VIDEO PODCAST
Indicium's Daniel Avancini on Using Your Own Data in AI Deployments
James Maguire
July 20, 2025

As companies face the challenge of integrating generative AI into their operations, many are discovering that the real power—and complexity—lies in their own data. In this TechVoices interview, Daniel Avancini, Chief Data Officer at Indicium, offers a clear-eyed view of how companies can leverage proprietary data effectively while avoiding common pitfalls.

From the rise of retrieval-augmented generation (RAG) to the risks of shadow AI and the ongoing need for robust data governance, Avancini lays out a practical roadmap for deploying AI responsibly and at scale.

Core Takeaways
Retrieval-Augmented Generation (RAG)
arge language models but instead using Retrieval-Augmented Generation (RAG) to allow natural language querying over structured or semi-structured proprietary data.
Shadow AI
is a real enterprise risk.
data—not models—is the key differentiator.
y available, data—not models—is the key differentiator.
data governance.
need for data governance.
KEY QUOTES

How AI models access data

You are not training a model, as people like to say, but you are using the models to search and to have a natural language interaction with your data. When you have a chatbot, for instance, and you ask, 'How much did I sell last month?' the LLM converts your question into a query, gets the data back, and converts the result into a natural language response. So the model isn't really accessing the data—it's just formatting the queries. This is probably the most common way people are using GenAI in companies right now.

Shadow AI

Many companies that are afraid of using AI are actually creating a problem for themselves because their employees are using AI without a license, and then they're incurring this risk. That's shadow AI. It's kind of the reverse of what IT departments think: 'We'll block AI to protect data,' but that just drives people to use unapproved tools, increasing the likelihood of data leakage or compliance issues.

AI requires data governance

AI does not fix data quality problems—especially when those problems come all the way from the source. It enables many more people to work with data, yes, but it requires an even larger investment in data governance and data quality than companies may have made in the past five to ten years. It's not a silver bullet. If anything, AI makes data platform engineering and stewardship more critical, not less.
ABOUT THE AUTHOR
James Maguire
Executive Director
An award-winning journalist, James has held top editorial roles in several leading technology publications, covering enterprise trends in cloud computing, AI, data analytics, cybersecurity and more. He regularly communicates with industry analysts and experts and has interviewed hundreds of technology executives.
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