Seequent geoprofessionals survey: data and AI trends explained for project teams
Reviewed by Joe Ashwell
First reported on Geoengineer.org – News
30 Second Briefing
Geoprofessionals worldwide now spend over 25% of their time on data management, with Seequent’s 7th Geoprofessionals Data Management Report finding mining specialists at nearly one‑third and civil engineers at over one‑fifth, yet only 39% of mining organisations and 41% of civil teams have defined data frameworks. The survey of 1,000+ respondents shows 80% of mining and 69% of civil practitioners rate data management as highly or critically important, but many still lack a centralised “single source of truth”. AI adoption is accelerating, with 51% of organisations using or considering AI, up from 30% in two years, signalling strong demand for better-structured subsurface and historical datasets.
Technical Brief
- Respondents report working across multiple software platforms, increasing effort spent on data transfer and reconciliation.
- Unmanaged historical datasets are a specific pain point, limiting reuse of legacy drilling, testing and monitoring data.
- Data quality and integration of diverse sources are cited as primary technical barriers to extracting value.
- Civil infrastructure feedback points to missing foundational data frameworks as a systemic organisational constraint.
- For design and operations, structured frameworks are implied as prerequisites for reliable AI-assisted subsurface interpretation.
- Findings are based on self-reported perceptions and practices, so no independent audit of data quality or time use.
Our Take
Seequent’s role in Anglo American’s Barro Alto nickel mine digital integration project (Central and Leapfrog Geo) shows that the survey’s findings on data-management pain points are already feeding into live, multi-discipline subsurface models rather than remaining a purely research exercise.
Within our 21 Software stories, Seequent and Bentley-branded subsurface tools appear frequently in connection with mine-scale digital twins, suggesting that the reported 25% time burden on data handling is a key commercial driver for end-to-end platforms like OpenGround and Enviro Data rather than point solutions.
The gap between the high importance mining geoprofessionals place on data management and the relatively low proportion of defined frameworks signals near-term opportunity for Seequent and other Bentley companies to bundle governance and chain-of-custody tooling into existing modelling products, especially for owners worried about auditability on large projects.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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