Last week I wrote about how mining companies are sitting on transformational value trapped in their data systems, and the comments that followed were fascinating. Most people agreed with the opportunity. But a recurring theme emerged: "That's all well and good, but what about the quality of the data in the first place?" Fair point. Because if you're going to build an AI-powered intelligence layer on top of decades of mining data, you'd better hope that data isn't complete rubbish. Recent industry surveys paint a confronting picture. The vast majority of mining professionals say data management is critically important to their organisation. Yet less than a third have an established framework for managing it. Most keep data "organised in various systems", which is corporate speak for scattered across a dozen folders, three legacy databases, and someone's USB stick. The historical data problem is particularly challenging. More than half the industry identifies unmanaged historical data as a significant challenge, yet only half feel confident their company can actually handle it properly. When you've got an average of 22 people touching datasets within an organisation and most companies can't reliably tell you who changed what, when, or why, you've got a recipe for expensive mistakes. In mining, decisions based on flawed geological data can lead to drilling in the wrong locations, overestimating reserves, or underestimating processing costs. Boards should be asking hard questions about this. So is fixing it actually possible? Yes. But it requires something the industry has historically resisted: discipline. The solution is in establishing clear data governance from the point of collection. Every piece of data needs provenance: who collected it, when, using what methodology, and what QA/QC processes were applied. The technology to automate most of this now exists. The barrier is cultural, not technical. Consider the alternative. You spend millions on an AI platform, hire a data science team, and build predictive maintenance models, only to discover your insights are based on assay results that were transcribed incorrectly in 2009. The billions sitting in server rooms that I mentioned last week? They're only accessible if the data is trustworthy. That’s why we aim to built our approach around data integrity from the start. AI technology must validate, cross-references, and flag anomalies before decisions are made. Because the most sophisticated algorithm in the world is worthless if it's trained on low quality data. Cleaning historical data and establishing proper frameworks isn't glamorous. It won't make headlines at mining conferences. But it's the foundation upon which every other digital transformation initiative depends.