Fuzzy Matching and Entity Resolution
It’s probably pretty clear by now that people and company names don’t match, and that mismatching data makes it very difficult to find duplicates (or to link/sync/relate the same records within and across different data sources). This is really the core of master data management but it’s also data quality, and it’s part of everyday […]
Industrial Strength Data Match and Entity Resolution Systems
Python users probably know data matching by the name “string matching”. Excel users probably use the “v lookup” and the “fuzzy lookup” functions. Business people will just tell you that there are too many duplicates and that they can’t find the same data in other business systems. Almost every system has their own version of […]
Complete Guide to Fuzzy/Probabilistic Data Matching and Entity Resolution
Introduction Fuzzy or probabilistic data matching and entity resolution are fundamental processes in data management and analytics. They involve identifying and linking records that refer to the same entity but may have variations due to errors, abbreviations, or inconsistencies. This comprehensive guide delves into the various aspects of fuzzy matching and entity resolution, including different […]
CRM Migration: How to Prepare Your Data for Seamless Transition
Migrating to a new Customer Relationship Management (CRM) system is a significant step for businesses aiming to enhance operational efficiency and customer engagement. However, the success of this transition heavily depends on the quality and readiness of your data. Proper data preparation ensures that your information is accurate, reliable, and compatible with the new platform.matchdatapro.com […]
Music Royalty Collections – MDP
The Challenge: Unmatched Data and Lost Royalties In the digital age, the music industry faces significant challenges in accurately tracking and distributing royalties. Discrepancies in metadata, such as inconsistent artist names, song titles, and rights-holder information, lead to unmatched data. This issue results in royalties being held in “black boxes,” where funds are collected but […]
The Power of Record Linkage: Enhancing Data Integrity with Match Data Pro
Record linkage is an essential process in data management, especially when merging datasets from multiple sources to identify records that refer to the same entity. From healthcare to marketing and education, organizations rely on accurate record linkage to maintain clean, unified data systems. However, managing this process can be challenging, especially with inconsistent, incomplete, or duplicated […]
Why MDP is the Smarter Choice for Data Matching and Deduplication
In today’s data-driven world, businesses face a common challenge: managing duplicate records and ensuring data accuracy across various systems. While some organizations attempt to build their own data matching solutions using SQL or JavaScript, these homegrown approaches often fall short in terms of deployment time, accuracy, and long-term investment. This article compares Match Data Pro (MDP) with traditional homegrown solutions to highlight why […]
Top 10 ways Match Data Pro is an easier way to clean, match, and merge data
Top 10 ways Match Data Pro is an easier way to clean, match, and merge data No need to contact us or to request anything to start using Match Data Pro. You can start testing free and anonymously without any registration, right from our homepage. It’s self-service so you can register for a free account […]
Why Advertising Agencies Need Fuzzy Data Matching for Smarter Campaigns
Why Advertising Agencies Need Fuzzy Data Matching for Smarter CampaignsIn the fast-paced world of advertising, precision is everything. Agencies and marketing departments thrive on delivering hyper-targeted, data-driven campaigns. However, the challenge is that customer data often resides in multiple, disconnected systems—CRM platforms, social media analytics, sales databases, and more. Inaccurate or inconsistent data can lead […]
Why Insurance Companies Need Fuzzy Data Matching for Smarter Operations
Why Insurance Companies Need Fuzzy Data Matching for Smarter OperationsThe insurance industry runs on data—policyholder records, claims history, underwriting information, and customer interactions. However, this data is often scattered across multiple systems, from CRM platforms and underwriting databases to claims processing tools and customer support logs. When records don’t perfectly match across systems due to […]