Blog
Insights and perspectives
Expert analysis, practical guides, and thought leadership on enterprise master data management.
Sensor data, transaction data, specialist applications, how the parts relate, and a practical guide to who benefits and where to start...
Quality identifiers, data governance, and how organizations measure and mature their data quality management processes...
What the series is, how ISO document types work, and the foundational and master data exchange parts...
AI is only as trustworthy as the data behind it. Discover why grounding large language models requires more than retrieval, and how master data management, shared definitions and semantic governance...
Why spreadsheet-based SPIRs fail — and how K:spir replaces them with continuous, governed data quality...
When the same product goes by different names across your systems, suppliers, and catalogues, data quality breaks down. There's an international standard designed to solve exactly this problem...
If your product data sits in spreadsheets with inconsistent categories, custom codes no one remembers creating, and no clear path to a searchable catalogue — here's where to start...
Trusted data does not have to be invented from scratch.
Standards like ISO 8000 already provide a practical framework for defining and measuring quality data — but many organisations still build...
Bad data keeps reappearing because the process around the system is broken.
When teams rely on spreadsheets, extracts, and hidden workarounds, the source system stops being the real system of record...
Poor data quality is not primarily a technology problem.
Systems matter, but they cannot create ownership. Until data quality is treated as a leadership metric, organisations will keep fixing symptoms...
Who owns trusted data?
That question sounds simple, but in many organisations the answer is still vague. Everyone depends on data quality, yet few people are formally accountable for it — and that gap...