Why Mines Still Struggle with Siloed Data and How to Fix It

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?.

1: What “Mining Data” Really Means

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

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.

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International Mining. (2025). Unlocking siloed data in mining operations. Retrieved from https://im-mining.com/2025/02/07/unlocking-siloed-data-in-mining

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