Buying location data is unusual among procurement decisions because the product is almost impossible to evaluate from the outside. Two vendors can show you the same demo city, quote similar prices, and claim similar coverage — and behave completely differently the first time your users query a rural postal code, a new subdivision, or a francophone municipality.
Having sat on both sides of these evaluations, here's the framework we'd suggest to any Canadian team — including teams evaluating us.
1. Interrogate Coverage Claims Geographically
"Canada-wide coverage" is the least informative phrase in the industry. Every dataset degrades somewhere; the question is where and how gracefully. Useful probes:
- Pick three test geographies before any demo: a major metro, a mid-size city in another province, and a rural area you know personally. Run the same queries in all three.
- Ask what percentage of the vendor's records come from each source type — authoritative (government, census), licensed commercial, and inferred/modelled. All three are legitimate; the mix tells you what you're buying.
- Ask specifically about Quebec. Language handling, municipal naming conventions, and data source availability differ enough that Quebec quality is a strong proxy for how seriously a vendor takes Canadian coverage overall.
2. Make Freshness Concrete
"Updated regularly" spans everything from nightly pipelines to an annual refresh someone runs manually. Pin it down per layer: census-derived demographics legitimately update on census cycles, but permits, listings-adjacent data, and boundaries should have much shorter loops. Two questions cut through most vagueness: "What's the date of the newest record in this layer right now?" and "When a municipality publishes new data, what's the lag before it's in the API?"
3. Read the Licence Before the Price
Location data licences vary more than the datasets do. The traps that actually bite teams later:
| Licence question | Why it matters |
|---|---|
| Can you store responses? | Some licences are display-only: you may show the data but not persist it. That forbids the pre-enrichment architecture most platforms eventually want. |
| Can derived data outlive the contract? | If you train models or compute scores from the data, find out what happens to those artifacts if you churn. |
| Are there field-level restrictions? | Datasets assembled from multiple upstreams sometimes carry different terms per field — discover this before building, not after. |
| What are the attribution requirements? | Usually easy to satisfy, but expensive to retrofit across a product you've already shipped. |
4. Evaluate the API as an Engineering Dependency
You're not just buying records; you're adding a runtime dependency. The basics — latency from your infrastructure, documented rate limits, status page history, sandbox access — matter more than feature breadth once you're in production. So does schema consistency: if every layer returns differently shaped responses, your integration cost multiplies by layer. And ask about bulk: platforms that succeed with enrichment usually end up wanting both real-time lookups and periodic full-inventory enrichment, which are different technical products.
5. Weight the Roadmap, Lightly
Finally, a caution in both directions. Vendor roadmaps matter — you're entering a multi-year relationship, and a vendor investing in Canadian sources will compound in value. But never buy a roadmap item as if it were shipped. Evaluate what exists today in the geographies you serve; treat everything else as upside.
The pattern behind all five sections is the same: replace adjectives with observable facts. A vendor confident in their data will happily let you test the unglamorous geographies, name their sources, and show you the newest record in the layer. The evaluations that go wrong are almost always the ones that stayed inside the demo.