Points of interest information sneaks quietly behind many businesses as it determines where they will open a store and how they will measure their real-world impact. But POI databases are often selected hastily, based on usability or familiarity, not fitness for a particular purpose.
Not all POI data is equal. The value it generates depends on the way it reflects your physical reality of your business in the physical environment in which it operates.
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Begin with the business question, not the dataset.
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Knowing what you need to support any POI database is crucial beforehand.
Some teams require precise store locations to conduct market analysis. Others depend on POI data to construct geofences, grasp foot traffic or connect visits in physical geography with digital activity. A database which seems good for visualization may still fall short for analytics, and vice versa.
Well-documented on paper you aren’t going to get the max out on the right POI data. It is the one you plan to make use of, the one aligned with how you will use it.
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Accuracy matters more than scale.
**Huge POI counts may be misleading.
A good and useful POI database must show valid, live, operational locations (a real, alive or operating location, and not just old listings, home-based entities, or locations which no longer exist). In the case of modeling, attribution or forecasting to which POI data is inputted, even the slightest missteps can add up to huge errors.
Whether true or not is frequently determined by sampling in known places, checking how often categories are broken down, and by the frequency at which data is refreshed.
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Investigate attribute depth and consistency.
**POI data is much more than just latitude and longitude.
Attributes, such as category, brand affiliation, address quality and operating status, help decide whether the data can justify meaningful analysis. Consistency is just as vital. When attributes are poorly populated or uneven across regions, the findings extracted from them can be biased.
A smaller or not-so-bad dataset with well-maintained, well-attributed attributes does much better than a big one.
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Granularity can change the answer.
**The spatial representation of a place makes a very significant difference.
Some POI databases contain only one point or a centroid for the location. Others comprise exact building footprints that replicate the actual form and limits of a location. This difference is essential for purposes like visit measurement, geofencing and proximity processing, where even the slightest spatial mistakes can result in incorrect statements.
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Understand coverage and relevance.
**Geographic coverage must fit where your business is either currently located or intends to be.
This can be seen not just at the national level, but also in cities and many kinds of locations. A POI database can perform well for consumer-facing business but could underrepresent industrial, healthcare or specialized locations.
Assessing coverage by category, and by a geographic region, prevents blind spots later on.
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Expect change; have a plan for it.
**Physical places are constantly opening, closing and evolving.
A reliable POI database embodies this reality; it frequently receives updates and measures changing behavior transparently. Knowing how often data is refreshed, how to handle changes and so forth are all just as important as the first day's snapshot you receive.
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POI data perspective
**This view follows from our work at SafeGraph, where we work right next to the POI data and see how differences in accuracy, granularity and maintenance in such data have direct consequences for downstream analysis.
In any use case there's one consistent pattern: when POI data is chosen carefully and critically, it gets deeply tied to real business needs and thus delivers the most value out of it.
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