The challenge is knowing what to do with it and how to use it to improve performance.

Across sites, large volumes of operational data are collected every day. Workforce metrics, equipment performance, production rates, and downtime are all tracked in some form. Despite this, many operations still struggle to translate that information into decisions that drive measurable results. Data often sits in spreadsheets, reports, or disconnected systems that were not designed to support real-time analysis or long-term planning.

From our experience, the opportunity is not to collect more data, but to better structure and apply what already exists.

The Data Is There, But It Is Not Always Working for You

Most operations are already capturing the information they need. Workforce data, such as crew availability, operator competency, and shift timing, is recorded alongside equipment performance metrics like run time, utilization, and output. Production is measured in tonnes, metres, or installed materials, while delays and downtime are tracked across activities.

In some cases, mining operations use less than 1% of the data they collect from equipment and systems (McKinsey & Company). This reinforces that the challenge is not data availability, but how effectively it is structured and applied.

The issue is that this information is often fragmented across multiple tools and formats. Excel spreadsheets, manual logs, and disconnected systems often require repeated manual entry and adjustment. This makes it difficult to consolidate information and identify trends over time. Even when dashboards are introduced, they are not always integrated into daily workflows or decision-making routines.
Valuable insights exist, but they are not always accessible in ways that support action.

Why Operational Data Goes Underused

When operational data is brought together, validated, and reviewed consistently, it begins to reveal patterns that are not always visible in day-to-day activities.
Analyzing workforce training and competency can highlight opportunities to better balance crews and improve productivity. In one example, redistributing labour across teams resulted in a more balanced and effective workforce.

Telemetry data can also provide clearer visibility into equipment usage patterns, helping teams improve shift utilization and make better use of available operating time. Reviewing historical production data helps ensure that planned targets align with workforce capability and system constraints, leading to more accurate and achievable plans.
These insights support stronger planning, more consistent execution, and more predictable outcomes. They also allow operators and supervisors to benchmark performance across crews and against targets, reinforcing accountability and supporting continuous improvement across the operation.

The opportunity is significant. Industry analysis suggests that data-driven improvements could represent up to 17% of the global mining cost base (McKinsey). At an operational level, broader industrial research shows that applying data-driven approaches can improve efficiency, reduce downtime, and enhance overall system performance when implemented effectively.

What Changes When Data Is Used Effectively

The underuse of operational data is rarely caused by a lack of effort. More often, it is the result of structural and cultural challenges.

Teams may not have a clear understanding of what data is available or where it is stored. In many cases, day-to-day operational demands leave little time for deeper analysis. Without a centralized system, data remains disconnected and difficult to use.

Adoption is another key factor. If data is not used consistently across all levels of the organization, its value quickly diminishes. In some environments, there can be hesitation to engage with data, particularly when it highlights gaps in performance. When data is positioned as a tool for improvement rather than evaluation, it is more likely to be adopted and used effectively.

Leadership alignment is also critical. When management consistently uses data to guide decisions, it sets the expectation for how it should be used across teams and helps embed it into daily operations.

From Information to Action

Collecting data is only the first step. The real value comes from turning that information into decisions that support the operation.

To do this, data needs to be visible to the right people at the right time. Supervisors benefit from daily visibility into performance, while management teams require a broader view of weekly and monthly trends. At the corporate level, data supports long-term planning and strategic direction.

Confidence in the data is equally important. If information is inconsistent or unreliable, it will not be used. Clear ownership ensures that data is maintained at the source and trusted across the organization. When issues are identified, they should be addressed at the source rather than adjusted downstream. This reduces rework, improves data quality, and builds confidence in the information.
Data must also be embedded into structured management routines. Daily reviews support immediate decision-making, while weekly and monthly trend analysis helps teams identify patterns and focus on the areas that will have the greatest impact.

This approach also supports planning and design decisions. At New Gold’s Rainy River Mine, simulation modelling was used to evaluate haulage requirements under different operating scenarios. By leveraging existing mine data, the team assessed equipment and infrastructure needs and supported more informed decisions around procurement and mine design.
In this case, data was used not only to understand performance, but also to shape future outcomes.

Bridging the Gap Between Data and Performance

When used effectively, operational data can drive measurable improvements across mining and industrial systems. It helps teams identify bottlenecks, track performance against targets, and understand the root causes of inefficiencies. It also strengthens planning by grounding decisions in actual historical performance.

One of the most valuable applications is validating planning assumptions. Comparing historical performance to forecasted rates provides a clearer picture of what is achievable and supports more reliable decision-making.
This is something we see regularly in practice.

At Glencore’s Sudbury operations, there was an opportunity to further align engineering planning with operational execution. By implementing a site-wide digital work management system, BESTECH helped centralize data, improve alignment between planning and execution, and introduce structured management routines. This resulted in improved collaboration, clearer visibility into performance, and more consistent decision-making. In many cases, real-time dashboards were introduced across the site to help teams quickly understand performance and focus discussions where they mattered most.

At Glencore’s Craig Mine, a focused time study provided detailed insight into development activities across multiple underground shifts. This work supported improvements in cycle efficiency and contributed to increased productivity.

In both cases, the value came from moving beyond data collection to structured analysis and application. With centralized and accessible data, teams are also able to respond more quickly to operational and corporate questions with clear, evidence-based insights.

Extending Data Beyond Operations

The same principles that improve operational performance also apply to planning, cost management, and long-term decision-making.

When data is structured and consistently reviewed, it provides a clearer understanding of how systems are performing today and how they are expected to perform in the future. This is especially important in areas such as energy, where costs are complex, variable, and influenced by external factors such as regulatory frameworks and market conditions.

At KGHM’s Victoria Project, large volumes of electricity market data are structured and analyzed to assess the impact of evolving IESO billing frameworks. This allows for scenario modelling and comparison against historical cost structures, supporting more accurate forecasting and informed financial planning.

Similarly, work with Magna Mining has included validating complex energy invoices and benchmarking new billing structures against historical data. This ensures that financial decisions are based on reliable information while identifying opportunities for cost optimization and improved long-term planning.

Whether applied to operations or financial management, the principle remains the same. When data is used consistently, it becomes a foundation for better decision-making across the business.

A Practical Starting Point

Improving the use of operational data does not require a complete overhaul. A practical first step is centralization. Bringing data together in a way that allows it to be consistently reviewed and understood creates immediate value.

From there, reducing manual data entry, automating reporting, and comparing historical performance against planned targets can improve both efficiency and confidence in decision-making.
A structured and collaborative approach ensures that data supports the operation rather than adding complexity.

Moving Forward

The value of operational data is not in how much is collected, but in how effectively it is used.

When data is centralized, trusted, and embedded into daily operations, it becomes more than a reporting tool. It becomes a foundation for decision-making that helps teams improve performance, strengthen planning, and operate with greater confidence.

For mining and industrial operations, the opportunity is not to collect more data. It is to use existing data more effectively and to build the systems and practices that turn it into measurable results.

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Prepared in collaboration with Justin Sivret