We write for CTOs and platform owners stuck in tech debt, tangled architectures, and AI hype fatigue.
From data product lifecycle to metadata-as-code, these aren’t trends — they’re battle-tested modernization principles.
👉 Ideal for leaders tired of duct-taping tools and chasing failed paradigms.
My Journey to Free Data at DevOps Speed By Brian Brewer | Published July 25, 2025, 09:00 AM EDT As of 08:34 PM EDT on Thursday, July 24, 2025, I look back on a career that’s defined both my journey and InfoLibrarian™’s future. For over two decades, I’ve consulted with SMBs and enterprises, driving principal architecture and CTO-level strategies through consulting firms and direct hands-on client engagements. I led the charge on successes you’ll find in our Customer Success Stories—from streamlining metadata for a global bank to modernizing data governance for a healthcare leader.
Unlocking AI Success: The Metadata Advantage By Brian Brewer | Published August 1, 2025, 09:00 AM EDT As of 12:03 PM EDT on Friday, July 25, 2025, the AI revolution is at a crossroads. With enterprises racing to adopt artificial intelligence, a stark reality emerges: Gartner (2025) predicts 90% of AI projects will fail by 2026. Having consulted with SMBs and enterprises for over two decades, driving hands-on principal architecture and CTO-level strategies, I’ve seen this failure rate firsthand.
The Data Product Lifecycle As of 08:03 PM EDT on Thursday, July 17, 2025
The buzz around data products is reshaping how organizations leverage data. For VPs of Data and CTOs, understanding this concept is key to driving strategic value. This article breaks down the definition, explains the accompanying diagram, explores implementation architectures, and discusses the role of open-source frameworks in building robust data solutions.
What Are Data Products? Data products are curated, reusable data assets—such as datasets, dashboards, or machine learning models—bundled with metadata (e.
Redefining Data Stewardship: How Computational Governance Aligns with DataOps and Modern Architecture In the fast-paced world of modern data systems—data lakes, real-time AI, and DataOps—traditional Data Governance is undergoing a radical transformation. The rise of computational data governance marks a shift from manual, rigid policies to automated, real-time, and agile frameworks that align seamlessly with DataOps, CI/CD, and the data product lifecycle. By integrating test-driven development (TDD), shift-left testing, and data contracts, computational governance empowers organizations to manage data products effectively while fostering robust data stewardship.
Article: Navigating the Paradigm Shift: Modernizing Data Management for AI-Driven Enterprises The IT landscape is undergoing a seismic shift. Traditional data management systems—rooted in the principles of the Data Management Association (DAMA) and its Data Management Body of Knowledge (DMBOK)—have long served as the backbone for business intelligence (BI) and operational workflows. However, the rise of AI/ML-driven applications demands a new approach: context engineering. This emerging discipline goes beyond static data pipelines, enabling dynamic, real-time, and scalable AI systems.
From Chaos to Contracts: How Metadata Powers the Future of Trusted Data Products The era of treating data like a byproduct is over. In modern, AI-enabled enterprises, data must be managed as a product—complete with ownership, SLAs, quality enforcement, and accountability. But this shift doesn’t happen by accident. It requires metadata-driven governance at every stage of the data lifecycle.
At InfoLibrarian™, we believe the foundation of this transformation is metadata-as-code—and specifically, data contracts that act as enforceable agreements between producers and consumers.
Why Data Governance Fails 80% of the Time—and How Modern Architecture Can Fix It Dear CTOs and VPs, if you’ve ever felt trapped by the promise of data governance only to see initiatives falter, you’re not alone. Industry research confirms a staggering 80% of data governance efforts fail, a statistic that demands we rethink our approach (Gartner, 2024). Gartner identifies the core issue as “lack of a real or manufactured crisis” and failure to “enable prioritized business outcomes”—but this stems from a deeper architectural problem: trying to bolt governance onto technical debt instead of modernizing the underlying systems.
