Why does almost every team shopping for point of interest data end up ranking vendors on one number, the total POI count? Because that is the figure every vendor leads with, and it sounds objective. A database of 150 million locations must be better than one with 80 million, right?
Not necessarily. Raw count tells you almost nothing about whether the data will actually work for your use case. A POI database can be enormous and still fail you on the dimensions that matter most: coverage in your target geographies, freshness of attributes, accuracy of coordinates, and consistency of categorization.
This guide is for teams that have moved past the “how many POIs?” question and want to evaluate providers on criteria that actually predict performance.
What Is Point of Interest Data?
Point of interest data is structured information about physical locations: businesses, landmarks, venues, and any other place that someone might want to find, visit, or analyze. Each POI record typically includes a name, address, geographic coordinates, category, and a set of attributes that vary by provider (hours, phone number, website, brand affiliation, and so on).
The term covers an enormous range of use cases. A retail brand might use POI data to identify competitor locations for site selection. A navigation app needs it to answer “coffee shop near me.” An insurance underwriter might use it to assess commercial property risk. A hedge fund might use it to track foot traffic patterns. The underlying data asset is the same; the quality bar and the attributes that matter are completely different.
That diversity is exactly why generic vendor comparisons are so unhelpful. The right question is not “which POI provider is best?” but “which provider is best for my specific use case, geographies, and update cadence requirements?”
The Six Dimensions That Actually Matter
When you strip away the marketing language, POI data quality comes down to six things. A provider can be excellent on some and poor on others, so you need to evaluate each one against your requirements.
1. Coverage
Coverage has two dimensions: geographic and categorical. Geographic coverage asks whether the provider has strong data in your target markets. A US-centric provider might have dense, accurate coverage in North America and thin, stale coverage everywhere else. If you operate globally, that is a problem.
Categorical coverage asks whether the provider has deep data in your category of interest. Some providers have excellent restaurant data and thin industrial facility data. Others have strong retail coverage and poor healthcare coverage. Ask vendors to show you raw record counts and sample data for your specific categories in your specific geographies, not aggregate statistics.
2. Accuracy
Accuracy has multiple components. Coordinate accuracy matters enormously for any use case involving routing or proximity analysis. A POI with coordinates that place it 50 meters from its actual entrance is fine for a map; it is a problem for last-mile delivery. Address accuracy matters for verification and mailing workflows. Attribute accuracy matters for everything else.
The only way to assess accuracy is to test against ground truth. Pull a sample of records in locations you know well, verify coordinates against satellite imagery, and check attributes against known-good sources. Providers who will not let you run this kind of validation before purchase should be treated with skepticism.
3. Freshness
POI data decays fast. Businesses open, close, move, and rebrand constantly. Studies of business churn in urban areas typically find that 10 to 15 percent of businesses change state in some meaningful way every year, and that number is higher in some categories (restaurants, retail) and lower in others (hospitals, government buildings).
Ask vendors how frequently they update their data, but also ask how they detect changes. A provider who re-crawls web sources monthly is doing something different from one who ingests real-time signals from payment processors or GPS fleets. The update mechanism matters as much as the update frequency.
Also ask about the difference between “refreshed” and “verified.” Some providers update their last-seen timestamps when they re-process a record, even if the underlying data has not changed. You want to know when attributes were last verified against a real-world signal.
4. Completeness
Completeness measures how much of each record is filled in. A database with 150 million POIs where 60 percent of records are missing hours of operation is less useful than one with 80 million POIs where 90 percent of records have complete attribute sets, depending on your use case.
Ask vendors for attribute fill rates broken down by category and geography. Do not accept aggregate statistics. The fill rate for restaurant hours in New York is probably very different from the fill rate for industrial facility hours in rural markets.
5. Consistency
Consistency covers two things: categorical consistency and deduplication. Categorical consistency asks whether the same type of location is classified the same way across the database. A provider who sometimes categorizes a pharmacy as “retail” and sometimes as “healthcare” creates problems for any analysis that relies on category filters.
Deduplication asks whether the same physical location appears multiple times under different names or slightly different addresses. Duplicate records inflate apparent coverage and create downstream problems in any system that expects one record per location.
6. Lineage and Licensing
Where did the data come from, and what are you allowed to do with it? These questions matter more than most buyers realize until they are in legal review.
Data lineage affects quality. A provider who aggregates from authoritative sources (business registrations, government databases, verified review platforms) will generally have more accurate data than one who scrapes web directories. Knowing the source helps you understand the error profile.
Licensing affects what you can build. Some POI licenses prohibit redistribution, which matters if you are building a product. Some prohibit use in certain industries. Some require attribution. Read the license before you sign, and make sure your legal team understands the constraints.
How to Structure Your Evaluation
A rigorous vendor evaluation has three phases: requirements definition, shortlisting, and proof of concept.
Phase 1: Define Your Requirements
Before you talk to a single vendor, write down your requirements in concrete terms. Vague requirements lead to vague evaluations.
- Geographies: Which countries, regions, and market tiers do you need? Be specific. “Global” is not a requirement.
- Categories: Which types of locations do you need? How deep does the category taxonomy need to go?
- Attributes: Which fields are required versus nice-to-have? What fill rates are acceptable for required fields?
- Freshness: How quickly do you need data to reflect real-world changes? What is the cost of a stale record in your use case?
- Volume: How many records do you need? Do you need the full database or a filtered subset?
- Delivery: How do you need to receive the data? Bulk file download, API, database replication?
- Update cadence: How often do you need refreshed data? Real-time, daily, weekly, monthly?
Phase 2: Shortlist Vendors
With requirements in hand, you can quickly filter the vendor landscape. Send a structured RFI (request for information) that asks vendors to respond to your specific requirements rather than send their standard pitch deck. Ask for:
- Record counts broken down by your target geographies and categories
- Attribute fill rates for your required fields in your target markets
- Update frequency and the mechanism used to detect changes
- Sample data (100 to 1,000 records) in your target categories and geographies
- Summary of data sources and lineage
- License terms, including redistribution rights and industry restrictions
- Pricing model and minimum commitment
Vendors who cannot or will not answer these questions in writing are telling you something. Narrow your list to two or three providers who can demonstrate they meet your requirements before investing in a proof of concept.
Phase 3: Run a Proof of Concept
A proof of concept (POC) is the only way to validate vendor claims against your actual use case. The goal is not to test the vendor’s demo environment; it is to test your production workflow with a representative sample of their data.
Structure your POC around the questions that matter most for your use case. If coordinate accuracy is critical, design a test that measures it. If freshness matters, check records in a geography where you know the ground truth and look for staleness. If you need specific attributes, measure fill rates on a random sample.
Run the same POC with each shortlisted vendor using the same test design. That lets you make apples-to-apples comparisons rather than relying on each vendor’s preferred showcase.
Common Mistakes in POI Vendor Selection
Teams that have been through this process multiple times tend to make the same mistakes their first time. Here are the ones that cost the most.
Optimizing for price over fit
POI data is not a commodity. The cheapest provider who does not cover your geographies or cannot match your freshness requirements will cost you more in engineering time and data quality issues than the premium you would have paid for the right provider. Establish your minimum quality bar before you negotiate on price.
Evaluating on aggregate statistics
Aggregate record counts and fill rates hide enormous variation across categories and geographies. A vendor might have 95 percent fill rate on business name and address globally, but 40 percent fill rate on hours of operation in Southeast Asia. If you need hours of operation in Southeast Asia, the aggregate number is meaningless. Always get statistics broken down to your specific requirements.
Skipping the POC
Every vendor looks good in a demo. The demo uses curated data in favorable geographies. Your use case will involve edge cases, underserved markets, and categories the vendor is less strong on. A POC that tests your actual requirements will surface problems that the demo conceals.
Ignoring update mechanisms
Two vendors might both claim monthly updates, but one re-crawls web sources and one ingests signals from business registrations and payment networks. The second will catch closures and openings faster. The mechanism matters, not just the frequency.
Not reading the license
POI data licenses vary widely. Some prohibit redistribution. Some restrict use to specific applications. Some require attribution. Finding out after signing that your intended use is restricted is expensive. Legal review of the license should happen before you finalize vendor selection, not after.
The Hyperlocal Dimension
Standard POI databases are built around the location as the unit of record. Hyperlocal data goes a level deeper, adding attributes that describe what happens at a location over time: foot traffic patterns, visitor demographics, dwell times, and the catchment area a location draws from.
Hyperlocal data is derived, not observed directly. It is inferred from aggregated and anonymized signals (primarily mobile location data) and modeled to produce estimates. That means it has a different error profile from standard POI attributes. Coordinate accuracy is still relevant, but the more important questions are about the modeling methodology and the representativeness of the underlying signal.
If your use case requires hyperlocal attributes, add these questions to your evaluation:
- What is the source of the underlying signal (mobile SDK, GPS, other)?
- What is the panel size and how is it constructed?
- How are visits defined and measured?
- How are home and work locations filtered?
- What is the minimum visit threshold for a location to appear in the data?
- How is the data validated? Is there a ground truth benchmark?
Hyperlocal providers vary as much as standard POI providers, and the same evaluation discipline applies: test against your specific geographies and categories before committing.
Questions to Ask Every Vendor
Here is a checklist of questions to ask every POI data vendor you evaluate. Use these as the backbone of your RFI and POC design.
Coverage
- How many POI records do you have in [specific countries/regions]?
- How many records fall into [specific categories] in those geographies?
- What is your coverage methodology? How do you determine whether a location should be in the database?
Accuracy
- What is your coordinate accuracy? How do you measure it?
- What is your address accuracy? Do you geocode against authoritative sources?
- Can you provide accuracy benchmarks for [specific categories and geographies]?
Freshness
- How frequently is your data updated?
- What signals do you use to detect that a location has closed, moved, or changed attributes?
- What is the difference between “refreshed” and “verified” in your data?
- What is the median age of a record at the time it appears in a customer delivery?
Completeness
- What is the fill rate for [required attributes] in [target geographies and categories]?
- Can you provide fill rate statistics at the geography-category level rather than as aggregates?
Consistency
- How do you handle categorization? What taxonomy do you use?
- How do you deduplicate records? What is your process for identifying that two records represent the same location?
Lineage and Licensing
- What are your primary data sources?
- Do you use scraped web data, authoritative government sources, or both?
- What does the license permit in terms of redistribution, sublicensing, and use in derivative products?
- Are there any industry or application restrictions?
What Good Looks Like
A strong POI data provider will be able to answer all of the above questions with specific numbers, not generalities. They will offer sample data before purchase. They will support a structured POC. They will have published documentation on their methodology. They will be willing to put accuracy and freshness commitments in the contract.
Providers who respond to specific questions with vague claims (“best in class coverage,” “continuously updated”) and resist pre-purchase testing are not hiding good data. Transparency is a quality signal.
The best vendors treat your evaluation process as an opportunity to demonstrate quality, not as an obstacle to close a deal quickly. That disposition, as much as any data quality metric, tells you something about how the relationship will go post-signature.
Making the Final Decision
After running your POC with two or three vendors, you should have enough information to make a decision based on your defined criteria. Here is a simple scoring approach:
- Weight each evaluation dimension by its importance to your use case (coverage, accuracy, freshness, completeness, consistency, lineage/licensing)
- Score each vendor on each dimension based on your POC results and RFI responses
- Calculate a weighted score
- Adjust for commercial factors (price, contract terms, support quality)
Do not let price dominate the decision if a cheaper vendor scored meaningfully lower on quality dimensions that matter for your use case. The cost of bad data in production almost always exceeds the cost differential between providers.
Also factor in the relationship. POI data is not a one-time purchase; you will need updates, support, and potentially custom extractions over time. A vendor who was responsive and transparent during the evaluation process is likely to be a better long-term partner than one who was evasive.
Alternative Approaches to Sourcing POI Data
Buying from a commercial vendor is not the only option. Depending on your requirements and resources, these alternatives are worth considering.
Open data sources
OpenStreetMap (OSM) is the most comprehensive open-source geospatial dataset in the world. POI coverage in OSM is excellent in dense urban areas in developed markets and patchy in rural and emerging markets. Data quality is community-dependent. For many use cases, OSM is a useful complement to commercial data or a viable standalone source for specific geographies.
Government open data (business registrations, licensing databases, property records) is underused as a POI source. Coverage and format vary by jurisdiction, but for categories where licensing is required (restaurants, healthcare providers, financial services), government sources can be more accurate and more current than commercial aggregators.
Building your own
Some organizations build proprietary POI databases for their specific use case. This makes sense when commercial sources do not cover your required categories or geographies well, when you need attributes that commercial providers do not collect, or when data sovereignty requirements prevent using third-party sources.
Building your own is expensive and time-consuming. The ongoing maintenance burden is often underestimated. But for organizations with specific needs that commercial providers cannot meet, it can be the right choice.
Custom extraction
Some data providers offer custom extraction services: they build a specialized dataset to your specifications rather than selling you access to their standard product. This can be a good option when your needs are highly specific or when standard products require significant post-processing to be useful.
Custom extraction typically costs more than standard licensing but can result in higher quality data for your specific use case because the provider is optimizing for your requirements rather than building for the broadest possible market.
Working with POI Data After Purchase
Buying the data is the beginning, not the end. Most POI datasets require post-processing before they are ready to use in production.
Normalization
Even high-quality providers deliver data with inconsistencies in formatting, categorization, and naming conventions. Address formats vary by country. Category labels are not always consistent. Building a normalization layer that standardizes the data to your internal schema is almost always necessary.
Deduplication
If you are combining data from multiple providers or augmenting a commercial dataset with open data, deduplication becomes critical. Matching records across sources requires a combination of exact matching (on unique identifiers like phone numbers or websites) and fuzzy matching (on name and address). The deduplication problem is harder than it looks and is worth investing engineering time in getting right.
Enrichment
Commercial POI data often serves as a foundation that you enrich with proprietary attributes. A retail brand might add its own store performance data to a base POI dataset. A real estate firm might add transaction history. An insurance company might add risk scores. The base POI data provides the geographic skeleton; your proprietary data adds the analytical depth.
Change management
POI data is not static. When you receive a data update, you need a process for ingesting changes without corrupting existing records or losing proprietary enrichments. Designing a change management workflow at the start will save significant pain later.
Evaluating Providers for Specific Use Cases
The six dimensions above apply to all POI evaluations, but the relative importance of each dimension varies by use case. Here is how the evaluation criteria shift for common applications.
Retail site selection
Coverage and accuracy are paramount. You need to know where competitors are and where complementary businesses are. Coordinate accuracy matters for mapping and proximity analysis. Freshness matters for detecting competitor openings and closures. Completeness on category is more important than completeness on operating hours.
Navigation and mapping
Freshness is the most critical dimension. A navigation app that sends users to a closed location erodes trust. Completeness on hours and temporarily-closed status matters a lot. Coordinate accuracy needs to be high enough to route users to the right entrance. Coverage needs to be global if the product is global.
Financial services and insurance
Accuracy and lineage are the critical dimensions. Financial and insurance applications require data that can withstand regulatory scrutiny. Source documentation matters. Fill rates on specific attributes (business type, square footage, ownership) may be more important than breadth of coverage. Licensing restrictions for financial use cases are common and need careful review.
Consumer applications and local search
Completeness drives user experience. A local search app that is missing hours, photos, or reviews delivers a worse experience than one with complete attributes. Freshness matters for time-sensitive information (hours, temporary closures). Coverage needs to be deep in the categories and geographies relevant to your user base.
Market research and analytics
Consistency is especially important for analysis. If categories are inconsistent, cross-sectional comparisons are unreliable. Historical data availability matters if you need to track changes over time. Licensing needs to permit the derivative analytical products you plan to create.
A Note on Data Brokers vs. Primary Collectors
The POI data vendor landscape includes two broad types of providers: primary collectors who build their datasets from original sources, and data brokers who aggregate and resell data collected by others.
Primary collectors tend to have better lineage documentation and more consistent quality within their areas of strength. They also tend to be more expensive. Data brokers can offer broader coverage by aggregating multiple sources, but the quality is less predictable because it depends on the quality of underlying sources, and lineage documentation is often incomplete.
Neither model is inherently superior. A primary collector with strong coverage in your target geographies and categories is likely to outperform a broker. A broker with strong curation practices and transparent source documentation can outperform a primary collector whose coverage is thin in your areas of interest.
What matters is understanding which type of provider you are dealing with and asking the right questions accordingly. For a primary collector, ask about their collection methodology. For a broker, ask about their source mix and curation process.
Conclusion
Evaluating POI data providers is not complicated, but it requires discipline. The temptation to shortcut the process by comparing on price and record count is understandable; those numbers are easy to compare. But they are poor proxies for the quality dimensions that actually determine whether the data will work for your use case.
The teams that get this right define their requirements precisely, ask specific questions, and test with their actual use case before committing. The teams that get it wrong optimize on the wrong dimensions, skip the POC, and end up either renegotiating or switching providers after integration is already complete.
The evaluation investment is small relative to the integration investment. Spend the time up front.
About Forage
Forage is a data acquisition platform that helps teams source, evaluate, and procure structured data at scale. We work with organizations that need high-quality POI data, business data, and other structured datasets for AI training, analytics, and operations.
If you are evaluating POI data providers and want to understand your options, our team can help you define your requirements and connect you with sources that match your use case. The options include:
- Pre-existing datasets: Curated POI databases from vetted providers available for immediate licensing
- Managed sourcing: We handle vendor evaluation, negotiation, and integration on your behalf
- Custom extraction: For requirements that standard products do not meet, we can scope and commission extraction to your own specification.
