From drill rigs to data platforms: clean information is becoming exploration’s sharpest tool for faster, smarter and more confident decisions

This article was originally written by Jamie Wade and published at The Rock Wrangler. Read the original published article here.

The future of mineral exploration may hinge less on drill rigs and more on the quality of the data flowing from them.

Speaking with The Rock Wrangler, Corey Hunter, Commercial Account Manager at acQuire, explained that exploration and mining companies are reaching a tipping point where structured, high-quality data is no longer optional – it’s essential. For Corey, it has become a fundamental enabler of faster decision-making and access to advanced technologies such as machine learning (ML) and artificial intelligence (AI).

“Machines are only as smart as the data they learn from,” Corey says. “If you don’t have accurate, trusted, structured data, the models simply can’t deliver reliable insights. But when you get that foundation right, you open the door to predictive capabilities that can transform exploration targeting and resource estimation.”

The hidden cost of messy data

According to Corey, one of the biggest challenges facing exploration teams today is validating data at the point of capture. Too often, data is gathered in the field and only checked later, resulting in errors that ripple through the workflow.

This issue is compounded by the fact that geological, geotechnical, hydro and GIS data often sit in different systems.

“When those systems don’t talk to each other, you end up with siloed information, manual copy-pasting, and version control headaches,” Corey explains. “Exploration professionals spend more time cleaning up their data than using it to drive decisions.”

The lack of standardisation only magnifies the problem. Something as simple as inconsistent lithology codes can create downstream reporting issues, introduce compliance risks, and delay project timelines. “Without strong data practices and standards, efficiency takes a big hit,” Corey says. “These are solvable challenges, but they require the industry to prioritise data quality in the same way it prioritises safety or equipment maintenance.”

Building rock-solid foundations

acQuire’s response to these challenges is to provide a structured framework that underpins geological data management from exploration through to production. Corey highlights acQuire’s data model, which sits at the heart of the company’s solutions, including GIM Suite and GIM Essentials.

“This model is the blueprint,” Corey explains.

“It lets teams design workflows that are consistent, reliable and scalable. With GIM Suite, you can automate data imports like plod and assay information. That reduces the risk of human error, keeps the original data untouched, and ensures a clean, auditable trail.”

The value of this approach, he says, is that it creates a trusted single source of truth for project teams – something that is essential when decisions worth millions of dollars depend on accurate inputs.

Smarter tools in the field

Exploration companies are also improving data capture at the source, with modern web and mobile technologies allowing geologists to log observations directly on tablets or smart devices, even in offline environments.

“Whether it’s grid logging, graphical logging or form logging, teams now have flexible options that suit their workflows,” Corey says.

“The ability to validate data in the field and then sync it back to the database with the press of a button means no more messy exporting and re-importing.”

On the integration front, Corey points to the growing use of web APIs that connect geological databases directly into business intelligence (BI) systems. This gives project teams real-time visibility of drillhole schedules, dashboard updates, and progress reports, reducing lag and improving control.

An exploration geologist conducting surface sampling in the field.

Structured data and the AI horizon

For Corey, the most exciting frontier is the role structured data will play in enabling predictive analytics and AI. “We’re seeing companies start to feed clean, organised data into ML models, and the results are promising,” he says.

When executed well, these models can highlight drilling targets, forecast resource potential, and provide early warnings about risks long before traditional workflows could.

“The better your data quality, the better these models perform,” Corey stresses. “And the more repeatable and scalable your success becomes across different projects.”

Case study: turning spreadsheets into confidence

The shift from messy to structured data is already paying dividends in the field. Corey points to the example of Peak Iron Mines, where the geology team was bogged down in spreadsheets and manual processes that slowed decision-making.

“By adopting GIM Essentials, they streamlined their data capture and validation methods,” he says. “Now the team has faster access to clean data, giving them greater confidence in their field decisions. It’s a great example of how investing in data management translates directly to improved operational outcomes.”

Up close view of the Project Overview in a GIM Essentials dashboard.

Striking the balance between speed and governance

Corey acknowledges that exploration teams often walk a fine line between the need for rapid access to data and the requirements for compliance. The solution, he says, lies in permission-based access and well-defined governance structures.

“Assigning licences and access levels by role or project ensures the right people get the right data at the right time,” he says.

“From there, business rules and workflows guide how data is captured, reviewed and locked down. That keeps it accurate, auditable and compliant, without slowing down operations.”

Real-time dashboards and reporting tools then give teams the visibility they need to move quickly while remaining within governance frameworks.

What’s next: voice capture, wearables and predictive geology

Looking ahead, Corey is particularly excited about emerging tools that will make field data capture faster and more intuitive.

“Smart logging technologies are around the corner,” he says.

“Imagine voice-activated data capture or wearable headsets and glasses that let geologists log observations hands-free in the field. It’s going to make data collection faster, more efficient, and a lot more natural.”

Integrations via APIs are also changing the way companies work. “The old ‘export to CSV and re-import’ workflow is on its way out,” Corey says. “Real-time data flow between platforms is the new normal, and it’s unlocking faster, cleaner decision-making.”

But it is the combination of structured data and machine learning that Corey believes will define the next era of exploration. “Predictive geology and resource estimation are no longer just aspirational ideas,” he says. “With clean, well-structured data, they are becoming operational realities.”

Advice for exploration companies

Corey’s final piece of advice is simple but powerful: don’t wait until your data becomes a problem.

“Start with a solution that can scale and integrate,” he says. “Invest in learning your systems, because the more your team understands their capabilities, the more value you’ll unlock. And above all, put strong governance and capture procedures in place early. A little structure up front saves a lot of pain later.”

For exploration teams, the message is clear. Structured data is no longer just a back-office compliance requirement – it’s the key to smarter targeting, faster decisions, and a competitive edge in a tightening resources market. And for companies willing to tackle the challenge now, the rewards could shape the next generation of discoveries.