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Featured Resources

Data Quality Management: What, Why, How, and Best Practices
data quality management framework concept

Last Updated on September 26, 2024 “Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction, and skillful execution; It represents the wise...

Last Updated on September 26, 2024 “Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction, and skillful execution; It represents the wise...

Last Updated on September 26, 2024 “Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction, and skillful execution; It represents the wise...

Fuzzy Matching 101: The Complete Guide to Accurate Data Matching
data matching with DME data profiling

Last Updated on December 1, 2025 Summary: Fuzzy matching identifies records that refer to the same entity even when formats, spellings, or fields don’t match exactly. This guide is for data practitioners,...

Last Updated on December 1, 2025 Summary: Fuzzy matching identifies records that refer to the same entity even when formats, spellings, or fields don’t match exactly. This guide is for data practitioners,...

Last Updated on December 1, 2025 Summary: Fuzzy matching identifies records that refer to the same entity even when formats, spellings, or fields don’t match exactly. This guide is for data practitioners,...

Data Quality Testing – A Quick Checklist to Measure and Improve Data Quality

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!...

Top 10 Alternatives to IBM Match 360

When customer records, product SKU data, supplier/vendor details, etc. are scattered across dozens of disparate systems, employees lose an average of more than a quarter

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