Authentic is not the same as usable
Most conversations about trustworthy data stop at two questions: is it real, and has it been changed? The sector has invested heavily in answering them, from signed imagery to content credentials. But a dataset can pass both tests and still fail the decision it is used for.
Consider a building-footprint layer. Its source is documented and the file has not been altered. But it is four years old, omits recent extensions and is licensed for internal mapping rather than the regulatory submission being prepared. Its authenticity does not resolve any of those limitations.
WGIC’s report, Data to Decisions: Rebuilding Trust in Decision-Grade Geospatial Data, explains what more is needed. Decision-grade data is fit, traceable, lawful and defensible for a specified decision. Those final words carry the weight: a dataset suitable for one task may be unsuitable for another.
Six properties that work together
The report identifies six linked properties. They are conditions, not sequential steps: remove any one and the data is not decision-grade, however strong the other five.
Figure 3 from the report: decision-grade data = fit, traceable, lawful and defensible for a specified decision.
Together, these properties connect evidence about a dataset to the purpose someone intends to use it for.
What each control proves, and what it does not
Each control answers a different question. Authenticity concerns whether data is what it claims to be; integrity, whether it has been altered; provenance, where it came from and how it was transformed. None, by itself, establishes that the original measurement was accurate or that the dataset suits a particular decision.
Content credentials can provide evidence about an image’s source and history. Users still need to assess acquisition date, cloud cover, resolution and other limitations. Documented quality does not establish permission to use the data, and an uncertainty statement does not establish who bears responsibility if its use causes harm.
These distinctions matter more as data passes through models, platforms and several organizations before reaching a user: trust built on measurement must be rebuilt on disclosure.
Fitness changes over time and place
A dataset that supported a decision when captured may become unsuitable as terrain, infrastructure or population patterns change. Capture dates and update cycles matter, along with the conditions that should trigger reassessment.
Coverage also deserves scrutiny. Urban and commercially important areas often have richer, more frequently updated data than rural or underserved regions. Those differences affect analysis and can introduce bias into model-derived outputs.
Responsibility needs similar attention. Providers, model developers, platform operators and users hold different controls over accuracy, validation, versions and final use. Understanding those roles helps identify where a problem should be detected and addressed.
Six questions before relying on a dataset
An unresolved gap calls for further evidence, validation or a change in use before relying on the data.
Fit for a decision, not fit in general
Authenticity and integrity remain necessary. They were never sufficient. The practical question is whether a dataset can support this decision, in this context, with these consequences.
Explore the framework in Chapter 2 of Data to Decisions: Rebuilding Trust in Decision-Grade Geospatial Data.
Explore what makes geospatial data fit for a decision. WGIC’s Data to Decisions report identifies provenance, governance, metadata and accountability as the foundations for rebuilding trust, and sets out practical recommendations across the data lifecycle, from data capture and lineage to AI-driven models and decision-making.
It also explores data sovereignty, transparency, uncertainty and the governance of Geospatial Foundation Models.
