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Featured Resources
“Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction, and skillful execution; It represents the wise choice of many alternatives.”...
“Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction, and skillful execution; It represents the wise choice of many alternatives.”...
“Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction, and skillful execution; It represents the wise choice of many alternatives.”...
In 2003, the Social Security Administration (SSA) and the Department of Transportation (DOT) launched a joint investigation into the possible misuse of Social Security Numbers by airline pilots. The two...
In 2003, the Social Security Administration (SSA) and the Department of Transportation (DOT) launched a joint investigation into the possible misuse of Social Security Numbers by airline pilots. The two...
In 2003, the Social Security Administration (SSA) and the Department of Transportation (DOT) launched a joint investigation into the possible misuse of Social Security Numbers by airline pilots. The two...
Don't wait for a data migration event to test your data quality. Perform data quality tests now before it gets too late. Here's everything you need to know!...
Don't wait for a data migration event to test your data quality. Perform data quality tests now before it gets too late. Here's everything you need to know!...
Don't wait for a data migration event to test your data quality. Perform data quality tests now before it gets too late. Here's everything you need to know!...
The Truth About Data as a Service (DaaS): Why It All Breaks Without Data Matching
Everyone’s Talking About DaaS, Few Are Ready for It The concept of Data as a Service (DaaS) is having its moment. On paper, it’s easy
Data Ladder vs. Experian Data Quality: Which One Is the Better Fit for Transparent and Accurate Record Matching?
Data quality is no longer a luxury. As organizations grapple with fragmented systems, duplicate records, and poor data trust, the debate isn’t just about who
Why Data Ladder Outperforms Alteryx for Data Matching and Cleansing
Alteryx built its reputation as a powerful analytics and automation platform. But when the problem isn’t just workflow inefficiency – it’s unreliable, inconsistent, duplicated, or
Big Data Analytics Is Booming – But Is Your Data Ready for It?
Amazon generates 35% of its revenue from data-powered recommendations. Netflix enjoys an 89% retention rate by personalizing every experience using viewer behavior, preferences, and interaction
Data Ethics in the Age of AI: Why Responsible Matching Matters More Than Ever
When AI systems deliver inaccurate or inequitable results, many people immediately assume that something went wrong in the algorithms. Rarely do we look upstream –
Data Ladder vs Melissa Data: Choosing the Right Tool for Accurate, Actionable Data
Melissa Data is known for one thing: address verification. If that’s all you need, it might seem like a solid choice. But most teams aren’t
Data Ladder vs. Informatica: Why a Purpose-Built Matching Solution Wins
Informatica does everything – but data matching often takes a back seat to its broader MDM functions. For organizations that need fast, accurate, and scalable
Why Data Interoperability Challenges Are Holding Back Your Insights – And What to Do About It
Every modern organization thinks it’s interoperable—until the data says otherwise. Your systems are technically connected. APIs are firing. Data is flowing. But when you zoom
Why Data Governance and Stewardship Are the New Cornerstones of Business Strategy
64% of organizations manage at least 1 petabyte of data and 41% manage at least 500 petabytes. But fewer than half (49%) of data practitioners
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