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Complete data quality hub

Featured Resources

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

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
data matching with DME data profiling

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!...

data management principles

8 Principles of Data Management

An average enterprise – with 200-500 employees – uses about 123 SaaS applications to digitize their business processes. With large amounts of data being generated

Merge and survivorship with DME

Merge Purge DME process of combining multiple sources of data while simultaneously removing duplicates and bad records from the data source.

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