[OC] I visualized historical production data of all major global mining companies Statistics

What This Visualization Shows

This data visualization displays "[OC] I visualized historical production data of all major global mining companies" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on Live app: [https://world-mining-monitor.vercel.app/](https://world-mining-monitor.vercel.app/)

GitHub: [https://github.com/kadoa-org/world-mining-monitor](https://github.com/kadoa-org/world-mining-monitor)

The Grasberg copper data I'm showing in the demo is an interesting example since it experienced a Q4 2025 drop of 84% QoQ after a mud rush incident.

The hard part is normalization since every region and company reports differently, and even for SEC filings, the production data is usually in the unstructured management discussion sections.

Traditionally it was very hard to get global coverage on data like this, and most large data providers still do it with a lot of human labor, but I think AI is getting to a stage where data sourcing tasks like these can be done efficiently and accurately at scale.

The main challenges are:

* Different units across reports like copper in kt, million pounds, or wet metric tonnes * Fiscal years don't align * Product naming is inconsistent (e.g. "copper concentrate" vs "cu conc") * Some report on a payable basis, others contained metal, others equity-adjusted

I used LLMs to deterministically generate extraction, transformation, and validation ETL code for each company. If a source changes or data issues appear, the system can automatically adjust the code. It's far from perfect, but it validated my hypothesis that we can now do a lot more with a lot less when it comes to data like this.

**What's next:**

* Historical backfill: This dataset currently covers 1-2 years for most companies * Continuous real-time updates as new quarterly reports come out * Expand company coverage * Expand dataset with more KPIs * Open source the extraction pipelines as well

Let me know if you find any bugs or have any feedback/suggestions :), which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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