WGIC Data Committee Webinar
Data to Decisions — Rebuilding Trust in Decision-Grade Geospatial Data in the Age of AI
TBD. 2026 · 10:00 – 11:15 MT · 75-minute executive webinar · Registration open
Geospatial data is entering a fundamentally different era. Data can now be inferred, synthesized and transformed at enormous scale — often through processes the ultimate decision-maker cannot inspect. The question is no longer simply “is the data accurate?” It is whether you can establish where it came from, what happened to it, whether you may use it — and whether it is fit for this particular decision.
The central question
Decision-grade data must be Fit. Traceable. Lawful. Explainable. Accountable. Defensible.
Why this webinar — why now
For decades, organizations trusted maps because they understood how they were made: measurements were taken, methods documented, accuracy declared, responsibility clear. AI, cloud processing, automated feature extraction and foundation models are changing that relationship — creating extraordinary opportunity and new risk in equal measure.
Measurement-based trust
Surveyed → Documented → Known responsibility
Model-based trust
Inferred → Automated → Multi-source → Continuously changing → Distributed responsibility
The six-part framework
From the white paper’s six-part framework: no single test establishes trust. Authenticity does not prove accuracy. Integrity does not prove the source was right. Provenance does not prove fitness for purpose. Accuracy does not establish lawful use.
Test 01
Where did the original measurement come from?
Consider
Follow the data
The white paper’s recurring scenario, animated: at every stage we ask what can fail — and what control keeps trust intact.
Surveyed and checked at the source.
AI classification and multi-source cloud processing.
Hashing, timestamping and machine-readable lineage.
Jurisdiction, licensing and authorized processing controls.
The information reaches a model or decision-maker.
Who owns the risk?
If poor geospatial data contributes to a costly or safety-critical failure — who is responsible? The answer increasingly cannot be left implicit. It must be addressed through governance, contracts, procurement requirements, documentation and competent human review.
Controls
Source accuracy, calibration, known limitations
Controls
Training data, validation, model behavior, uncertainty
Controls
Versions, updates, logging and access
Controls
Fitness-for-purpose assessment before acting
Recommended format
Designed as a conversation rather than a sequence of presentations — senior, cross-functional, and grounded in the white paper’s framework.
Opening poll — could your organization reconstruct exactly which data, model and transformations informed an important decision six months ago?
Why traditional geospatial trust worked, what cloud and AI have changed, and why visually convincing outputs are not synonymous with trustworthy outputs.
The six linked properties of decision-grade data — and why no single test establishes trust on its own.
The flood-map journey: five stages from measured to used, asking at each — what can fail, and what control keeps trust intact?
Data liability across provider, model developer, platform and deployer — and why responsibility can no longer be left implicit.
Sovereignty ≠ residency. Federated architectures: data stays home, insight can travel, governance travels with it.
Governance by design across the data, governance and model layers — with audit spanning all three.
The WGIC three-horizon roadmap, and the one thing a CDO or CTO should start on Monday morning.
Provenance, liability, procurement, metadata, sovereignty, AI and foundation models.
The WGIC three-horizon roadmap
Five attendee takeaways
Understand why accuracy alone is insufficient and apply the six linked dimensions of trust.
Trace risk from sensor and source through processing, AI, platforms and ultimately the decision.
Understand what provenance, uncertainty, sovereignty and model documentation should reveal.
Identify the respective controls held by data producers, model developers, platforms and end users.
Leave with concrete priorities for now, the next 1–2 years and the next 2–3 years.
Plus both resources: the full WGIC white paper and the companion Decision-Grade Data Playbook.
Start the Playbook now →Featuring
Chair, WGIC Data Committee
Airedale Advisory Limited
John Renard chairs the WGIC Data Committee and is Founder and Owner of Airedale Advisory Limited, with senior leadership roles across the geospatial industry and long-standing service in WGIC’s executive leadership. Focus: industry governance, decision-grade data, provenance, sovereignty, accountability and the WGIC action agenda.
Senior Vice President
RIEGL International
James Van Rens is a senior geospatial industry executive with more than three decades across remote sensing, 3D mapping and LiDAR technology, active in government and industry relations and WGIC initiatives on the future of geospatial data. Focus: source and capture, sensor-to-decision trust, data quality, automation and LiDAR.
WGIC Data Committee
Data to Decisions
Rebuilding Trust in Decision-Grade Geospatial Data
White paper · 2026
WGIC Data Committee white paper
Rebuilding Trust in Decision-Grade Geospatial Data
The WGIC Data Committee examines what organizations need to establish confidence in geospatial information as AI, cloud platforms and increasingly complex data ecosystems change the way spatial data is captured, processed, transformed and ultimately used. The paper develops a practical framework around provenance, governance, metadata and accountability — and proposes an industry action agenda for rebuilding trust in the emerging GeoAI environment.
It’s data you can defend.
Register for the webinar, read the white paper, assess your own environment — and start the conversation inside your organization.
