Artificial intelligence is transforming the way location data companies collect, process, and analyze data. With the help of AI, these companies are able to gain a deeper understanding of their customers and the areas in which they operate, which in turn enables them to make more informed and efficient business decisions. 

AI’s greatest value is that it can outperform humans in many functions at macro and micro levels. At the macro level, AI can analyze enormous amounts of data quickly, making it possible to find trends and make predictions that humans would miss. At the micro level, it can sift through minute details and eliminate data duplications and other errors that might pass by human eyes. 

At the same time, the key to leveraging the power of AI is a multifaceted approach that makes use of its strengths while anticipating and accounting for its weaknesses. Black swans are the classic example. England only has white swans, so an AI trained there would conclude that every swan must be white, and consequently miss on all black ones. This points to a weakness of AI: it only knows what it has been shown, and, unlike humans, it struggles to fill in the gaps between things it does not already know. The consequences can be serious, as in the example of a self-driving vehicle that failed to brake because it identified a pedestrian and their bike as unknown objects.

That’s why dataplor employs a multifaceted approach to AI that maximizes its abilities while avoiding potential drawbacks. Below, we explore three ways in which dataplor’s deployment of AI gives it a competitive edge: drawing on deep image processing and data enhancement; analyzing data at scale to develop predictive models; and leveraging the power of computer-human synthesis.

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How dataplor uses AI to analyze images and collect and enhance data

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In addition to being worth a thousand words, pictures provide key clues as to when a business opens or closes, what kinds of beverages a restaurant offers, or whether a new location is viable in a developing area. AI systems have given new life to photos because of their capacity to analyze huge amounts of them and identify important information. Called deep image processing, this application lets dataplor draw untapped location intelligence from the multitude of photos publicly available online, photos taken by in-house human validators, and images gleaned from alternative sources and partners like dash cam footage.  

Imagine you’re a CPG executive tasked to undertake a global search for restaurants in major cities in need of a soft drink provider. Dataplor’s AI-based deep image processing can pour over photos taken by individuals from inside restaurants in Sydney, Kolkata, Bogotá, or San Antonio, scanning pics of menus, counters, door stickers, soft drink dispensers, and so on, looking for a brand name that signals a provider or lack thereof. A global CPG company can then use this intelligence to identify new business opportunities.

Another application of AI is data collection and enhancement. Dataplor uses AI call bots to get ground signals from across the globe at minimal cost. Language-trained bots call businesses to ask about hours or services, providing detailed local business data at scale. For example, AI callbots can find out a business’ hours or verify whether those hours are correct. In tandem, machine learning systems dig through this data to eliminate duplications, minimizing inaccuracies and rendering the data immediately actionable for a wide range of use cases.

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How dataplor uses AI to help businesses predict the future

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One of the more promising applications of AI is predictive analytics: the use of historical data, machine learning, and statistical algorithms to identify the likelihood of future outcomes. Location data companies use predictive analytics to forecast future customer behavior and make data-driven decisions. For example, a company can use predictive analytics to identify which neighborhoods are likely to see the most growth in the coming years and invest resources accordingly.

Photos also come into play with predictive analytics, this time as a source of historical data. An AI analysis of storefront photos from one year might reveal lots of construction and development that disappears from photos five years later, signaling a downturn not yet evident in standard economic indicators. 

AI can also crunch location data to create predictive models that help businesses make better decisions: for example, helping a retail store to determine the best location to open a new branch or aiding a hotel in predicting seasonal demand for travel accommodations. 

In addition, location data companies like dataplor use machine learning to analyze satellite imagery of a particular area to identify patterns in land use, such as the location of buildings, roads, and infrastructure. This can be used to gain insights into population density and land values, which real estate companies can use to identify areas with high potential for development or investment.

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How dataplor’s computer-human synthesis gives it a competitive edge

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Despite these benefits, AI systems can be biased or make errors, and humans are needed to detect and correct these issues. One way to do this is for humans to provide domain expertise. For example, a human with expertise in location data for the retail industry can help ensure that the AI system is properly analyzing the information it receives and making predictions that are relevant to the industry. An AI might mistake a distressed brick warehouse in the Allston area of Boston as a sign of economic decline, for example, when it’s actually a newly renovated coworking space.  

For these reasons, dataplor uses human-computer collaboration to anticipate and improve the performance of its AI systems. Specially trained human validators, with expertise across cultures and languages, act as eyes on the ground and provide real-time feedback that AI systems can use to improve. Validators spot challenges like wordplay in business names or cultural differences in how addresses are formulated and make sure that data is tagged and processed accurately.  

For all these reasons, AI will keep growing as a centerpiece of the location data industry. The edge, however, will go to the companies that know how to identify its limits and leverage human-AI collaboration to succeed.

Whether they’re looking to improve site selection, add precision to marketing, or gain insight about the competition, companies of all kinds—from real estate to retail to logistics—are dedicating considerable spend to location intelligence.

As demand for this data continues to rise, supply has followed suit. What’s more, now that the risks of free location data are more widely understood, brands are gravitating towards third-party providers whose data will grant them the granular insights needed to grow at scale without sacrificing accuracy.

 But not all providers are created equal. From the collection process to the final product, each offers a different path to location intelligence. Two features that distinguish dataplor from other location data companies are its combined use of AI and human expertise as well as its solution-forward approach to client satisfaction.

A technology-smart process backed by human know-how 

By relying on technology and human expertise, dataplor has crafted the most rigorous and global data collection process in the geospatial data business.

 Where other providers suffer from an overreliance on human sources and outdated technology (which can lead to errors and inconsistencies), dataplor employs a combination of AI, machine learning, and deep learning for collection. Thanks to our significant investment in these tools, we’re able to use AI call bots to gather up-to-date information, deep learning and image recognition to expand and verify datasets, and machine learning to update attributes and identify issues and patterns in the data.

 For example, we might establish a record with a call bot by confirming a restaurant’s phone number and operating hours. From there, we can use image recognition tools to verify on-the-ground information and, if it’s a chain, identify other locations. This enables us to avoid inaccuracies, such as including the record for a location in San Antonio, Texas in a dataset focused on Austin.

Once this data has been collected, dataplor knows precisely when and how to improve it. This is largely thanks to our multipronged approach to enhancement. Before a dataset containing the record above makes its way into a client’s hands, for instance, it will have gone through multiple stages of refinement. These could include dataplor’s custom-built machine learning technology, which eliminates duplicate records (a major issue with POI data), as well as our proprietary enhancement engine, which automatically identifies incorrect geocodes, addresses, and other faulty location attributes. 

In addition to leveraging cutting-edge technologies, we know the value of human sources. Our network of over 100 validators does the important work of last-mile verification in more than 200 countries. Each of these local experts has been trained to identify and improve POI records by ensuring that our AI machine learning tools are correctly tagging information. This allows us to offer our customers industry-leading accuracy on an international scale—a veritable rarity in today’s location data market. 

A solutions-focused approach to global intelligence

 While many companies in the geospatial data market boast a wide array of products and services, dataplor remains focused on places data alone. This enables us to ensure the quality of our product instead of spreading ourselves thin across different paths to location intelligence.

 This focus also provides dataplor’s customers with a solutions-focused resource rather than a one-size-fits-all headache. Some providers, for instance, only sell out-of-the-box data products that come as-is, which buyers need to somehow adapt to their specific needs and contexts. Unfortunately, this can lead to wasted hours, unexpected costs, and misleading insights.

 By contrast, dataplor offers customers the solutions they need when they need them, such as custom data collection and tailored datasets. To do so, we rely on our POI know-how to identify hard-to-find places or data points; recently, for example, we worked with a major manufacturing company in India to deliver a dataset focused on competitors’ EV charging stations, gas stations, parks, libraries, and other infrastructure complementary to transportation. This helped the client determine how to plan out their EV charging network and gain market share in the electric vehicle space. 

Because dataplor deals in POI data alone, we’re able to work with clients across global regions to solve problems and explore opportunities as they arise. Put otherwise, with dataplor, customers can rest assured that they won’t be left guessing with an unwieldy dataset—instead, they’ll be provided with the ongoing support necessary for truly global POI data-driven intelligence.