Why Your Data Is Failing You, and the Architecture That Finally Fixes It

Between Product and Partnerships

In this episode of Between Product and Partnerships, Cristina Flaschen sits down with Michael Kowalchik, Founder and CEO of Matterbeam, to unpack why so many companies still feel stuck when working with their data. Drawing on years of experience in machine learning and large-scale product architecture, Michael shares what he learned at Pluralsight when he was asked to help overhaul the company’s data systems during a period of rapid growth.

Cristina and Michael explore why traditional approaches often fall short once a business reaches a certain level of complexity. Pipelines designed for one purpose end up stretched far beyond their limits, and central repositories rarely reflect the day-to-day realities of how different teams actually operate. Michael explains how a shift toward immutable logs and replayable data streams created a more predictable, trustworthy foundation inside Pluralsight, and how that experience eventually led to the formation of Matterbeam.

They also examine the current wave of AI adoption and discuss why many organizations assume AI can cover foundational issues that still need real architectural attention. Michael describes the importance of building systems that make it possible to trace what happened, reproduce it, and understand how data moves through each step. He and Cristina talk about how this clarity becomes even more important as more software depends on automated decision making.

If you’ve struggled with disconnected systems, slow access to information, or constant rework caused by shifting definitions, this conversation offers a grounded perspective on how to rebuild your data layer in a way that actually supports the work your teams are trying to do.

For more insights on partnerships, ecosystems, and integrations, visit www.pandium.com
To learn more about Matterbeam, visit www.matterbeam.com

To sign up for Matterbeam's upcoming webinar, visit www.matterbeam.com/webinar