MatchUp3 Component for SQL Server

Get cleaner data. Make better decisions.  

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:

• Combine data from duplicate records to streamline communications and business operations
• Make reporting initiatives accurate by ensuring uniqueness of counted records
• Entity Identification management: Link related records within or across multiple datasets
• Save on printing and mailing costs, save time and reduce errors
• Eliminate excessive “rules-based” matching
• Resolve matches regardless of inconsistencies in formatting or standardization
• Free technical support included. Details here

Features:

• Compare records in one or more tables at once and integrate the data. Use two input pins (i.e. source and lookup) to suppress duplicates or list out intersecting records
• Recognizes any combination of over 35 data types, including ZIP Code, Address, and Last Name, Email Address, Social Security Number, with domain specific knowledge
• Cascade up to 16 MatchCodes to catch all matching rules in one pass
• Graphically design rules with the MatchCode Editor

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

Exact Jaro Jaro-Winkler n-Gram Levenshtein Needleman-Wunch Smith-Waterman Smith-Waterman-Gotoh ice’s Coefficient

Jacard Overlap Longest Common String Soundex Phonetex Frequency Frequency Near Containment MD Keyboard

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

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
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.

Speed/Scalability
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)

• 32-Bit Windows XP/2003/2008/Vista/7
• Microsoft SQL Server 2005/2008
• 6 GB hard-disk space

Recommended Requirements

• 64-Bit Windows XP/2003/2008/Vista/7
• Microsoft SQL Server 2008 R2/SQL Server 2012
• 6 GB hard-disk space

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