SSIS transforms that cover the full spectrum of enterprise data quality

Features and Benefits
    • Delivers a single view of the customer
    • Supports data quality and MDM initiatives
    • Leverages MS SSIS to profile, cleanse and integrate critical information assets
    • Lowest cost of ownership

Plug these data quality and enrichment transforms into your SSIS pipeline:

Data Quality and Enrichment Transforms                                                         

View Demonstrations

Step 1: Profiling

	Address Transform Corrects, validates and standardizes addresses against the latest USPS data.			Phone Transform Updates, corrects, and standardizes U.S. and Canadian area code/prefix numbers, or full 10-digit phone numbers 6 months or older. Identifies number as cell, landline, or VOIP.	Step 2: Cleansing
	Email Transform Validates and corrects misspelled or invalid email addresses using three levels of verification: Syntax, Local Database, and MX Lookup.			Name Transform Parses full and multiple names into five components, genderizes first name, and flags suspicious and vulgar words.
	GeoCoder Transform Appends lat/long coordinates, Census tract block numbers, and county name and FIPS code to the ZIP+4 or rooftop level.			SmartMover Transform Updates addresses of individuals, families and businesses that have filed a change-of-address with the USPS in the last 48 months and qualifies First-Class & Standard mailings for postal discounts.	Step 3: Parsing & Standardization
Fuzzy Matching and Duplication
	Jaro, n-Gram Transform, Jaro-Winkler Transform n-Gram Transform Matches customer records into identifiable groups, using sophisticated fuzzy matching algorithms to link or merge related records within or across disparate datasets.			MatchUp Transform Identifies duplicate records for merge/purge efforts to reduce costs and achieve a single view of the customer.
Generalized Cleansing and Validation Transforms					Step 4: Matching
	Validation and Cleansing Transform Normalization Transform Corrects data values to meet specific business standards, customer business rules, or relationship constraints.
Data Profiling and Monitoring
	Column Profiling Identifies data quality issues that require immediate attention to avoid unnecessary processing of unacceptable data.			Value Distribution Column Pattern Transform Monitors data in real-time using automated processes to detect when data exceeds pre-set limits.

System Requirements
• Microsoft SQL Server 2005-2008 – any edition
• .Net Framework 3.5
• Windows XP SP2, Windows Server 2003 and Vista (Business or Home) 32/64-bit

Call 1-800-MELISSA (635-4772) or click here to request a Free Trial.