Address validation, duplicate removal, gender coding for MS Access

Address Validation & Standardization - Cleaning-up and standardizing addresses has never been so simple. You can even split apart multiple addresses in the same field! Specify up to two input addresses for processing. The better address will be cleansed and standardized then combined with the secondary address (Apartment, Suite, etc.) if present, and placed into the output address fields. The output address fields can specify the full address or you can just split off the address parts you need such as Apartment, PO Box, or Street Name for an alphabetical sort.

	Save $$$ on Postage by Eliminating Partial or Non-Addresses
	Standardize Addresses for Faster Processing
	Easily Separate Name, Street Address and City/State/Zip
	Unlimited Processing Volume; No Recurring Update Charges

The screen shot above was taken from frmAccessAddress (click image to enlarge)

Name Parsing & Gender Determination

Description

Name Parsing & Gender Determination - Verify whether or not a particular field contains a valid individual or company. A 100,000-word name dictionary in combination with an 8,000-word company dictionary ensures precise gender determination. You can even split apart compound names. John & Jane Doe will be split into 2 separate fields: John Doe / Jane Doe. The output names can be the Full Name or, you can just split off the parts of the name that you need such as Last Name. Gender Coding also allows you to create personalized greetings for a more positive response.

	100,000-Word Name Dictionary for Pinpoint Accuracy
	Nicknames Property - Invaluable for Identifying Duplicate Names
	Easily Separate Dual Names in the Same Field
	Prefixes and Suffixes are Standardized to Your Specifications

The screen shot above was taken from frmAccessGender (click image to enlarge)

Proper Case Conversion

Description

Proper Case Conversion - Now you can easily convert existing data formats into proper-cased formats that are more attractive and easier to read. Your data is accurately converted through the use of Style Tables that you define. You can easily translate, punctuate, abbreviate, unabbreviate as well as correct common misspellings. The result is a precise transformation to proper case. no matter what type of data.

	Easily Add Punctuation Such as: “abc inc” -to- “ABC, Inc.”
	Save Keying Time - Automatically Convert Symbols and Translate Abbreviations into Their Proper-Cased Full Spelling
	Apply Different Capitalization Rules to the Same Word Based on its Context - “Co” is Company and “CO” is Colorado
	Correct Typos Using the Built-In List of “Common Misspellings”

The screen shot above was taken from frmAccessCase (click image to enlarge)

Data Re-Fielding

Description

Data Re-Fielding - Quickly and accurately identify name, company, street address, city/state/zip, phone number and e-mail and put them in their place. Each input field is examined to determine the type of data and then placed into the appropriate output field.

	“Re-Field” Data to Exactly Where it Belongs
	Select from 15 Different Output Field Types Including Name, Company, Address, Phone Number, E-mail Address and More
	Each Input Field is Examined Moving Names into Name Fields, Companies into Company Fields, Etc.
	Convert Unorganized Chaos into Accurately Fielded Data!

The screen shot above was taken from frmAccessFielder (click image to enlarge)

Duplicate Record Removal

Description

Duplicate Record Removal - Save $$$ on postage by eliminating incomplete or bad addresses. Find more duplicates through the use of the built-in name and address parsing technology which makes even hard to find nickname matches a snap. View and select exactly which records you want to keep or remove in a convenient, uncompromised preview grid.

	Select One of the Supplied Match Code Groups or Create Your Own Customized Match Code Groups
	Each Match Code Element can be set to Match on Just Part of a Field Up to the Full Length of a Field
	Save Time - Name, Address, and City/State/Zip Fields can be Split on-the-fly
	Use Nickname Matching to Catch Even More Duplicates - Robert matches Bob, Bobby, Rob, Robby and Robert
	View All Duplicate Records or View Each Duplicate Group Separately in an Easy-to-use Preview Grid
	Select or Deselect Which Records are to be Kept or Removed

The screen shot above was taken from frmAccessMatch (click image to enlarge)