---
title: Why Mines Still Struggle with Siloed Data and How to Fix It
description: Discover why siloed mining data persists, the cost of fragmentation, and a five-layer framework that turns disconnected data into real-time insight.
---

[The Signal by NTWIST | Blog on AI & Operational Excellence ](https://ntwist.com/blog)

# [Why Mines Still Struggle with Siloed Data and How to Fix It](https://ntwist.com/blog/why-mines-still-struggle-with-siloed-data)

 Written by [NTWIST](https://ntwist.com/blog/author/ntwist) | 1-Aug-2025 7:23:59 PM

## Why Mines Still Struggle with Siloed Data: How to Fix It

Mining companies generate petabytes of information from drill-hole assays to haul-truck telemetry yet production meetings still rely on spreadsheets and whiteboards. The root cause is **siloed mining data**: information locked inside incompatible systems, departments, and time zones. This article digs into *why* those silos persist, explains the technical traps behind them, and lays out a practical five-layer integration framework that any mine can start implementing.

For a related deep dive on data-heavy innovation, see our post [Digital Twins in Mining: Real ROI or Just Hype?](https://ntwist.com/blog/digital-twin-roi-mining).

### 1: What “Mining Data” Really Means

Before we talk solutions, clarify the four main data classes that flow through an operation:

| Data class | Examples | Typical refresh |
| --- | --- | --- |
| **Spatial / Geological** | Block model, blast pattern, dig lines | Weekly or monthly |
| **Time-series OT** | SCADA tags, crusher amperage, mill power | 100 ms to 1 s |
| **Event / Telemetry** | Haul-truck payloads, tyre-pressure alerts | Seconds |
| **Transactional / ERP** | Downtime codes, maintenance work orders, dispatch KPIs | Minutes to hours |

Silos arise when each class lives in a different software stack with no common ontology—that is, no shared naming, units, or timestamps **(International Mining, 2025)**.

### 2: Why Silos Survive

- **Legacy control systems.** Many concentrators still run 1990s DCS platforms. CSV exports are the only escape hatch, so data arrives late and without context.
- **Vendor lock-in.** Drill-and-blast software stores pattern geometry in proprietary databases, fleet-management vendors encrypt telemetry in the cab, and plant historians use closed schemas.
- **Organisational incentives.** Geologists measure reconciliation variance, not mill recovery; maintenance chases MTBF, not throughput. KPIs pull groups apart **(McKinsey & Company, 2024)**.
- **Air-gap security culture.** OT teams default to isolation to reduce cyber risk, but isolation freezes data at the edge.

### 3: Quantifying the Hidden Cost

McKinsey found that poor data integration can cut *ore-processing recovery by three to five percentage points* on sulphide circuits, worth about **180 million USD per year** at a 200 ktpa copper-equivalent mine **(McKinsey & Company, 2024)**. Fragmentation also fuels:

- fifteen percent more unplanned downtime when mobile-equipment logs never reach reliability engineers
- thousands of engineer hours lost to manual extract-transform-load work
- higher safety risk when alarms in PI tags are not correlated with slope-stability data

### 4: A Five-Layer Integration Framework

#### Layer 1: Contextual Tag Naming

Adopt ISA-95 or B2MML conventions so every tag carries `Site.Area.Unit.Service`. A truck-payload tag then becomes `NTWIST.NORTHPIT.FH400.PAYLOAD_T`. Consistent naming is the cheapest way to reduce future mapping effort.

#### Layer 2: Open Connectivity

Bridge legacy PLCs to OPC UA gateways.  
Stream high-frequency data via MQTT with Sparkplug B payloads for stateful telemetry.

#### Layer 3: Unified Data Lake or Mesh

Land raw OT and telemetry data in an edge buffer, then fan out to a cloud data lake. Use Delta Lake or Apache Iceberg format for ACID commits; mount spatial tables next to time-series data.

#### Layer 4: Real-Time Governance

Automate quality checks; range validation, rate-of-change alarms, and time-sync audits, so bad data never reaches dashboards.

#### Layer 5: Data Products and Shared KPIs

Create haulage-to-mill or ore-blend data products with owners, SLAs, and version control. Tie bonuses to blend compliance, not to silo-specific metrics.

### 5: Deep-Dive Example: Linking Drill Logs to Recovery

At one copper-gold operation, blast-hole assays sit in a geology SQL server while plant recovery lives in PI tags. By stitching the two streams through a shared block ID, the mine built a gradient-boosting model that predicts flotation recovery in real time. The pilot improved rougher recovery by **2.8 percentage points**, reduced reagent overuse by **eleven percent**, and paid back its cloud costs in six weeks.

### 6: Key Takeaways

- **Integration is an economic lever.** Frame every API or historian connector in dollars per tonne, not IT jargon.
- **Open standards age better than vendor feature sets.**
- **Data quality rules belong at ingestion.** Fixing data downstream costs ten times more.
- **People silos break when KPIs converge.** Shared recovery and cost metrics force collaboration.

### Conclusion: Turning Isolated Data into Integrated Insight

Digital winners in mining are not those with the fanciest AI models—they are the ones with clean, contextual, cross-domain data pipelines. Build the five layers, reward teams for shared KPIs, and the payoff shows up in tonnes, not slide decks.

[Explore Our Mining Data Solutions](https://ntwist.com/solutions/mining)

 References

International Mining. (2025). *Unlocking siloed data in mining operations*. Retrieved from [ https://im-mining.com/2025/02/07/unlocking-siloed-data-in-mining ](https://im-mining.com/2025/02/24/breaking-down-data-silos-the-path-to-mining-operational-excellence/)

McKinsey & Company. (2024). *Mine-to-market value chain: A hidden gem*. Retrieved from [ https://www.mckinsey.com/industries/metals-and-mining/our-insights/mine-to-market-value-chain-a-hidden-gem ](https://www.mckinsey.com/industries/metals-and-mining/our-insights/the-mine-to-market-value-chain-a-hidden-gem)

[View full post](https://ntwist.com/blog/why-mines-still-struggle-with-siloed-data)

```json
{
  "@context" : "http://schema.org",
  "@type" : "BlogPosting",
  "author" : {
    "@type" : "Person",
    "name" : "NTWIST"
  },
  "dateModified" : "2026-07-22T05:47:51.875Z",
  "datePublished" : "2025-08-01T19:23:59Z",
  "headline" : "Why Mines Still Struggle with Siloed Data and How to Fix It",
  "image" : {
    "@type" : "ImageObject",
    "height" : 630,
    "url" : "https://8730707.fs1.hubspotusercontent-na1.net/hubfs/8730707/Featured%20Images%20%281200%20x%20630%20px%29%20%2823%29.png",
    "width" : 1200
  },
  "mainEntityOfPage" : "https://ntwist.com/blog/why-mines-still-struggle-with-siloed-data",
  "publisher" : {
    "@type" : "Organization",
    "logo" : {
      "@type" : "ImageObject",
      "height" : 60.0,
      "url" : "https://8730707.fs1.hubspotusercontent-na1.net/hubfs/8730707/Logo-removebg-preview%201.png",
      "width" : 196.55173
    },
    "name" : "The Signal by NTWIST"
  }
}
```