Testing location data with edge cases, not just happy paths
An address in a well mapped city center tells you almost nothing about how your system handles a rural route, a disputed border, or a query near the poles. Test the hard cases deliberately.
Why "99% accurate" claims need a definition to mean anything
A number like "99% accurate" sounds precise and reassuring, and that is exactly the problem with it. Precision in tone is not the same as precision in meaning, and a bare accuracy percentage, offered without any statement of how it was measured, against what reference set, and at what precision level, is closer to a marketing slogan than a technical claim you can actually evaluate or rely on.
Every one of the questions this series has walked through individually, how a reference set was built, whether urban and rural addresses were both represented, whether the claim covers house precision or something coarser like city, how disputed or edge-case locations were handled, whether the figure is an average across wildly different regions or country data quality levels, changes what a single accuracy number actually means, sometimes dramatically. A 99% figure measured against a reference set of easy, well-mapped urban addresses in a handful of well-covered countries describes something completely different from a 99% figure measured against a globally representative set including rural addresses, postal-only regions, and areas with comparatively sparse underlying data, even though the two numbers look identical printed side by side.
This is exactly why a meaningful accuracy claim needs to specify, at minimum, what was measured, house-level rooftop accuracy is a fundamentally different claim from overall match success rate regardless of precision, what it was measured against, a stated and ideally reproducible reference set rather than an unspecified internal sample, and how results varied across different regions and precision levels rather than collapsing everything into one blended figure that necessarily hides the variation underneath it. A provider willing to share this level of methodological detail is giving you something you can actually evaluate and compare. A provider offering only the bare percentage is asking you to simply take their word for it.
This is also precisely why we do not publish a single blended accuracy or coverage percentage for My Geocode. Any such figure would necessarily average across enormously different conditions, well-mapped cities and sparsely documented rural areas, countries with granular postal data and countries without it, in a way that would describe an average location that does not actually correspond to any of your users' real queries. What we can offer instead, covered throughout this series, is the actual method: reference-point testing, the meaning behind precision and confidence, and the known, general limitations of each specific data type, so you can run the evaluation yourself against addresses that actually represent where your own users are, and reach a number that means something for your specific application.
If you are comparing providers, ask for the method behind any accuracy claim before you ask for the number itself. Try your own representative queries against the demo or the forward geocoding endpoint and build your own answer to what accuracy actually looks like for the addresses your business actually cares about, since that is the only number that was ever going to be useful to you in the first place.