The Solution to Large Quantities of Data

Every successful business scales and grows. As a result, so does its database. And as data grows so do problems including: faulty customer entry, breakdowns in data migration, disparate upstream legacy, and big data systems. All of these problems lead to one thing: bad data. High quality data is key to uncovering meaningful insights and new opportunities. Bad data, on…

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New! Data Profile & Monitor Video

The first step to gaining more business value from your customer data is to analyze the data you have, and determine what condition it's in. Data profiling from Melissa Data helps you discover existing weaknesses in your database so you can create and enforce business rules on incoming records to maintain data quality. Profiling is an important step in Melissa Data's…

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Introducing the New and Improved Global Email Web Service

How It Can Protect and Increase Your Email Reputation Score By Oscar Li, Data Quality Sales Engineer/Channel Manager for Global Email Melissa Data recently introduced several improvements and new features to its Global Email Web Service - an all-in-one real-time email mailbox validation and correction service. Here's a quick list of our latest improvements:Improved fuzzy matching of domain correctionsUpdated our…

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Melissa Data Completes the Data Quality Circle with Addition of Profiler Tool for SQL Server Integration Services

Flagship SSIS Developer Suite Now Enables Data Assessment and Continuous Monitoring Over Time; Webinar Adds Detail for SSIS Experts Rancho Santa Margarita, CALIF - March 17, 2015 - Melissa Data, a leading provider of contact data quality and address management solutions, today announced its new Profiler tool added to the company's flagship developer suite, Data Quality Components for SQL Server…

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Data Profiling: Pushing Metadata Boundaries

By Joseph Vertido Data Quality Analyst/MVP Channel Manager Two truths about data: Data is always changing. Data will always have problems. The two truths become one reality--bad data. Elusive by nature, bad data manifests itself in ways we wouldn't consider and conceals itself where we least expect it. Compromised data integrity can be saved with a comprehensive understanding of the…

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Validation of Data Rules

By David Loshin Over the past few blog posts, we have looked at the ability to define data quality rules asserting consistency constraints between two or more data attributes within a single data instance, as well as cross-table consistency constraints to ensure referential integrity. Data profiling tools provide the ability to both capture these kinds of rules within a rule…

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More About Data Quality Assessment

By David Loshin In our last series of blog entries, I shared some thoughts about data quality assessment and the use of data profiling techniques for analyzing how column value distribution and population corresponded to expectations for data quality. Reviewing the frequency distribution allowed an analyst to draw conclusions about column value completeness, the validity of data values, and compliance…

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Data Quality Assessment: Value Domain Compliance

By David Loshin To continue the review of techniques for using column value analysis for assessing data quality, we can build on a concept I brought up in my last post about format and pattern analysis and the reasonableness of data values, namely whether the set of values that appear in the column complies with the set of allowable data…

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Data Quality Assessment: Value and Pattern Frequency

By David Loshin Once we have started our data quality assessment process by performing column value analysis, we can reach out beyond the scope of the types of null value analysis we discussed in the previous blog post. Since our column analysis effectively tallies the number of each value that appears in the column, we can use this frequency distribution…

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Data Quality Assessment: Column Value Analysis

By David Loshin In recent blog series, I have shared some thoughts about methods used for data quality and data correction/cleansing. This month, I'd like to share some thoughts about data quality assessment, and the techniques that analysts use to review potential anomalies that present themselves. The place to start, though is not with the assessment task per se, but…

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