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Featured Resources

Data quality management: What, why, how, and best practices

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. William A. Foster...

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. William A. Foster...

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. William A. Foster...

Fuzzy Matching 101: Cleaning and Linking Messy Data
fuzzy matching

In this blog, we will take an in-depth look at fuzzy matching, the go-to approach for data deduplication and record linkage. We will cover: What is Fuzzy Matching? Why Do Businesses Need Fuzzy Matching?...

In this blog, we will take an in-depth look at fuzzy matching, the go-to approach for data deduplication and record linkage. We will cover: What is Fuzzy Matching? Why Do Businesses Need Fuzzy Matching?...

In this blog, we will take an in-depth look at fuzzy matching, the go-to approach for data deduplication and record linkage. We will cover: What is Fuzzy Matching? Why Do Businesses Need Fuzzy Matching?...

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!...

The Challenges of ICD-10

Changing the diagnosis and procedure coding, or ICD-10, is on the horizon in healthcare. The sheer size and cost of the massive project is challenging

Data Cleansing Techniques for Redundancies

Dealing with duplicate data requires a strategy to deal with inconsistent data. The first step would be to standardize addresses with data matching software.  Secondly,

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