Mike Le Galloudec

Preaches the gospel for building a techno-future we believe in. Part time muppet, full time acronym – will be either writing code or on camera.

Post Common Data Challenges & How to Avoid Them

Common Data Challenges & How to Avoid Them

Data Decoded: Calling out some of the traps we see in data programmes. The last blog in my ‘data decoded’ series looks at the common issues we see when data platforms are built and automatic ROI is expected. Familiar Data Platform Challenges & Downfalls Department A builds a platform, Department B builds their own. Two…

Post The Four Pillars of a Data Platform

The Four Pillars of a Data Platform

Data Decoded: How to go from “we want to be data-driven” to “we are data-driven”. We hate to break it to you, but there isn’t any natural ROI in building a data platform. The ROI happens when decisions get made from that platform. As a data engineer or data platform consultant, you build around four…

Post Data Decoded 2025: How to Get the Most from Data Consulting

Data Decoded 2025: How to Get the Most from Data Consulting

How to see ROI, extract value from a data platform and get more from your data consultancy, as explored by our Principal Engineer Advocate, MLG, at Data Decoded 2025. In today’s world, you don’t run a business unless you make decisions. It doesn’t matter what industry you’re in – utilities, transportation, computing, retail – you…

Post What is Agentic AI?

What is Agentic AI?

Here at Oakland, we’ve been talking about our Intelligent Agents for quite some time. Now, it seems everyone from Gartner to Microsoft is with us, except we have a new name: agentic AI. And it’s Gartner’s top tech trend for 2025.  But what is agentic AI? In its simplest form, the term describes semi-autonomous machine…

Post Agentic AI and AI Agents: Shaping the Market in 2025

Agentic AI and AI Agents: Shaping the Market in 2025

What a difference a year makes! As 2024 comes to a close, and for everyone in the world of AI, it’s been a blast! It’s been a pivotal moment in the evolution of artificial intelligence, particularly generative AI. The year has been defined by a number of major milestones in deploying large language models (LLMs)…

Post Why We Recommend the Intelligent Agent Approach to Generative AI

Why We Recommend the Intelligent Agent Approach to Generative AI

Discover how Intelligent Agent AI can revolutionise your data management with Oakland. As generative  AI continues to advance at pace, more businesses are adopting it into their data management and day-to-day operations. Picking off-the-shelf tools for your AI is simple and straightforward. Still, these ready-made generative systems don’t work seamlessly with your business, thanks to…

Post Everything you need to know about Big Data & AI World

Everything you need to know about Big Data & AI World

Introduction  Last week, I had the opportunity to attend the highly anticipated Big Data and AI World conference, an all-encompassing event that showcased the breadth and depth of the data industry. From the infrastructure backbone of data centers to the cutting-edge applications in machine learning (ML) and large language models (LLMs), the conference offered a…

Get to know Mike

MLG is Principal Engineer Advocate at Oakland, dividing his time between engineering agentic AI solutions and translating them for non-technologists. He recently steered Network Rail’s first production-grade AI application, leveraging the Microsoft Azure stack and custom agent frameworks to turn raw data into real-time decisions. A former data scientist with a Physics degree from the University of Oxford – and a neural network tattoo to keep him honest – he still enjoys fine tuning predictive models at ridiculous hours.

Beyond delivery, MLG hosts Oakland’s YouTube channel and a fast-growing tech TikTok, distilling dense data topics into 90 second ‘aha’ moments for thousands of viewers. Whether advising rail, transport or utilities giants, he balances builder pragmatism with teach clarity, championing agentic artificial intelligence that makes enterprise datasets think for themselves.