--- title: Why data quality is the real AI challenge in the mining industry date: 2026-09-02T12:53:00-04:00 author: Maelle Benoit canonical_url: "https://www.bbaconsultants.com/es-cl/perspectivas/why-data-quality-is-the-real-ai-challenge-in-the-mining-industry" section: Publications --- 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. 1. ## The myth of the magic algorithm Discussions about AI often focus on predictive models, analytics platforms and the latest technological advances in large language models (LLMs) with advanced reasoning capabilities, in autonomous agents and in automated recognition systems. However, the biggest challenges are often much more fundamental. Before deploying an AI project, an organization should be able to answer the following questions: - Are instruments properly calibrated? - Is data collected continuously? - Do systems communicate effectively with one another? - Are historical data sets long enough to train a reliable model? - Are maintenance events documented properly? Without these foundations, AI is essentially trying to interpret a partial picture of reality. 2. ## Challenge 1: missing data 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. 3. ## Challenge 2: inconsistent data 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. 4. ## Challenge 3: noise data 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. 5. ## Challenge 4: lack of context 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. 6. ## Why smart sensing is becoming essential 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. 7. ## Conclusion 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. This content is for general information purposes only. All rights reserved ©BBA 1. ![Marie Eve Desrochers](https://www.assets.bbaconsultants.com/bba-production-assets/production/images/avatars/_size_avatar/215810/Marie-Eve-Desrochers.webp) Marie-Eve Desrochers Senior analyst, Process control and operational technologies [ ](https://www.linkedin.com/in/marie-eve-desrochers/) 2. ![Guillaume Boily](https://www.assets.bbaconsultants.com/bba-production-assets/production/images/avatars/_size_avatar/215807/Guillaume-Boily.webp) Guillaume Boily Senior analyst, Process control and operational technologies [ ](https://www.linkedin.com/in/guillaume-boily-44a74413/) - [ 17 ago 2026 Structural engineering early phase: unlocking value through early decisions - ![Nicolas Samson](https://www.assets.bbaconsultants.com/bba-production-assets/production/images/avatars/_size_avatar/197852/Nicolas-Samson.webp) Nicolas Samson, P.Eng. Senior Engineer, Structural ](https://www.bbaconsultants.com/es-cl/perspectivas/structural-engineering-early-phase-unlocking-value-through-early-decisions "Structural engineering early phase: unlocking value through early decisions") - [ 12 ago 2026 Four mistakes that can harm the future performance of industrial assets - ![Caroline provost](https://www.assets.bbaconsultants.com/bba-production-assets/production/images/avatars/_size_avatar/208557/Caroline-provost.webp) Caroline Provost P.Eng. Director, Operational Strategy Implementation ](https://www.bbaconsultants.com/es-cl/perspectivas/four-mistakes-that-can-harm-the-future-performance-of-industrial-assets "Four mistakes that can harm the future performance of industrial assets") - [ 27 jul 2026 Minería y metalurgia Por qué las decisiones tempranas sobre relaves definen el desempeño de la mina a largo plazo - ![Geraldine Cosset](https://www.assets.bbaconsultants.com/bba-production-assets/production/images/avatars/_size_avatar/206707/Geraldine-Cosset.webp) - ![Jared Purdy](https://www.assets.bbaconsultants.com/bba-production-assets/production/images/avatars/_size_avatar/212424/Jared-Purdy.webp) ](https://www.bbaconsultants.com/es-cl/perspectivas/las-decisiones-sobre-relaves-definen-desempeno-mina-a-largo-plazo "Por qué las decisiones tempranas sobre relaves definen el desempeño de la mina a largo plazo") - [ 02 jul 2026 Minería y metalurgia Python: Una herramienta clave para el análisis de datos en el procesamiento de minerales - ![Charles Blais](https://www.assets.bbaconsultants.com/bba-production-assets/production/images/avatars/_size_avatar/211197/Charles-Blais.webp) - ![Juan Pablo Vergara](https://www.assets.bbaconsultants.com/bba-production-assets/production/images/avatars/_size_avatar/211266/Juan-Pablo-Vergara.webp) ](https://www.bbaconsultants.com/es-cl/perspectivas/python-una-herramienta-clave-para-analisis-datos-en-procesamiento-minerales "Python: Una herramienta clave para el análisis de datos en el procesamiento de minerales") [ Ver todo ](https://www.bbaconsultants.com/es-cl/perspectivas "Ir a la página del listado de Publications") 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. 1. ## The myth of the magic algorithm Discussions about AI often focus on predictive models, analytics platforms and the latest technological advances in large language models (LLMs) with advanced reasoning capabilities, in autonomous agents and in automated recognition systems. However, the biggest challenges are often much more fundamental. Before deploying an AI project, an organization should be able to answer the following questions: - Are instruments properly calibrated? - Is data collected continuously? - Do systems communicate effectively with one another? - Are historical data sets long enough to train a reliable model? - Are maintenance events documented properly? Without these foundations, AI is essentially trying to interpret a partial picture of reality. 2. ## Challenge 1: missing data 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. 3. ## Challenge 2: inconsistent data 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. 4. ## Challenge 3: noise data 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. 5. ## Challenge 4: lack of context 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. 6. ## Why smart sensing is becoming essential 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. 7. ## Conclusion 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. This content is for general information purposes only. All rights reserved ©BBA