Books & essays on the meaning behind your data.
From Business Rule Solutions — on how organizations think, decide, and make their knowledge work, in the age of AI.
The Ontology Pipeline is Broken: Why Your Data Strategy is Waiting Too Late to Talk to the Business
The so-called ‘ontology pipeline’ is seductive, organized – and fundamentally wrong.
Accurate Data. Wrong Decision. What’s Missing?
Knowing your customer data isn’t the same as knowing how your business works.
Data is Not Language: W3C/RDF vs. SBVR
Adding a shape to a linguistic problem is like trying to fix a misunderstanding in a conversation by writing a stricter dress code for the speakers.
The Truth Bridge: W3C/RDF vs. SBVR
The W3C/RDF stack jumps straight from a triple to an assumed truth.
![Side-by-side panels: in RDF / OWL, "knows" links Alice to Bob and Organization A to Organization B alike; in an SBVR verb concept, "knows" is bound to [Person] and [Person]](https://assets.brspublications.com/articles/verb-concepts-w3c-rdf-vs-sbvr/banner.webp)
Verb Concepts: W3C/RDF vs. SBVR
True interoperability comes from knowing which things can – and more importantly cannot – relate.
Business Rules vs. Requirements
The business rules exist independently of the solution we choose to implement.
How the W3C/RDF vs. SBVR Distinctions Apply to LLMs and Prompt Engineering
LLMs are fundamentally linguistic and conceptual engines – they naturally speak the structured natural language of SBVR, not the flat math of RDF.
Human-Centered Enterprise Semantics
The comparison between the W3C stack and SBVR is not a battle of file formats; it is a fundamental choice between data-centric linking and human-centered semantics.
Is AI Like Knitting?
AI is a tool. Like a knitting machine, it can only produce from the pattern it's given — and the quality still depends entirely on the inputs.
BRSdata Capability Matrix™
A capability map for understanding what data teams should be responsible and accountable for across the organization.
The Data Organization Spectrum
A framework for deciding whether data work should be centralized, democratized, federated, or somewhere in between.
What's Your Data Culture Plan for 2023?
A month-by-month plan for improving data trust, data communications, and senior-leadership support.
Your Next Data Skill is Stakeholder Empathy
Why data professionals need to understand stakeholder workflows, goals, questions, and concerns.
Data Culture Issues and How to Fix Them
How fear of data, inconsistent vocabulary, and mistrust undermine data culture, plus ways to address them.
Understanding Data In Your Organization
A wholistic view of the data ecosystem across quality, freshness, culture, and outcomes.
Current State Analysis of Your Data – Part 2 – Data Freshness
A stakeholder-oriented guide to understanding whether data is available when people need it.
Current State Analysis of Your Data – Part 3 – Data Culture
A practical current-state lens for understanding the human side of data management.
Current State Analysis of Your Data – Part 1
A starting point for understanding data quality, data freshness, data culture, and data-driven outcomes.
The Four BRS Dimensions of Semantic Quality: A Business View of Data and Data Quality
A business perspective on why data quality problems are really problems of meaning, context, and communication.