Welcome

Passionately curious about Data, Databases and Systems Complexity. Data is ubiquitous, the database universe is dichotomous (structured and unstructured), expanding and complex. Find my Database Research at SQLToolkit.co.uk . Microsoft Data Platform MVP

"The important thing is not to stop questioning. Curiosity has its own reason for existing" Einstein



Friday, 9 October 2026

Apache Ossie (incubating) Could become important for AI and Analytics

Apache Ossie (incubating) is the universal standard for semantic data







Apache Ossie (incubating) is an industry-wide specification effort to standardize how we exchange semantic metadata across analytics, AI and BI platforms, providing a vendor neutral, single source of truth for semantic data. Ossie was previously known as Open Semantic Interchange (OSI). A semantic model is simply a shared definition of business concepts, metrics and relationships that helps people, reports and AI systems interpret data consistently.

As organisations invest in AI, they face a surprisingly familiar problem: different systems often define the same business concepts in different ways.

A metric such as revenue, customer count or profitability can be calculated differently across BI tools, data platforms and AI applications. The result is inconsistent reporting, conflicting answers and reduced trust in both data and AI.

This is the challenge that Apache Ossie (Incubating) is seeking to address. Apache Ossie is an open, vendor-neutral standard designed to allow organisations to define business metrics, dimensions and semantic models once and share them across multiple platforms. Rather than recreating business logic in every tool, the aim is to establish a common semantic language that can be understood consistently across analytics, BI and AI ecosystems. 

Why is this important? Because AI systems do not just need access to data. They need access to trusted business context. An AI assistant may know what data exists, but without clear definitions of what "customer", "revenue" or "active account" actually mean, its answers can still be misleading.

This is where Apache Ossie becomes particularly interesting. By providing a portable semantic model, organisations can help ensure that people, reports and AI agents are working from the same business definitions. The vision is simple: define business meaning once and use it everywhere. 

The initiative is also attracting significant industry support. Microsoft recently announced its commitment to Apache Ossie, describing semantic interoperability as an important foundation for AI-powered analytics and agentic experiences. The goal is to enable semantic definitions to move more easily between platforms while preserving governance and business meaning. Microsoft is working alongside Snowflake and other partners to support the development of the standard. 

For data governance professionals, Apache Ossie is worth watching. Governance has always been about creating trust in data. As organisations increasingly rely on AI, semantic consistency may become just as important as data quality, ownership and stewardship. Apache Ossie represents an attempt to create a common foundation for that trust across the modern data and AI landscape.

Reference 

https://ossie.apache.org/

Tuesday, 6 October 2026

Data Analytics Monthly Forum

Today i spoke at the 75th Data Analytics Monthly Forum with 2 of my collegues Kamila Spencer and Andrew Edge on Building the foundations for trusted AI. We explored the foundations of AI readiness - from trusted data and governance to security, risk and compliance.
 
DATE: Tuesday 6th October 2026 | 12:00pm – 1:00pm
 
We discussed how to create a secure, well-governed foundation for AI with Microsoft Purview. Here was the agenda:
  • Why AI success depends on trusted and high-quality data.
  • The growing importance of AI governance and emerging standards such as the EU AI Act.
  • How data quality impacts AI performance, reliability and outcomes.
  • How Microsoft Purview can help bring governance, security and compliance capabilities together.
  • Practical approaches to identifying and managing AI related risks.
The goal of the session was to help people leave with a clearer view of what your organisation needs to put in place before AI can deliver value safely and at scale.