Guest: Gavin Goodland, Chief Data Officer at National Grid

AI is creating extraordinary opportunities for businesses, but it is also creating an enormous new demand for energy.

In this episode of Databricks Diaries, Andy Davis sits down with Gavin Goodland, Chief Data Officer at National Grid, to explore the relationship between AI, data centres and the infrastructure required to power an increasingly digital economy.

As hyperscale data centres add significant new loads to the electricity network, National Grid is simultaneously using AI, real-time data and advanced optimisation to help manage a grid that is becoming more complex, distributed and dynamic.

Gavin explains how AI is being applied across National Grid — from predicting equipment performance and optimising network capacity to physical AI, drones, workforce training and intelligently managing demand from data centres.

The conversation also goes beyond individual use cases into a bigger question facing every enterprise: how do you turn rapid AI innovation into sustainable business value?

They discuss:

• Why AI and data-centre growth are becoming a major consideration for the electricity grid
• How AI can help balance demand, generation and network capacity
• Using real-time operational data to get more value from existing infrastructure
• Physical AI, robotics and drones across critical infrastructure
• Why National Grid starts with the business problem rather than the technology
• The importance of keeping the “human in the lead”
• Moving employees from curious, to comfortable, to confident with AI
• Where organisations should build AI themselves versus buy from technology providers
• Why legacy technology shouldn’t prevent organisations from innovating
• The emerging importance of an AI control plane for integration, observability, lineage and governance
• Managing the rapidly changing economics of AI and consumption-based technology
• Why Gavin believes organisations should establish an AI Value Office to evaluate value and risk consistently

A fascinating look at AI from an organisation sitting at the intersection of two of the biggest technology and infrastructure shifts happening today: the growth of AI and the transformation of the energy system.

Is AI making technical skills less important?

Or is it actually making experience, judgement and technical understanding more valuable?

In this episode of Databricks Diaries, Dan Thornton is joined by Sham and his son Milo for a conversation about AI, data engineering, careers and what companies should really be looking for when hiring data talent.

They get into:

• What being “AI-ready” actually means

• Why strong data foundations matter before AI can give you useful answers

• How AI helped turn work that could have taken 2–3 months into a couple of weeks

• Why you still need to understand and review AI-generated work

• How AI is changing the day-to-day role of data professionals

• What Milo looks for when hiring data analysts

• Why curiosity and problem-solving matter when assessing candidates

• What makes a strong modern data engineer

• Whether companies should prioritise experienced AI-savvy engineers over junior “vibe coders”

There’s also a more personal side to the conversation, with Sham and Milo talking about their different routes into technology, career advice and what they’ve learnt from each other.

The big question:

Has AI actually made technical experience less valuable — or more valuable?

What do you think?

Connect with Dan Thornton and Primus Connect for specialist Databricks recruitment.

#Databricks #AI #DataEngineering #DataAnalytics

AI experimentation is easy. Turning those experiments into something that delivers genuine business value is much harder.

In this episode of The Databricks Diaries, Daniel Thornton speaks with Natalia about the realities of taking AI from experimentation into production.

Natalia has spent around a decade working across data and AI, including building AI capability from scratch in a regulated banking environment. She shares practical lessons on what organisations need to get right before they can start delivering real AI impact.

We discuss:

✔️ Why organisations need to start with the business problem, not the technology

✔️ Building the right platform and environments for AI

✔️ How to prioritise AI use cases

✔️ Why early wins can help build confidence and secure further investment

✔️ The importance of measuring real business impact

✔️ Where organisations are currently seeing value from AI

✔️ What the future of AI and self-service could look like

One of the biggest takeaways: don't ask what you can do with AI. Start by asking what problem is worth solving.

Connect with Daniel on LinkedIn:

https://www.linkedin.com/in/databricksdan/

Learn more about Primus Connect:

https://www.primus-connect.com/

#Databricks #AI #ArtificialIntelligence #DataEngineering #DataLeadership