Modernize with Confidence (EMEA Broadcast)

How Pitney Bowes built a governed data foundation to get ready for the AI future

Pitney Bowes, a more than 100-year-old shipping and mailing technology company, runs on data pulled from 25+ systems like Salesforce, SAP ECC, MongoDB, Oracle, and more. A few years ago, their Big Data team was drowning in hand-coded ETL scripts that couldn’t keep up with new cloud apps. Today that same data flows through governed pipelines into a data lake and Snowflake, feeding 250+ business users across Sales, Marketing, Finance, and Shipping & Mailing. This is the kind of infrastructure most companies talk about wanting and rarely finish building.

That’s the foundation Pitney Bowes has spent years getting right. Now it’s the foundation their next move depends on: giving AI systems governed, secure access to that same data without reopening every question about risk, cost, and control they thought they’d already answered.

Join Vishal Shah, Lead Data Architect at Pitney Bowes, as he walks through how his team modernized its data infrastructure at enterprise scale, the architecture decisions, the governance model, and the lessons that came from doing it the hard way first. Then we’ll show how SnapLogic’s newest capabilities extend that same governed foundation for AI, including SnapCode, an AI-powered coding experience that helps teams build integrations faster, and Enterprise MCP, which gives AI agents secure, governed access to enterprise systems and tools.

Join us to learn how to:

• See what a real modernized data foundation looks like: Not theory but an actual enterprise data architecture built to scale, and what it took to get there.
• Get governance and security right before AI needs them: Access control, authentication, and visibility that hold up whether it’s a person or an AI agent touching the data.
• Turn a modernized foundation into AI-readiness: How SnapCode and MCP Server let teams extend existing governed infrastructure to AI agents without starting over.