We’re joined today by Chloe Maraina, a renowned Business Intelligence expert whose passion lies in transforming vast, complex datasets into compelling visual stories. With a keen aptitude for data science, Chloe has a unique vision for the future of data management in the AI era. In our conversation, we’ll explore some of the most pressing challenges and innovative solutions in the field, delving into why siloed information is crippling AI initiatives, how intelligent systems can learn and adapt to user workflows, the critical importance of providing AI with governed, live data to prevent hallucinations, and the architectural foundations required to build a truly AI-ready enterprise.
You’ve called siloed data the “kryptonite” of AI transformation. Can you elaborate on how modern platforms specifically break down these barriers? Perhaps walk us through how they create a trusted, rich data asset that an AI agent can actually rely on?
Absolutely, “kryptonite” feels like the right word because silos don’t just slow things down; they actively poison an AI’s ability to learn and function. The most effective way to break them down is with a platform that acts as a central system of context. Think of it as a bridge, like the Reltio MCP Server, that connects all your disparate data islands. The process starts with prebuilt agents, like those in Reltio AgentFlow, that are designed for specific data governance tasks and workflows. For example, an agent can reach into your Salesforce CRM, pull customer records, and then immediately trigger a cleansing and standardization workflow. Simultaneously, another agent pulls related invoice data from your ERP system. The platform then intelligently unifies this information, creating a single, rich, and—most importantly—trusted profile of that customer. This isn’t about creating “good enough” data; it’s about forging a complete, secure asset that gives your AI the deep context it needs to deliver a true competitive advantage.
A fascinating concept you’ve discussed is treating a data platform like a “well-trained application expert” that learns over time. How does this kind of system actually suggest next actions and adapt to a user’s preferred workflows, and could you share a scenario where this approach has dramatically reduced an organization’s time to value?
It’s a powerful shift in perspective, moving from a static tool to a dynamic partner. A system like Melissa Unison embodies this by observing and learning from user interactions. Imagine a junior data analyst tasked with their first major project. They need to connect to multiple data sources, but they’re not sure which fields are most important or what the best practices for data quality are. This “application expert” guides them, suggesting connections, helping map content to the correct fields, and even proposing the next logical actions, like running a data quality check or an enrichment process. Over time, it learns this analyst’s preferred methods and begins to automate them, establishing a persisted workflow. We saw this with a client whose onboarding for new data team members used to take weeks. By using a guided, learning-based system, new hires became productive in a matter of days because the platform itself was their expert guide, removing the initial barrier to entry and dramatically accelerating their journey to creating valuable data products.
You’ve pointed out a critical danger: AI will hallucinate without governed access to live data. How can organizations provide this verifiable access without creating risky, unsecured copies of their data, and what does that technical process look like?
This is one of the most significant and dangerous hurdles in deploying AI today. The moment an AI doesn’t have the data it needs, it will invent an answer, leading to disastrous business decisions. The knee-jerk reaction is to create copies of data and feed them to the AI, but this is a security and governance nightmare. A far better approach is to implement what insightsoftware calls an AI semantic platform, like Simba Intelligence. Think of it as a highly intelligent and secure data translator that sits between your AI agents and your live data sources. When an AI asks a question, the semantic platform intercepts it. It understands the context of the query, applies all the necessary governance and security rules, and then fetches only the required data directly from the live source. It provides verifiable access without ever making a risky copy, effectively eliminating hallucinations at the source and ensuring every AI-generated insight is grounded in secure, accurate, and real-time information.
When building an AI-ready architecture, you emphasize the need for “fine-grained governance.” How can a modern connectivity platform manage this requirement at a massive scale, and could you share an anecdote where this level of control prevented a potential data security disaster?
Fine-grained governance is non-negotiable for a serious AI-ready architecture. It’s about moving beyond simple role-based permissions to control access at the level of individual rows and columns, along with defining read/write privileges. A platform like the CData Connectivity Platform achieves this by creating a universal connectivity layer that acts as a central checkpoint for all data requests. We had a client in the financial sector implementing an AI-powered analytics tool for their wealth managers. The AI needed live access to client portfolio performance but was strictly forbidden from accessing personal identifying information. A junior wealth manager, using the AI tool, inadvertently crafted a query that could have exposed the home addresses of high-net-worth individuals. The connectivity platform’s fine-grained governance immediately blocked the query because it violated a policy preventing access to that specific data column. That real-time, automated intervention prevented a massive data breach and a potential compliance disaster, all while allowing the tool to function seamlessly for legitimate queries. That’s the power of building governance directly into the data movement fabric.
What is your forecast for the evolution of AI-driven data management over the next three to five years, particularly regarding the role of automated governance and real-time data integration?
Looking ahead, the lines between AI and data management will blur almost completely. We are moving beyond an era where AI merely consumes data products to one where AI actively builds, manages, and governs them. I forecast that automated governance will become standard, with AI agents not just enforcing rules but proactively identifying data quality issues and suggesting new governance policies based on real-time usage patterns. Real-time data integration will be the default, powered by intelligent pipelines that can learn and adapt on the fly to changes in data sources or business logic without human intervention. The ultimate evolution is a self-optimizing data ecosystem where AI and data teams work in a seamless loop, continuously enhancing data quality, security, and accessibility to fuel a new level of intelligent applications we are just beginning to imagine.
