02 Sep 2026
Mining and Metals
Why data quality is the real AI challenge in the mining industry
Insights
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Artificial intelligence (AI) is playing an increasingly important role in the mining industry. Predictive maintenance for critical equipment, energy optimization, improved metallurgical recovery, asset monitoring and process anomaly detection are just a few of the many applications with significant potential benefits.However, when an AI project fails to deliver the expected results, the problem rarely lies with the algorithms themselves.In our experience, the main obstacles are usually related to data quality, availability and structure. Even the most sophisticated models cannot produce reliable recommendations if the data they rely on is incomplete, inconsistent or difficult to use.Before we talk about artificial intelligence, we first need to talk about data.
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Amanda Fitch, P.Eng.
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Todd McCracken, P.Geo.
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Todd McCracken, P.Geo.
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Stéphane Charest, P.Eng.
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Ali Naderian, Ph.D., P.Eng., PE, IEEE SM
Principal Advisor, High Voltage Services
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Dave Olsthoorn, P.Eng., M.A.Sc.
Engineer, Energy and Air Quality
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Patrick Beaulieu, EIT
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Omid Sargazi, P.Eng., P.E.
Senior Engineer, Civil
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Luis Ramirez, P.Eng.
Principal Engineer, Mechanical
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