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MatchUp3 Component for SQL Server

Get cleaner data. Make better decisions.  

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Don’t rely on just the stock fuzzy lookup and fuzzy grouping components for SSIS. Manually crafting rulesbased matching, is an arduous and difficult undertaking, requiring painstaking planning and development, and still does not account for many specific matching criteria. Don’t reinvent the wheel. Build your data matching routines around MatchUp3 for SSIS, which makes use of advanced fuzzy matching algorithms, coupled with deep domain knowledge, to granularly identify matches between names and nicknames, street/alias addresses, companies, cities, states, postal codes, phones, emails, and other contact data components.

Benefits: Features: MatchUp employs state-of-the-art fuzzy matching algorithms, including:

  • Exact
  • Jaro
  • Jaro-Winkler
  • n-Gram
  • Levenshtein
  • Needleman-Wunch
  • Smith-Waterman
  • Smith-Waterman-Gotoh
  • Dice’s Coefficient
  • Jacard
  • Overlap
  • Longest Common String
  • Soundex
  • Phonetex
  • Frequency
  • Frequency Near
  • Containment
  • MD Keyboard

Fuzzy Matching Component - Matching, Options, source and fields example

Identify Obvious and Not So Obvious Duplicates
MatchUp’s advanced fuzzy matching algorithms can identify obvious duplicates like:

MatchUp Component for SSIS - Obvious Duplicate Match Example

And not-so-obvious duplicates like:

MatchUp Component for SSIS - Not Obvious Duplicate Match Example

MatchUp 3 enables powerful matching techniques:

Golden Record Solution
The final step in Record Matching and De-Duplication is the process of Golden Record Selection. Golden Record Selection allows for automatically selecting the best and most accurate record in a group of duplicate records, to be selected as the single best and unique representation of that entity in your data. There are 4 main options for how a golden Record is selected: Last Updated Record, Most complete Record, Custom Expression/Rules, and most important of all, Data Quality Score. It offers the ability to select the Golden Record based off the actually quality and goodness of your Contact Records by looking at each records’ Data Quality Score – a functionality that is unique to Melissa Data’s MatchUp..

Householding is a concept that allows you to group data by pre-defined criteria. Often, this criterion is a household (all members of a household count as one group). However, the same concept can be applied to any other criteria whether it’s a workplace, a department, a last name, or even a location.

Proximity Matching
One powerful feature that sets MatchUp apart from all other tools is the ability to set the geographic distance as a criterion. Often, a large building or company will have entrances on different streets, or a physical mail box along with a PO Box. You can use Proximity matching to group records that are geographically close together.

List Intersection/Suppression
MatchUp makes matching between multiple lists easy. Sometimes, your project requires you not to consolidate multiple lists, but just to find common or unique records between them. MatchUp’s list intersection ability allows you to find all the common data between multiple lists. Conversely, MatchUp’s list suppression ability allows you to find just the data that is unique to each individual list. The applications for these abilities are nearly endless, especially with MatchUp’s flexible matching capabilities which allow you to customize the matching to your exact data set.

Point Of Entry/Batch
As a developer, system admin, or database admin tool, MatchUp Object can be implemented at point of entry or in batch mode. Point- of- entry processing provides real-time monitoring of incoming records allowing you to accept unique records while rejecting duplicate records while they are being entered. Batch processing enables you to dedupe an entire database at once. Use CLR integration from SQL or MatchUp’s incremental deduping engine to perform point of entry deduplicationandenable incremental matching. Or use MatchUp’s Read/Write deduping engine, CLR integration or the MatchUp SSIS transform to perform batch deduplication.

MatchUp is a fast and scalable tool. It can handle millions of records an hour in batch and find any matches in under a second for point of entry. Built for the enterprise, MatchUp is multi-thread and SSIS pipeline optimized and safe and scalable for data sets of any size.

System Requirements
Data Quality Components for SSIS are 32/64 bit tools available for Windows XP/2003/2008/Vista/7 and Microsoft SQL Server 2005/2008/2012

Minimum Requirements (Not Recommended)

Recommended Requirements

Melissa Data's MatchUp Component for SQL Server

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Melissa Data
The leading international provider of data quality and address verification software. Melissa Data's affordable, easy-to-use solutions for data cleaning and deduplication can be used separately or together to provide full global data quality. Melissa Data specializes in Microsoft .NET and SQL Server Integration Services, IBM, Java and Oracle environments with APIs, Web services and enterprise plugins that empower batch and real-time applications. Melissa Data's flagship Contact Zone enables data stewards to quickly create complex data quality and data integration routines with visual programming. Melissa Data also provides a full line of direct marketing solutions including mailing software, list hygiene and data append services, and a comprehensive line of mailing lists and sales leads.