DataMatch
DataMatch Enterprise™ solution is a highly visual data cleansing application specifically designed to resolve customer and contact data quality issues. The platform leverages multiple proprietary and standard algorithms to identify phonetic, fuzzy, miskeyed, abbreviated, and domain-specific variations. Build scalable configurations for deduplication & record linkage, suppression, enhancement, extraction, and standardization of business and customer data and create a Single Source of Truth to maximize the impact of your data across the enterprise.
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Data8
Data8 offers a comprehensive suite of cloud-based data quality solutions designed to ensure your data is clean, accurate, and up-to-date. Our services encompass data validation, cleansing, migration, and monitoring, tailored to meet specific business needs. Data validation services include real-time verification tools for address autocomplete, postcode lookup, bank account validation, email verification, name and phone validation, and business insights, all aimed at capturing accurate customer data at the point of entry. Data8 helps improve B2B and B2C databases by offering appending and enhancement services, email and phone validation, data suppression for goneaways and deceased individuals, deduplication and merge services, PAF cleansing, and preference services. Data8 is an automated deduplication solution compatible with Microsoft Dynamics 365, designed to dedupe, merge, and standardize multiple records efficiently.
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NetOwl NameMatcher
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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Zuar Runner
Utilizing the data that's spread across your organization shouldn't be so difficult! With Zuar Runner you can automate the flow of data from hundreds of potential sources into a single destination. Collect, transform, model, warehouse, report, monitor and distribute: it's all managed by Zuar Runner.
Pull data from Amazon/AWS products, Google products, Microsoft products, Avionte, Backblaze, BioTrackTHC, Box, Centro, Citrix, Coupa, DigitalOcean, Dropbox, CSV, Eventbrite, Facebook Ads, FTP, Firebase, Fullstory, GitHub, Hadoop, Hubic, Hubspot, IMAP, Jenzabar, Jira, JSON, Koofr, LeafLogix, Mailchimp, MariaDB, Marketo, MEGA, Metrc, OneDrive, MongoDB, MySQL, Netsuite, OpenDrive, Oracle, Paycom, pCloud, Pipedrive, PostgreSQL, put.io, Quickbooks, RingCentral, Salesforce, Seafile, Shopify, Skybox, Snowflake, Sugar CRM, SugarSync, Tableau, Tamarac, Tardigrade, Treez, Wurk, XML Tables, Yandex Disk, Zendesk, Zoho, and more!
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