Building the Foundations for AI & Analytics: Practical Lessons from the Field, Shared by Customers and Partners

Across both days, with data & analytics as the focus on day 1 and AI on day 2, customer and partner sessions once again proved to be a highlight at CData Foundations. What made them stand out was the candor: organizations shared how they are truly laying the groundwork for AI and analytics in practice. The strongest themes weren’t about chasing hype, but about the foundations that sustain progress: governance and trust, integration that bridges silos and enables real-time value, and the business growth that follows when data is finally unified.

Governance and trust are non-negotiable

Across sessions, governance emerged as the backbone of trustworthy analytics and the essential foundation for AI. Whether teams were initiating AI projects, democratizing access, enabling distributed work, or meeting compliance requirements, the consensus was clear: without governance, data initiatives lose credibility.

The stakes get even higher with AI, where missing lineage, controls, and quality guardrails can quickly lead to biased or misleading results. Several speakers stressed that organizations cannot hope to expand AI responsibly without first establishing governance frameworks that make data transparent, auditable, and trusted.

Customers reinforced this point by showing how governance challenges show up even before AI. If analytics projects lack lineage or controls, scaling AI on top of them only amplifies the problem. Michael Docteroff, Senior Director of Delivery for Data & Analytics at Argano, described one client’s challenge:

“One of our clients had ingested data from multiple ERP systems, but they never tracked data lineage. So analysts basically couldn’t explain or understand where certain numbers came from.

Rahul Pahuja, Field CTO at CData, emphasized that governance must be embedded from the beginning:

“Governance is something that you have to do by design rather than do it as an afterthought.”

And Sven Wilbert, Senior Manager at BearingPoint, pointed out that governance becomes even more critical in distributed organizations: democratizing access only works if people can trust the data they’re given. Otherwise, widening access only multiplies risk.

The shared takeaway: governance is not a box-ticking exercise. It is the foundation of trust — making data reliable across teams, enabling safe democratization, supporting compliance, and ensuring AI initiatives stand on solid ground. Without governance, the challenges organizations face with analytics don’t disappear — they only amplify with AI.

The sessions at Foundations offered practical insights into how organizations can begin approaching this challenge, laying down the governance frameworks needed to enable trustworthy analytics and responsible AI.

From bottlenecks to real-time: The power of connectivity and integration

If governance provides trust, integration provides speed. And it was one of the most consistent themes across customer stories. Integration not only eliminates delays but also creates the connected, timely data environment that AI depends on. Without it, AI faces two critical risks: incomplete data, when not all relevant sources are integrated, and outdated data, when integration processes take too long to deliver information into AI tools. In both cases, the result is the same: AI making predictions without the full or current context.

Customers at Foundations illustrated how they are tackling integration challenges, from incomplete data trapped in silos to delays that leave information outdated by the time it reaches users or AI tools. Without strong integration foundations and the right approach in place, these problems only multiply once AI is in use. That’s why many organizations emphasized integration as a core foundation for both analytics and AI, ensuring that insights and models alike are built on complete and timely data.

At the operational level, being able to connect key financials in Sage Intacct and customer data in Salesforce eliminated bottlenecks and made real-time financial reporting possible. Marcelo Lavanhini, CFO at RLTYco, described how automation reshaped processes:

"I'm not spending my time setting up and refreshing data anymore...I just run the queries and update everything because it’s set up with CData. Either for Power BI or for my management deck through the Excel add-in, it doesn’t matter. Everything’s set up."

The impact was immediate. Seamless integration is transformative not just for real-time dashboards and insights, but emerging AI use cases as well. In the same session, Benjamin Lehrer, CEO at First Water Finance, explained:

“One of the ways that we're using the automation and speed around AI enablement and output is to unearth those things to drive better dialogue faster within the organizations.”

And Ittichai Chammavanijakul, Senior Digital Automation Manager at Zebra Technologies, highlighted the role of connectivity:

“The Salesforce ODBC driver from CData really served as a foundation enabler for us to do automation. It gave us secure and high-performance two-way connectivity to Salesforce and other systems.”

The stories at Foundations showed that with CData connectivity and integration in place, teams no longer wait on data requests from IT or partner teams. Instead, they can access complete and timely data directly in the tools they already use, whether AI assistants like Claude and ChatGPT or analytics dashboards in Power BI. The result is faster decisions and more trusted outcomes, supported by data that is immediately at hand.

Driving AI and analytics through self-service

Customers at Foundations showed that one of the biggest shifts happening today is the move to self-service. Instead of waiting for IT or data teams, business users increasingly expect to access, explore, and act on data themselves directly in the tools they use every day. This change is what makes analytics and AI practical at scale, because it brings insights into the flow of work.

At First Water Finance, Benjamin Lehrer explained why connectivity was so important:

“It’s one of the reasons that we turned to CData — because we step into a variety of system environments, varying degrees of data maturity, and we saw a flexible solution that would have the connectivity into the systems we needed.”

That connectivity gave finance teams the freedom to work independently, modeling growth and financing outcomes without waiting for IT. Lehrer highlighted the difference when data is available directly in dashboards:

“Having not just Power BI, but really well-connected dashboards and reports to your real-time sources of truth in Sage Intacct and Salesforce, that’s a really powerful thing.”

Elli.ai focused on making AI usable by non-technical business users. Their platform enriches and simplifies metadata so teams can ask natural questions and receive immediate answers. Underpinning this capability is CData connectivity, which ensures the AI assistant can access the full, trusted data it needs to respond with confidence.

Red Wing Shoes demonstrated the same principle on the operations side. As Buck Debnam explained, fragmented point-of-sale, ERP, and cloud systems had left managers without a complete view:

“Once I got CData in place, we could have an analyst manage and set up the data flows that didn't require them waiting on me to write code or a developer to write an SSIS package to move data from one system to another.”

He noted that this foundation now powers dashboards that put insights directly in the hands of managers, enabling them to act in real time rather than wait for assembled reports.

The consistent message: self-service is no longer optional. Empowering people to use analytics and AI directly, from running growth scenarios in finance, to querying AI assistants in plain English, to accessing live dashboards in operations is what turns data into confident decisions. With trusted and connected data as the foundation, organizations can scale AI and analytics together, making insights available in the flow of everyday work.

Foundations for the future

Across both AI and analytics sessions, customers and partners underscored the same reality: success with AI and analytics depends on strong foundations. Governance ensures trust, integration delivers completeness and timeliness, and self-service empowers teams to put data to work where it matters most.

With AI, these foundations matter more than ever. The power of AI is to amplify and accelerate. But that means it multiplies risks when governance is weak, magnifies gaps when integration is incomplete, and exposes delays when users cannot access data themselves. At the same time, when the foundations are strong, AI amplifies the benefits: trusted decisions, connected insights, and empowered teams moving at speed.

What stood out at Foundations was not theory but practice. Organizations openly sharing how they are already addressing these challenges and enabling new opportunities. These stories proved that building the right foundations is not only the prerequisite for AI adoption; it is how companies are already competing and growing today.