Best Data Quality Software for Google Maps
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Compare the Top Data Quality Software that integrates with Google Maps as of August 2025
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This a list of Data Quality software that integrates with Google Maps. Use the filters on the left to add additional filters for products that have integrations with Google Maps. View the products that work with Google Maps in the table below.
What is Data Quality Software for Google Maps?
Data quality software helps organizations ensure that their data is accurate, consistent, complete, and reliable. These tools provide functionalities for data profiling, cleansing, validation, and enrichment, helping businesses identify and correct errors, duplicates, or inconsistencies in their datasets. Data quality software often includes features like automated data correction, real-time monitoring, and data governance to maintain high-quality data standards. It plays a critical role in ensuring that data is suitable for analysis, reporting, decision-making, and compliance purposes, particularly in industries that rely on data-driven insights. Compare and read user reviews of the best Data Quality software for Google Maps currently available using the table below. This list is updated regularly.
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Semarchy xDM
Semarchy
Use Semarchy unified data platform to experience xDM. Discover, govern, enrich, enlighten and manage data. You can easily transform data into insights with xDM and rapidly deliver data-rich applications with automated master data management. Its business-centric interfaces provide for rapid creation and adoption of data-rich applications, while automation rapidly generates applications to your specific requirements. Use the agile platform to quickly expand or evolve data applications. -
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NetOwl NameMatcher
NetOwl
NetOwl NameMatcher, the winner of the MITRE Multicultural Name Matching Challenge, offers the most accurate, fast, and scalable name matching available. Using a revolutionary machine learning-based approach, NetOwl addresses complex fuzzy name matching challenges. Traditional name matching approaches, such as Soundex, edit distance, and rule-based methods, suffer from both precision (false positives) and recall (false negative) problems in addressing the variety of fuzzy name matching challenges discussed above. NetOwl applies an empirically driven, machine learning-based probabilistic approach to name matching challenges. It derives intelligent, probabilistic name matching rules automatically from large-scale, real-world, multi-ethnicity name variant data. NetOwl utilizes different matching models optimized for each of the entity types (e.g., person, organization, place) In addition, NetOwl performs automatic name ethnicity detection as well.
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