Insights & Resources
Guides, best practices, and thought leadership on data governance and AI compliance.
Why Data Governance Matters More Than Ever
With the EU AI Act, evolving privacy regulations, and the explosion of AI adoption, data governance is no longer optional. Here's why every data team needs a strategy.
Read moreIntroducing Qarion: Lightweight AI & Data Governance
Meet Qarion — a modern platform that makes data governance accessible to organizations of all sizes. Learn about our approach to cataloging, quality monitoring, and compliance.
Read moreThe Composable CDP: An Emerging Architecture
How the composable CDP architecture decouples storage from orchestration — letting your data warehouse serve as the single source of truth while the CDP handles identity, segmentation, and activation.
Read moreDissecting a CDP's Segmentation Engine
A deep dive into how modern Customer Data Platforms build segmentation engines — from recursive filter DSLs and SQL compilation to warehouse-native execution.
Read moreWhat Features Make for a Great CDP?
An in-depth analysis of the features that distinguish best-in-class Customer Data Platforms — from identity resolution and segmentation to real-time activation and privacy compliance.
Read moreCDP: When and When Not to Purchase an Off-the-Shelf Solution
A practical framework for deciding whether to buy a packaged CDP or build one in-house — covering cost, time-to-value, data sovereignty, and long-term flexibility.
Read moreStrategies to Effectively Propagate Master Data Merges
Practical strategies for propagating master data merge operations across downstream systems — covering event-driven architectures, CDC pipelines, and reconciliation patterns.
Read moreMDM: How to Match and Merge Records to Unify Your Data
A comprehensive guide to record matching and merging strategies for Master Data Management — covering deterministic rules, probabilistic scoring, and merge conflict resolution.
Read moreData Management, Quality, and Governance
An overview of the interconnected disciplines of data management, data quality, and data governance — and how they form the foundation for trustworthy, compliant data operations.
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