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Melissa Data Enables Data Quality For SQL Server Integration Services (SSIS)
The comprehensive data quality toolkit optimizes master data management to protect worldwide customer data quality over time.
Posted Oct 21, 2015
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Melissa Data, a provider of global contact data quality and integration solutions, today announced full spectrum data quality in its Data Quality Components for SQL Server Integration Services (SSIS).

Offering a comprehensive suite of data transformations for Microsoft SSIS, Data Quality Components for SSIS supports the entire data quality lifecycle for a global customer base, handling data profiling, verification, enrichment, and matching. Iit enables database administrators and SSIS developers to manage, protect, and unify data into a single version of the truth.

Data Quality Components for SSIS enables data quality operations, such as verifying U.S. and international addresses for more than 240 countries; adding rooftop lattitude/longitude coordinates to addresses; preventing fraud with identity verification; and cleansing worldwide names, emails, and telephone data. The toolkit also de-duplicates and consolidates international data.

Data Quality Components for SSIS's Profiler Transform identifies problems and weaknesses with customer data; Contact Verify, Global Verify, Personator & SmartMover Transforms work together to verify identity to prevent fraud, and correct and standardize customer data to ensure it is accurate, reliable, up-to-date, and complete. Personator Demographics, IP Locator & Property Transforms enable greater insight into customer data, providing more detailed information that improves the ability to contact customers most effectively. The MatchUp Transform flags duplicate records using powerful fuzzy matching algorithms; duplicates can be eliminated, and the system identifies and retains the best overall record to ensure a single, accurate view of each customer.

"Up to 94 percent of businesses recognize their customer data may be inaccurate, a result of not only data entry errors but also good data that has gone stale at a rate of 2 percent of customer records each month. This is a real threat to data warehousing and business intelligence initiatives, affecting the accuracy and usefulness of data used for communications, analytics, and compliance," said Bud Walker, vice president of enterprise sales and strategy at Melissa Data, in a statement. "To reduce waste, drive revenue, improve business decisions, and stay in touch with your best customers, it's essential to implement an end-to-end approach, profiling data to identify weaknesses and cleaning, enriching, and matching information to assure data is kept clean over time."


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