How to Achieve Accurate Senzing Entity Resolution in 2025
How to Achieve Accuracy with Senzing Entity Resolution in 2025 Entity resolution is at the heart of clean, trusted data.Whether you manage customer records, citizen data, or donor lists, you need to identify when different records actually represent the same person, organization, or location. In 2025, one of the most recognized technologies for this task […]
2025 Guide: Why AI Data Matching Fails and How to Fix It
2025 Guide: Why AI Data Matching Failsand How to Fix It Artificial intelligence is changing the way we manage data—but when it comes to large-scale data matching, many organizations are discovering a painful truth: AI alone isn’t enough. Too often we see teams upload millions of records into a large language model (LLM) and hope […]
Complete Guide to Fuzzy Matching Donor Lists for Non-Profits
Complete Guide to Fuzzy Matching Donor Lists for Non-Profits When you run multiple fundraising events every year—galas, auctions, online drives—your donor data can quickly get messy.The same supporter might appear as Jonathan Smith in an event sign-in sheet, Jon S. in an online donation form, and J. Smith in a newsletter signup. If your team […]
AI Data Cleansing for Businesses: Faster, Smarter, Better
AI Data Cleansing for Businesses: Faster, Smarter, Better Why Clean Data Is the Foundation of AI Success Bad data is expensive. Every duplicate record inflates costs. Every formatting inconsistency slows down matching. And every placeholder entry reduces confidence in your reports. When businesses try to scale analytics or adopt AI, these issues only get worse. […]
Beyond FuzzyWuzzy: A Better Way to Match and Clean Data
Beyond FuzzyWuzzy: A Better Way to Match and Clean Data The Hidden Pain of String Matching On the surface, matching strings looks simple. You take two names, run a string similarity check, and get a score that tells you if they’re the same. For that reason, many teams start with open-source Python libraries like FuzzyWuzzy […]
How to Eliminate Duplicate Voter Records with Match Data Pro
How to Eliminate Duplicate Voter Records with Match Data Pro Accurate voter registration data is essential for organizations managing elections, membership programs, or political outreach. Outdated addresses, duplicate records, and incomplete contact details can lead to costly inefficiencies and errors in communication. Match Data Pro (MDP) is the AI-ready data quality platform that ensures your […]
How to Make Your Business AI-Ready
How to Make Your Business AI-Ready in 2025: Data Cleansing, Profiling & Matching Becoming “AI-ready” isn’t just about installing machine learning models. The quality of your data is the foundation. Without that, your AI will fail or deliver inconsistent results. In this 2025 guide, we’ll walk you through how to profile your data, use AI-driven […]
What’s the Best Way to Match Data Across Multiple Fields?
What’s the Best Way to Match Data Across Multiple Fields? Data rarely lives in perfect form. It’s messy, fragmented, and often scattered across multiple fields—especially when you’re working with millions of records. Matching data across columns like main_phone, mobile_phone, office_phone, and fax_phone shouldn’t feel like solving a logic puzzle. But for many data analysts, it […]
Ultimate List of the Best Data Cleansing Tools in 2025
Ultimate List of the Best Data Cleansing Tools in 2025 Looking to clean, normalize, and match your data in 2025? This ultimate list evaluates top tools to help you make informed decisions. Match Data Pro leads the pack—but you’ll discover what makes each contender unique. 1. Match Data Pro® — Best All-In-One Cleansing & Matching […]
What’s the Best Way to Cleanse and Standardize Address Data
What’s the Best Way to Cleanse and Standardize Address Data We’ve worked with address data for years—some of it clean, most of it not. If you’ve ever dealt with a messy spreadsheet full of inconsistent addresses, you know how painful it can be. You’re not alone. Address data is notoriously difficult to get right, especially […]