Why Strategic Leaders are Pivoting to Contextual Governance

For decades, data governance has been treated as a static discipline with a set of rigid policies laid out in formal frameworks and applied uniformly across the enterprise. But in an era defined by decentralized architectures and the breakneck speed of AI adoption, this one-size-fits-all approach is inefficient and has the potential to increase business risk.

The mismatch between static governance and dynamic data estates is the primary reason why many digital transformation projects stall. It is time to move toward Contextual Governance.

The Governance Friction Paradox

Traditional governance models fail because they are binary. They treat data as a fixed asset rather than a fluid utility. This creates a paradox:

• Over-governance: Smothering low-risk innovation with unnecessary red tape.

• Under-governance: Missing the subtle, high-risk nuances of how data is actually used in the wild.

Static rules rely on metadata labels that are often outdated the moment they are applied. Contextual governance, however, shifts the focus from what the data is to how the data is behaving.

What is Contextual Governance?

Contextual governance is a move from policing to orchestration. It is an adaptive framework that evaluates risk in real-time based on the intersection of three pillars:

1. The Actor: Who is accessing the data, and what is their historical behaviour?

2. The Environment: Where is the data flowing? Is it a sandboxed R&D environment or a customer-facing LLM?

3. The Intent: Is the data being used for a routine report, or is it being fed into a model that could leak proprietary logic?

The Strategic Shift: We are moving from asking, Is this data protected? to asking, Is this data protected enough for this specific moment?

Beyond Compliance: The Competitive Edge

For the C-suite and strategic leads, contextual governance isn't just a compliance checkbox. It is a performance multiplier.

• Agility at Scale: By automating the easy permissions and tightening controls risk is reduced and  the bottlenecks a removed that frustrate engineering and data science teams.

• AI Readiness: AI systems don't live in a vacuum. A model that is safe in a localized test may become dangerous when exposed to real-world edge cases. Contextual governance provides the guardrails necessary to deploy AI with confidence.

• Intelligent Foundations: This shift forces a higher standard for metadata and lineage. You are mapping data and mapping the value stream of the entire organization.

The Path Forward

Transitioning to this model requires more than new software; it requires a cultural pivot. We must change how we view governance from firm control to see it as a central intelligent system of the enterprise.

The future of data doesn't belong to those with the thickest rulebooks. It belongs to those who can govern at the speed of business.

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.

AI is Making us Dumber Highlights the need to Fix Our Data Foundations

There’s a growing irony in the AI boom, the more we automate, the less we seem to understand. People are outsourcing judgement to models they barely comprehend, and organisations are making decisions based on outputs they can’t trace. It’s not that AI is inherently dangerous,  it is that our data foundations are often too weak to support the weight we are placing on them. When the underlying data is inconsistent, undocumented, or poorly governed, AI becomes a mirror reflecting our own gaps back at us.

The problem isn’t the technology; it’s the dependency. When teams rely on AI to summarise, interpret, or decide, they lose the ability to interrogate the underlying data. This erosion of understanding is subtle but profound. It creates a culture where speed is valued over clarity, and convenience over accountability. That’s when AI stops being a tool and starts becoming a potential issue.

The antidote is governance. Strong lineage, quality controls, and stewardship ensure that AI systems are built on solid ground. Governance doesn’t slow innovation, it stabilises it. It gives organisations the confidence to adopt AI without sacrificing transparency or control.

If we want AI to augment rather than erode our intelligence, we must invest in the foundations. AI should elevate human capability, not replace it. And that begins with knowing our data.

A couple of the ever increasing articles on the subject.

Is AI Dulling our Minds

Will AI make you stupid?

Data Culture

Data culture is a term that has been talked about over many years. It is about using data to drive an organizations decisions.  Mckinsey state there are seven principles that underpin a healthy data culture

• data culture is a decision culture
• data culture is a C-Suite imperative, and that of the board
• the democratization of data
• data culture puts risk at its core
• culture catalysts with people bridging data science and on the ground operations
• sharing data beyond company walls is shifting for in house competitive advantage to assembling the breath of best data assets in the market
• marrying talent and culture 

Alation have started doing quarterly State of Data Culture reports . The latest report is June 2021. Within that report they are sharing a Data Culture Index (DCI) which is a quantitative assessment of how well organisations are positioned to enable data driven decision making. The index they have is based on data search and discovery, data literacy and data governance. I do think that data culture is also about data ethics.

The report states the top initiative to foster data culture is managing data governance and improving that data quality. 

Data Skills Framework

The ODI have created a useful methodology for looking at data skills. It classifies the data landscape skills into technical skills, and other skills such as service design, data innovation and change leadership which are important for organisations to succeed.  I would recommend looking at the resource page as data literacy is most important to address and this framework helps look holistically at data skilling.