MINING SOFTWARE
Hexagon Smart Centre services combined with Safety Insights are helping customers to unlock new safety and productivity benefits, Hexagon says
Making connections
As more software solutions receive the AI treatment, the need for connectivity, context and integration increases, Dan Gleeson warns
There is no shortage of data in the mining sector with every bit of hardware – from drill strings to SAG mills – equipped with multiple sensors that provide readings on a regular basis.
Similarly, there are oodles of software platforms ingesting this data and displaying it in a meaningful way for interested parties to use in their decision-making processes. Then there is the onset of artificial intelligence( AI) to consider; a development that has put the spotlight back on the data source, with any algorithm worth its salt requiring the right structure and the right context to provide value.
This environment means the mining sector has a real chance of achieving the transformation it has been looking to capture for decades, in the process creating the fully autonomous and digitalised mine of the future.
Rudy Moctezuma, Chief Business Relations Officer at Eclipse Mining Technologies.
A truck payload metric is simply a number until connected with information about the operator, current weather conditions, haul route, the shovel loading it and the mine’ s downstream processing performance, for example. This context is a requirement for operators understanding performance variations and coming up with a plan to improve it.
The same is true for AI algorithms, as Gustavo Pilger, WW GEOVIA R & D Strategy & Management Director at Dassault Systémes, says.
“ Algorithms need to understand not just the data, but their meaning and their purpose within the context of mining processes,” he told IM.
And these same algorithms require that data – often collected from different systems across the operation – to be consolidated, indexed and standardised even before context is factored in.
Eclipse spotted these potential pitfalls some time ago, unveiling its flagship SourceOne™ Enterprise Knowledge Performance System all the way back in 2020 at the SME Annual Conference & Expo.
SourceOne was described back then as a trailblazing solution featuring a collaborative platform to connect data from different sources, and a datahub to store historical and contextual data, rendering it serviceable for analytics and for adoption of tools, such as AI and machine learning.
Where most mining software platforms focus on data from a specific domain, such as fleet, spatial or financial, SourceOne is designed to link data across the entire value chain.
It does this with an ontology-driven architecture that creates and preserves relationships between different datasets, enabling the system to understand how data connects and make suggestions based on these relationships, rather than simply storing it.
A knowledge-based approach
These trends also hold the potential to reinforce the siloed thinking that outsiders have observed over this same timeframe – it could be AI-backed grade control drilling not being linked to the relevant downstream metallurgical teams to tweak future mill configurations, for instance.
Therefore, the biggest challenge for mining companies today is establishing and leveraging a“ good foundation”, so all these various systems can integrate, according to
58
Eclipse’ s SourceOne Enterprise Knowledge Performance System has“ built in” mining intelligence, such as block models, haul cycles and reconciliation, to ensure up- and down-stream data provides full value chain context for clients
International Mining | FEBRUARY 2026