outcome for citizens, consumers, and for people in need.”

Keir Garrett, Cloudera’s managing director for Australia and New Zealand, says that in seeking to build trust, many organizations at the conference were putting a priority on the idea of “data lineage” with their AI projects.

“I think this is part of the maturity curve,” says Garrett. “So many organizations are on the AI journey, but they are waking up and saying, ‘yes, we did pocket AI, and while some of it helped us make good decisions, also by chance we may have made catastrophic decisions because the data was the problem.’”

“So this is really encouraging because in fixing the data, they are looking at the governance and compliance, strengthening their teams in this area, and this is also putting a focus on where the data comes from,” she adds.

Once this governance level is assured, and organizations can be confident that they are using the “right data,” the next step is around operational performance, and this is where the other “h” word comes in: hybrid.

With data coming from so many disparate sources, data management platforms should be unified by cannot afford to be monoliths.

“You don’t have to retrain those models in mass data sets, but you enable them in new data sets, and this helps them stick to the facts.”

Data is managed in the cloud, on premises, and at the edge. There is historical data, real-time data, and data from third parties.

It comes from sensors in the field, from social media and legacy databases, and much of its distribution and processing — certainly when used or GenAI — must happen in real time and is often best powered by Retrieval Augmented Generation (RAG) frameworks.

“RAG helps the large language models to implement their learning with the data sets you have right now,” says Cardoso.

“You don’t have to retrain those models in mass data sets, but you enable them in new data sets, and this helps them stick to the facts, be very specific, and minimize AI hallucinations.”

Life and death situations

In some cases, the technology can make a difference in life and death situations, examples that shine a light on AI’s transformative potential.

Cloudera has partnered with Mercy Corps, a humanitarian organization working on the front lines of crisis and disaster, and created an AI assistant that queries a curated data database of critical information to improve decision making and resource deployment.

Running on AWS with full-stack NVIDIA AI-accelerated computing, it is a cloud-based application called Methods Matcher, which summarizes, references, and recommends a data-driven response in under-pressure crises.

“Instead of getting a group of people together and sitting at a table for hours thinking about how they will react, they now have the ability to respond to situations within seconds, and do so confidently,” says Garrett.

There is also a significant financial payoff from a more effective disaster response. The global cost of natural disasters was estimated at USD250 billion in 2023 as the world endured climate-related crises such as wildfires, floods, and storms.

Cloudera presented the Mercy Corps case study at the Gartner conference as a use case to draw an analogy on how other organizations can adopt similar solutions to address issues such as cyber fraud.

“What Methods Matcher does is reduce the cognitive load on an individual to make decisions,” says Garrett.

“It reduces the time to action and drives better decisions faster, so organizations can be proactive, not reactive.”

Image credit: iStockphoto/Andry Djumantara