Geographic information systems (GIS) have changed how companies from nearly every sector navigate the global market. These systems grant organizations increased competitive intelligence by enabling them to layer, map, manage, and analyze different types of data at the click of a button.

Among the greatest insights made possible by GIS platforms are those rooted in point of interest (POI) data. Having the right POI data is particularly important for two types of businesses: those that provide GIS navigation services (including Google Maps and Apple Maps) and those that leverage such services to make customers’ day-to-day lives easier (think Uber or DoorDash). But how?

In this article, we’ll answer this by first diving into how GIS mapping works. We’ll then discuss how accurate POI data helps companies increase customer satisfaction, avoid costly errors, and scale globally. Along the way, we’ll cover the true costs of bad GIS data and why dataplor is an industry-leading provider of location intelligence for GIS services of all kinds. 

What is GIS?

GIS visually layers one or more datasets so that users can analyze each in isolation or together. In other words, GIS platforms link these layers to a map by combining location data with various forms of descriptive data. 

These maps provide interactive business insights that raw data cannot generate on its own. GIS map layers allow organizations to see and toggle between different relationships, patterns, and geographic contexts found in their data. By analyzing GIS layers in this way, companies can unlock operational know-how and smarter decision making in real time. 

How does location intelligence support GIS platforms?

Though they enable users to visualize more than one type of data, geographic information systems run primarily on location intelligence. This data is the backbone of GIS mapping, since it allows these systems to feature layers focused on specific geographic contexts. 

For example, a GIS map could contain a polygon layer for France, an additional one for Paris, then yet another for popular tourist and shopping neighborhoods, such as the Champs Elysées or Marais. The same map might also contain additional layers that detail clusters for demographic skew for these sought-after destinations.     

From a business standpoint, this GIS data is crucial for capturing market share. Through it, users are afforded granular insights about points of interest, including their addresses, hours of operation, websites, and phone numbers. As a result, companies can use GIS platforms that contain POI layers to boost competitive advantage, operational savings, and customer satisfaction.   

To better understand this, let’s zoom out to consider what GIS providers themselves stand to gain with POI data. GIS mapping companies such as Apple Maps need up-to-date POI layers so that users can access and navigate their way to and from any point of interest, including restaurants, shops, parks, and other popular landmarks. 

Imagine, for example, that a tourist using Apple Maps wants to visit a new streetwear brand’s flagship store on the Champs Elysées after a morning of site seeing around the Arc de Triomphe. If the platform has an accurate POI layer, they’ll be able to find reliable transportation to the store and arrive at a time that they know it to be open. Each experience like this leads to repeat use and supports consumer confidence in the GIS provider.

Companies that rely on GIS mapping and navigation also benefit from POI integration—so much that they’ll pay premiums for access to platforms that run on the right geospatial data. Remember our tourist? Hungry after a day of sightseeing and shopping, they decide to order from the hippest restaurant in the Marais using UberEats. Whether the app is relying entirely on another GIS or has integrated additional POI datasets to optimize its algorithms, customer satisfaction hinges on data accuracy: the courier will need the correct addresses, hours of operation, and phone numbers to make sure that the meal delivery is executed seamlessly. 

What are the costs of bad GIS data?

Unfortunately, not all data is created equally. While free or out-of-the box data solutions might be less expensive, they’re often out-of-date and lead to unexpected spending down the line. POI datasets that are missing address details, contain inaccurate details about hours of operation, or suffer from duplicate records need to be enhanced if they’re to be reliably integrated into GIS platforms. And on top of all that,  much international POI data is simply inaccurate.

The results can be disastrous when GIS platforms integrate bad data. Imagine that Apple Maps has the wrong Parisian arrondissement listed for the streetwear brick-and-mortar on the Champs Elysées or incorrect hours of operation for the restaurant in the Marais. Errors like these cost time, money, and have the potential to do irreparable damage to brand image and consumer confidence. For GIS providers or the companies that rely on their services, these consequences might also stifle efforts to expand globally. 

Mapping international growth with dataplor

To avoid these costly errors, it’s important to only use POI data from vendors that 1) specialize in POI data, 2) streamline their places datasets using multiple sources, 3) provide metadata and other indicators for every record, and 4) know the value of local sources.

Thankfully, dataplor checks every one of these boxes. As an industry leader in location intelligence, we offer best-in-class POI data that GIS companies of all kinds can mobilize to gain truly global competitive intelligence. That’s because we use a winning combination of technology—including proprietary AI and machine learning—along with human verification to ensure that your mapping and navigation is always accurate.

Ready to take your GIS mapping to the next level with POI data? We’d love to hear from you!  

Many businesses use Google Places API to request location data and imagery about points of interest and other locations. The appeal is obvious; with products like Maps and Earth, Google has insight into location data around the globe. But for businesses hoping to use location data to expand, refine ad targeting, or conduct market research, Google Places API is not as cost-effective or accurate as it seems. 

Many users quickly discover that Google Places API is more costly and restrictive than they’d hoped. In fact, when you dive into the details of Google Places API pricing, it’s clear that this solution is in fact a barrier to developing location intelligence at scale.

 To help you make the best decision for your needs, this article will focus on the benefits of location intelligence, how Google Places API pricing works, and the true costs and restrictions of Google Places API. We’ll then dive into alternatives to the Places API.

The benefits of location intelligence

Companies can capitalize on geospatial insights regardless of their industries. From third-party logistics to quick-service restaurants and real estate, location data is fueling growth for businesses of all sizes and types. This data enables companies to conduct more comprehensive market research and optimize upstream supply chain opportunities and expansion plans. It also opens the floodgates for highly targeted and effective advertising.

When researching site selection, location intelligence reveals the physical footprint of competitors in any given market, as well as point of interest (POI) data that aids in the evaluation of complementary businesses or attractions that could generate customers. To increase the odds of acquiring these customers, companies can use location data to create geo-targeted mobile advertising campaigns and purchase out-of-home (OOH) ad space to deliver relevant impressions.

The hidden costs and restrictions of Google Places API 

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With so many tantalizing business use cases for location data, companies are eager to start collecting and acting on it. One popular platform is the Google Places API, which seems on the surface to be an easy, cost-effective way to unlock the benefits of geospatial insights. However, the Places API has significant weaknesses—and hidden costs.

Users of the Places API receive a $200 credit each month toward their requests. According to Google Places API pricing, that’s equivalent to 28,500 maploads per month. But not all queries are created equal, as the Places API uses a stock keeping unit (SKU) system to classify different services and their corresponding rates. As a result, companies can rack up bills that are much higher than the monthly credit, depending on which type of Google Places API data they’re accessing—maps, routes, or places.

Because market research often requires multiple calls to the API to collect the full scope of required information, companies in the midst of expansion are most at risk of overspending on the Places API. For example, Passenger Coffee, a regional brand in central Pennsylvania, could plan to expand with new distribution partners and a physical cafe in Philadelphia. Passenger’s team would likely be searching for information beyond basic place attributes, such as contact information or operating hours—all of which would require individual calls to the API. If they use the Places API to pull POI datasets for research on site selection, competitive presence, and potential distribution partners, they could quickly end up exceeding the $200 monthly credit. This could prove challenging for smaller brands, where every dollar counts.

Additionally, limitations of the Google Places API can make this research even more challenging. Storing data in any capacity for more than 30 days is a violation of Google’s terms of service (TOS). So, the monetary investment could be significant for a smaller CPG brand, yet the data they collect will vanish quickly in the shadow of the Google TOS. Similarly, tech giants like Google have the power to change their licensing at will. This leaves companies at the mercy of a large corporation, adding an unnecessary element of risk to any strategy driven by Google data.

On a more technical level, data from the Google Places API can’t be used to build iterative data products, and cannot be transferred for use in other company initiatives. The service doesn’t account for duplicate records nor does it provide tailored customer service for the Places API product. While this API is a step up from open source data that is often outdated and rife with errors, unpredictable charges and limitations to scalability makes the Google Places API a poor fit for many businesses. 

Trusting dataplor as a partner for truly global intelligence 

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The point of this article is not to rake Google Places API over the coals. Google is a trusted data source for millions of users around the world. But it’s simply not the best fit for companies that need high volumes of accurate, comprehensive location data, and a more tailored customer experience as they gather global intelligence. 

dataplor is in many ways the opposite of Google Places API. Our data is meticulously curated and vetted for accuracy by humans around the world. Global POI datasets updated in near real-time ensure that companies are getting their hands on the most up-to-date and enhanced insights as they pursue expansion plans. 

Companies may be able to extract raw location data from Google, but only dataplor can categorize U.S. and international locations by type, such as restaurants and hospitals. This includes 13,000 brands and chains identified by 35 unique attributes in 5,200 categories. dataplor POI datasets contain 200 million POIs in more than 200 countries and territories.

Our competitors have a narrow focus on the U.S. and Canada, but dataplor’s capabilities are global — that’s always been our identity. The extent of our solutions-focused services, our commitment to working with customers on their datasets, and the quality of our international data make dataplor the singular global partner for location intelligence. 

One of the most pressing challenges for companies hoping to capitalize on location data is data quality. A dataplor analysis of open-source Mexican data, for example, found that more than 70% of point of interest records contained inaccuracies. For example, a business’ record might have included an incorrect address or multiple different addresses. These inaccuracies may sound minor, but at scale, they can lead to poor decisions that cost companies using location data for logistics, site selection, and advertising millions of dollars.

This is why the most rigorous location data companies don’t just use machines to collect data at scale; they also use machines — and people — to verify it. Dataplor, for example, hires local experts to further validate the accuracy of data that has been collected by AI call bots and deduplicated with machine learning.

But what exactly does human validation add to the location data collection and verification process? What is the industry standard, how does human validation exceed it, and for what sort of scenario is human validation most useful?

Here’s how human validation helps dataplor provide the most accurate possible POI data.

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How most location data companies collect information

Businesses and third-party providers are increasingly relying on geospatial intelligence to power their predictive modeling, expansion plans, and evaluations of market trends and competition. Location data companies provide this placed-based intelligence by compiling location information from a wide range of sources. This includes anonymized and aggregated data gleaned from mobile devices, applications, and POS and ad services. The result is rich datasets available for a wide range of potential use cases.

Across the industry, location data companies employ machine learning technology and AI to identify and analyze location data. Oftentimes, companies advertise the fact that they also engage human capital — but the industry standard for doing so isn’t always clear. 

Relying too much on human capital can result in overexposure to human error; in other cases, human validators add wasteful, imprecise, or inefficient complications rather than bolster existing tech. This can happen when companies neglect an enterprise approach that quickly and effectively integrates human capital with ML and AI processes and emphasizes consistent data management standards. 

In other words, human validation can be an essential part of the location data quality enhancement process. But most companies use it minimally or optionally. For example, they might allow academics to use their data for free and point out issues and errors. But this isn’t a proactive approach; it relies on the possibility that academics will find mistakes and correct them. Other companies hire a very select group of people to walk a small area and gather on the ground data. But that data is often interpolated to other areas, which is a highly assumptive, inaccurate approach.

A better approach would be to start with scalable, high-coverage, high-accuracy data and improve it even more with experts who are employed directly to systematically upgrade it. 

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Why dataplor uses human validators

Technology drives 90% of dataplor’s approach to data collection, and human capital supplies the final 10%. What does this look like, and why does it lead to data that is more accurate and usable at scale? 

In short, human capital plays the essential role of fine-tuning tech-based data collection, ensuring that information is consistent and accurate. For example, many countries have different standards for information like zip codes, phone numbers, and street names. Plus, translation issues can lead to inaccuracies in data, like when an AI analysis might mistake a grocery store for a hardware store because of differences in local language or dialect. Dataplor’s human validators anticipate these issues for their local areas and use proprietary tools to fix inaccuracies and tag data based on its quality. 

The result is a combination of the power of machine-driven data collection at scale with the knowledge and innovation of local experts to ensure quality.

How human validation improved the accuracy of cafe identification in Japan 

For an example of a human validator increasing data accuracy, take Nel Ferrer, a regional operations manager at dataplor. Nel came to dataplor after working in the tech and finance industries, where he specialized in cross-cultural collaboration among data-oriented teams. He runs a multicultural group of validators focused on POI-related issues. One of their key contributions, Nel says, is “ensuring that AI is correctly tagging information in different places and that this information is 100% correct.” Specifically, much of his team’s current focus is on “how local culture affects the POI address structure,” which they do by providing what he calls a “human touch” that makes sure that AI is “perceiving the environment as consistently and correctly as possible.” In this way, Nel’s validators are quite literally AI’s eyes on the ground. 

Nel’s role also includes training and auditing the ongoing work of his validators. Training is one on one and walks validators through the process of understanding what to expect and how to handle various scenarios when in the field. Nel works in constant communication with his team to answer questions and solve problems as they arise. He also provides an additional level of review to the data collection and enhancement process by reviewing his team’s work weekly on an individual level. For example, he’ll choose five to ten random POI locations and make sure they are correctly tagged and free from duplications. 

Asked about a recent instance where his team was able to fix an inaccuracy, Nel recalls an example in Japan, where cafes with the word “cat” (“neko”) in their business name were being mislabeled as pet stores. 

This kind of advanced training of and detailed fixes from human capital add up to accurate, trustworthy, and actionable datasets. Location data can be consistently tagged and cross-checked, and automated identification processes can quickly correct for mistakes via validator feedback. 

The result is location data with global reach and local distinction. Dataplor’s approach to leveraging the additive benefit of human validators like Nel and his team make it possible to provide on-point geospatial intelligence at the scale that international businesses need.