How dataPlor Closes Small Business Data Gaps

When dataPlor evaluated Google’s business data in Mexico. Both Google and dataPlor executives were shocked to learn that 75% of businesses in Mexico were not listed in Google’s search results or internal data set

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When you look up a business in Google, is the address precisely located in the search results? If you plan to visit this business, would you trust the address?

Or, you may have searched “nearby convenience stores” on Google, and a list of all the stores near you pops up. You would likely expect these to be all the options nearby, right?

Most people would say yes to these questions. We inherently trust large search engines and big data to provide us with the answers we need, without hesitation and few questions asked. However, these quick and accurate results are not always typical.  Large data companies like Google may not always have all the information on businesses or the correct information,  whether they know it or not. This is especially important in emerging markets such as Mexico and throughout Latin America.

Case Study #1: Gaps In Google's Small Business Data

Take Google for example. In 2018, dataPlor evaluated Google’s business data in Mexico. Both Google and dataPlor executives were shocked to learn that 75% of businesses in Mexico were not listed in Google’s search results or internal data sets. When a potential customer searched for a product or service, many qualified companies did not appear in the search results. Potential customers have had no knowledge of these businesses.

Small businesses need as many customers as possible to survive. Both tourists and locals depend on Google search results to find what they are looking for. When not online, these businesses are missing a large portion of their potential customer base. What’s more, customers are not being exposed to all of their potential options and may not find the business that suits their needs.


At dataPlor, we bring these small businesses online, helping them thrive. Our field team are on the ground recording and verifying the locations and information for these businesses. Dataa uncovered was information was used to improve Google’s search results in Mexico.

Case Study #2:  Location Data For Large Ridesharing Company

In addition to proving data sets to large data companies like Google, dataPlor provides exact location data to another app-related convenience we've all come to know and rely on, a large rideshare company. When customers would type in a business name, the ridesharing app could not locate exactly where to drop the customer off. Typically, the driver’s directions would be off from a few storefronts to a few blocks, creating widespread inconvenience for both customers and drivers.

Our field team records the best pick up and drop off coordinates for a number of small- and medium-sized business throughout Mexico, eliminating the issue. Like Google, this ridesharing company was surprised to learn how much of their data was flawed.

Conclusion: Closing The Small Business Data Gap

dataPlor’s unique business model---using humans and artificial technology verify small- and medium-sized business information that is then triple verified---played a strong role in helping these companies solve their data problems.

It’s clear that a lack of connection between technology and information is prevalent in Mexico. dataPlor strives each day to close this gap and strengthen the economies of emerging markets. 45,000 strong, they bring the human element necessary for properly collecting the information. No matter how powerful given data is, it does not measure up to the strength of a real person talking with a shop owner or discovering the best door to enter a restaurant.

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