17:00 - 18:00
Danny Kruge
Data Platform MVP | Mission Critical Engineer from Schuberg Philis
Danny Kruge is a Microsoft Data Platform MVP, Mission Critical Engineer at Schuberg Philis, and founder of Azure Heroes. With 16+ years of experience, he is a technology leader and hands-on engineer specialising in designing, building, and operating data and cloud platforms at scale.
His expertise spans SQL Server, Azure Data Factory, Microsoft Fabric, cloud architecture, platform engineering, and
mission-critical environments, including OLTP and OLAP databases exceeding 3 TB. Throughout his career Danny has
grown from database administration into broader technical leadership, taking ownership of complex platforms and
engineering initiatives across SQL Server, Azure, and AWS.
Danny focuses on defining technical direction, simplifying complexity, and turning challenging problems into reliable,
scalable solutions. He takes a pragmatic approach and stays focused on measurable outcomes, whether working on large
database platforms, cloud architecture, automation strategy, or organisational transformation.
Originally from Leicester, England and now based in the Netherlands, Danny founded Azure Heroes to bring the Microsoft
Azure community together. He regularly contributes through speaking, articles, interviews, and events.
How Not to Build a Data Platform: Lessons from Failure to Success
Building a data platform is not just about technology. It’s about adoption.
In this session I’ll share the hard-learned lessons from a data platform initiative that failed because we never engaged the business end-users it was built for. The architecture was sound and the pipelines ran, but nobody came.
You’ll learn why selling the platform’s value to stakeholders is as critical as the engineering. You’ll also see how aligning with real business needs turned that failure into a success.
You’ll leave with practical strategies for making your data platform a cornerstone of business decision-making instead of a forgotten project. These are the foundations that make later work, such as AI on top of your data, actually pay off.