Inspirational STEM 1958-1968

From the moment I first understood the meaning of my mum’s , Joan Holt, school motto “Be strong and very courageous” I realised it wasn’t just a phrase she carried; it was a quiet force that shaped her life. Long before women in STEM were recognised or encouraged in the way they are today, she worked in a world of theoretical physics, numerical analysis, and early computing with a determination that still leaves me in awe. Hers was not the loud, celebrated courage of someone who set out to break barriers, but the steady, purposeful courage of someone who simply refused to accept that those barriers applied to her. With it being International Women’s Day last week we celebrated the women who paved the way and that reminds that one of those pioneers was my mum.

Her career reads like a living history of British computing. Her first days were working on IBM mainframes and analysing data, when computers filled whole rooms and printouts were the size of phone books to programming the Ferranti Mk1. She drafted manuals for the Elliott 503 and 4100, and solved problems with nothing but symbolic assembly code. She lived through the evolution of technology as few people did. She worked in rooms where magnetic tapes towered over her, where data meant punched cards and where a single mistake meant repunching a deck. She navigated machines that shook themselves off desks, deciphered the results of calculations that once took over 8 months, and wrote documentation that bridged engineers and the future operators. She was often the only woman in the room, one of only a handful among thousands of men at just nineteen. She simply worked hard proving herself indispensable through intelligence, persistence, and grace.

Today, on Mother’s Day, I think not only of the extraordinary work she did, but of the extraordinary woman she was. A role model who taught me that courage can be quiet, curiosity can be powerful, and that you can shape the world even if you never stand in the spotlight. While the world now celebrates women in STEM more visibly than ever, she lived those values when the path was far tougher and the recognition far thinner. Her achievements may sit in old manuals, early programs, and memories of rooms filled with tapes and valves, but her legacy is alive in me. I am proud beyond words to have been her daughter, and prouder still to share her personal story to help inspire future generations.

Fabric, Purview, and the New Shape of Enterprise Data Architecture

Fabric has reshaped the Microsoft data landscape by unifying analytics, engineering, and storage into a single experience. But unification alone does not create coherence. The real transformation happens when Fabric is paired with Purview. Together, they form an architecture where data movement, governance, and analytics operate as one system rather than disconnected components.

This convergence matters because modern data estates are too complex to govern manually. Data flows across pipelines, notebooks, semantic models, and AI workloads. Without integrated governance, organisations end up with pockets of visibility rather than a complete picture. Purview provides the lineage, classification, and policy enforcement that Fabric alone cannot deliver.

One of the most powerful aspects of this integration is the alignment between data products and governance. Fabric encourages teams to think in terms of products that are curated, reusable assets with clear ownership. Purview reinforces this by providing the metadata, stewardship, and controls that make data products trustworthy. Governance becomes part of the product lifecycle, not an afterthought.

This new architecture also supports hybrid and multi‑cloud realities. Many organisations are not all using Fabric, nor should they be. Purview’s ability to govern across environments ensures that Fabric becomes a strategic hub rather than a silo. The result is an architecture that is unified, not monolithic but flexible.

As organisations modernise their estates, the combination of Fabric and Purview will become the default pattern. It is not just a technical alignment but it is a governance first architecture for the AI era.

It is FABCON and SQLCON in Atlanta March 16 - 20, 2026. 

Purview and OneLake Govern tab change

The Purview Hub in Fabric insights have now moved to the OneLake catalog’s Govern tab. The change helps bring governance closer to where the data actually lives, rather than leaving them in a parallel experience that always that wasn't as helpful as it could have been. In the Govern tab, you now see the same posture summaries, recommended actions, and learning resources that were in Purview Hub, but framed within Fabric’s unified governance model. It is a cleaner, more coherent way of surfacing what core information about the health of your data estate.

Functionally, the Govern tab now gives you a consolidated view of governance status, recommended actions, sensitivity and endorsement insights, and links into deeper governance tooling. You can drill into items that need attention, track improvements over time, and understand how your organisation is using Fabric’s governance features. The experience also ties directly into the OneLake catalog, so governance isn’t an afterthought. It is embedded in the same place you explore, classify, and manage data assets.

Microsoft hasn’t yet published a formal retirement date for the Purview Hub. Fabric is now  presenting a single, coherent story about how organisations should understand and manage their data estate.

You can learn more about it here.

The Rise of Contextual Governance: Moving Beyond Static Rules

We have reached a point where models can learn faster than organisations can adapt. This creates a dangerous asymmetry: the technology evolves, but the governance, culture, and literacy lag behind. The result is a widening gap between capability and control. Organisations deploy increasingly powerful models without fully understanding their behaviour, limitations, or risks.

This gap is not caused by technology but by it’s caused by organisational inertia. Many teams still rely on outdated governance processes that cannot keep pace with continuous learning systems. Policies are static, reviews are infrequent, and oversight is reactive. Meanwhile, models evolve with every new dataset, every retraining cycle, and every shift in user behaviour.

The solution is not to slow the models but to accelerate organisational learning. Governance must become continuous, adaptive, and embedded into operational workflows. This means real‑time monitoring, dynamic policies, and stewardship that evolves alongside the data. It also means investing in literacy so that teams understand not just how to use AI, but how to question it.

When organisations learn as fast as their models, AI becomes a strategic advantage. When they don’t, AI becomes a liability. The choice on how to move forward is not technological, it is cultural.

AI Is Making Us Dumber, Part III: When Models Learn Faster Than Organisations Do

We have reached a point where models can learn faster than organisations can adapt. This creates a dangerous asymmetry: the technology evolves, but the governance, culture, and literacy lag behind. The result is a widening gap between capability and control. Organisations deploy increasingly powerful models without fully understanding their behaviour, limitations, or risks.

This gap is not caused by technology, it is caused by organisational inertia. Many teams still rely on outdated governance processes that cannot keep pace with continuous learning systems. Policies are static, reviews are infrequent, and oversight is reactive. Meanwhile, models evolve with every new dataset, every retraining cycle, and every shift in user behaviour.

The solution is not to slow the models  but it is to accelerate organisational learning. Governance must become continuous, adaptive, and embedded into operational workflows. This means real‑time monitoring, dynamic policies, and stewardship that evolves alongside the data. It also means investing in literacy so that teams understand not just how to use AI, but how to question it.

When organisations learn as fast as their models, AI becomes a strategic advantage. When they don’t, AI becomes a liability. The choice is not technological, it is cultural.

I spoke on the topic at the  Data Toboggan Winter Edition in a session entitled 'Data Literacy: The Human Advantage in an AI World.'

Data Toboggan Winter Edition 2026

It is that time of year again when Data Toboggan is running another 12 hour conference with 3 tracks with speakers from around the world. There are some amazing sessions to learn from. The conference is free to attend as usual. 

I am speaking on something of interest and topical in my lightning talk in The Chalet on Data Literacy: The Human Advantage in an AI World.

AI is accelerating decision‑making across organisations, but it’s also accelerating how quickly mistakes can scale. This session explores how data literacy keeps humans in the loop, prevents over‑reliance on AI, and strengthens judgment, context, and critical thinking. Attendees will see real examples of AI hallucinations, learn how provenance and triangulation protect against bad outputs, and understand why cognitive skills weaken when tasks are automated. They will leave with a practical checklist for questioning AI outputs, a clear view of the risks of low data literacy, and a framework for building teams that use AI responsibly, confidently, and intelligently.

We have our usual Piste Maps with the agenda.

World Economic Forum 2026 in Davos Global Council for Responsible AI

At the 56th World Economic Forum 2026 in Davos between 19–23 January 2026 , the Global Council for Responsible AI officially unveiled GRAICE™ (Global Responsible AI Compliance & Ethics). It is designed as humanity’s operating system for AI. Introduced to global leaders and policymakers, GRAICE moves Responsible AI from principle to practice, integrating ethics, governance, compliance, and human-centric design into a unified, scalable framework. 

The framework is an integrated system rather than a collection of policies that are simple and repeatable.

• Foundational values established non-negotiable ethical and human centred boundaries
• Seven pillars translate values into operational requirements
• Assurance tears verify that requirements are met with evidence
• Governance structures assign accountability and decision authority

 The six foundational grounded values are  

• Human dignity and autonomy
• Accountability and governance 
• Fairness and justice
• Transparency an explain ability
• Reliability and security
• Inclusivity and social benefits

And the seven pillars for responsible AI define what responsibly I must achieve in practise

• Ethical leadership
• purpose driven innovation
• Human centric use
• responsible implementation
• AI literacy and workforce readiness
• Data governance and integrity