Why data quality is the real AI challenge in the mining industry

Mining environments are complex and often located in remote areas, making communication outages and instrumentation issues common.

Consider a predictive maintenance system designed to monitor a SAG mill.

For several weeks, a vibration sensor transmits data normally. Then, because of a communication failure, five days of data are lost. Unfortunately, that period coincides with the first signs of a bearing failure. The model never sees how the problem evolves. When data transmission resumes, the observed behaviour appears suddenly abnormal, even though it’s actually the result of gradual deterioration. The result is lower accuracy, more false positives and reduced trust in the system.

Mining companies often operate multiple sites that have evolved independently over many years.
It’s common to encounter:

• Different naming conventions
• Different units of measurement
• Non-standardized data structures
• Variable definitions of equipment statuses

For example, a conveyor may be identified as:

• CV-101 at one site
• CONV-101 at another site
• Conveyor_Main_A at a third site

For a person, the distinction is straightforward.
For a corporate analytics system, however, this inconsistency significantly complicates data integration and the development of models that can be applied across multiple operations.

A sensor never measures reality perfectly.

In mining environments, data often contains noise, meaning variations or disturbances that aren’t directly related to the phenomenon being measured. This noise can come from many sources, including electrical interference generated by motors or variable-frequency drives, poorly calibrated or ageing sensors and vibrations transmitted from nearby equipment. 

For example, a vibration sensor installed on a conveyor belt may also detect vibrations from a nearby crusher, creating a signal that doesn’t accurately reflect the conveyor's actual condition. Without proper validation and processing, AI models may interpret these disturbances as anomalies or significant trends, leading to inaccurate recommendations. The quality of analytics therefore depends on the ability to distinguish useful signals from background noise in operational data.

This is probably the most underestimated challenge because an isolated data point rarely provides much value. Imagine a significant increase in vibration levels on a process pump. This increase could be associated with:

• Mechanical wear
• A change in flow rate
• Recent work
• A change in slurry density
• Changing operating conditions

Without access to process data, maintenance data and operator logs, the algorithm cannot distinguish among these situations. To produce meaningful recommendations, AI must understand the context in which an event occurs.

The concept of smart sensing is often associated with adding newer, more powerful and smarter sensors. In reality, its purpose is much broader. It aims to ensure that the most relevant data is:

• Collected
• Accessible
• Reliable
• Put into context
• Actionable

An effective smart sensing strategy can help:

• Improve measurement quality
• Increase data acquisition frequency, when necessary
• Reduce operational blind spots
• Ensure event traceability
• Lay the groundwork required for AI projects

In many projects, the greatest gains come from improving the quality of existing data, not from new algorithms.

AI presents an exceptional opportunity for the mining industry, but its success depends on a reality that’s often overlooked: data quality.

Before investing in advanced analytics platforms, organizations must ensure they have reliable, consistent and well-contextualized data.

In our next blog article, we'll explore how mining companies can maximize the value of their existing data using smart sensing, virtual sensors and computer vision technologies.