Our Process and Differentiators

dataplor’s Process: The most rigorous in the geospatial data business

“dataplor's global POI data was much more accurate than other providers - up to 90% in some regions.”
Global Data Acquisition Manager, Top 5 Consumer Packaged Goods Company.

Technology

Machine Learning

Monitor and update geospatial data at scale, dynamically and 24/7.

AI Call Bots

Gather the most direct, up-to-date signals from the point of interest via quick, automated, AI driven conversation directly from the source.

Deep Learning Image Recognition

Leverage on-the-ground information, for example store signs, to expand and verify location data

ML Deduplication Process

Leverage our proprietary custom-built machine learning technology to eliminate duplicate records, a major problem with POI data.

Use Proprietary Built Data
Improvement Engine

Automatically identify incorrect geocodes, addresses, and other location attributes.

Human Capital

Local experts in every one of dataplor’s countries help verify data to ensure industry-leading accuracy.
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Our Process and Differentiators

Our major differentiators against subpar industry standards

Industry Standards

Industry Standards

dataplor Approach

Over exposure to human error
Not taking an enterprise approach to data validation
Over reliance on humans and third parties, introducing errors, inconsistencies, and unfamiliarity with the data

dataplor Approach

In-house experts for human validation
Trained Local Analysts
In House Data Experts

Industry Standards

Poor Global Data
Overblown claims about global coverage that mask incomplete and widely inaccurate global data

dataplor Approach

Data at Global Scale
Globally Scaled data across places, brands, and chains

Industry Standards

Incomplete datasets limited to specific brands
Datasets show a brand's location, but not all other places surrounding those locations

dataplor Approach

Complete picture of an area, including brands and all POI's
Leverage understanding of all locations to drive business outcomes and gain a full picture of the market

Industry Standards

Murky data verification practices
Poor accuracy and no explanation of how AI and human verification work at scale

dataplor Approach

Best-in-class data, not tangential services
Sharp focus on scalable technology to deliver high quality data
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