From scattered spreadsheets to a reliable water balance: AI applied to water management in mining. We integrate data, operational context, governance and analytical automation to support better decisions on water, risk, compliance and operational performance.
In many operations, water is already too critical to continue being managed through fragmented sources, fragile manual routines and balances that arrive too late to support decisions. Isolated spreadsheets, scattered field data, partial instrumentation, multiple owners and low traceability make the process slow and vulnerable.
The result is well-known: difficulty consolidating the water balance with confidence, low visibility for leadership, rework in audits, struggle to respond to deviations and little capacity to turn water information into a management routine.
Data Riders solves this by connecting data, assets, operational context, governance and applied AI.
The approach combines information engineering, operational logic and applied intelligence. Instead of just aggregating numbers, it contextualizes assets, inputs, outputs, storage, owners, data reliability and review needs.
Aligned with standards and best practices such as WAF, ICMM and interfaces with TSM/GISTM, we turn water information into an executable decision routine.
Spreadsheets, reports, field measurements, sensors, emails and operational documents.
By asset, flow, process, owner and criticality.
Consistent with WAF, ICMM and interfaces with TSM/GISTM.
Operational, tactical and executive — turning data into a decision routine.
Consistency checks and better readiness for audits, reporting and governance.
Less dependency on manual consolidation and higher data reliability.
Recirculation, specific consumption, discharge compliance and availability.
Interconnections and systemic view of the operation's water network.
Climate and operational scenarios with pre-defined triggers and actions.
Preparation for sustainability, licensing and executive reporting.
Monthly/weekly cycle with indicators, owners and traceable decisions.
Value is not just about "having a nice dashboard". It's about improving how you work, increasing data reliability, reducing regulatory exposure and maturing water governance.
With more traceable data, clients gain a better base for reports, decision-making, indicators, leadership discussions, stakeholder interaction and audit processes.
Implementation doesn't need to start from a perfect scenario. Data Riders can act at different maturity levels, starting from where the client is. In some cases, this means first structuring the basics: criteria, databases, information quality, responsibilities and rituals. In others, it means advancing with automation, sensor integration, digital twin and more sophisticated AI. An evolutionary, realistic path — not a promise of instant transformation.
Full case study of water management with AI in a mining operation.
See caseNo. The dashboard is one of the possible outputs. The proposal involves data, operational context, reliability, governance, decision routines and readiness for audit and compliance.
Yes. Evolution by maturity: from isolated spreadsheets and manual processes to more robust automations, sensor integration and more advanced architecture.
As consistency and governance foundations. The solution connects with mining standards and best practices — not a stand-alone automation.
No. AI accelerates ingestion, consolidation, traceability and review. Technical judgment and governance remain human.
More reliability, better water balance, less rework, more traceability, useful dashboards and better readiness for audit, reports and executive decisions.