API:FAQ:Capabilities

File & Name Standardization

Explain in detail how your tool consolidates several different input files with different file formats into single output file format.

The Melissa Data APIs are essentially blind to the data source – you control the file handling, allowing for the greatest flexibility to access (and process) data in any format or structure. Using native plugins for ETL tools such as SSIS or Pentaho Data Integration, the Data Quality Components allow cleansing of most file formats and outputting the format of choice.

Explain in detail how your tool parses input names into pre name, first name, middle name, last name and post name and sends the data to individual fields.

When a Full Name or Multiple Names are passed into Name Object it first determines the gender of the first-names from a lookup table and then parses the specific name components as individual output properties using proprietary logic. Name Object will parse names into all the individual components. Incoming data can be in varied formats and patterns (Mr John Smith; Smith, John; Mr J L Smith Sr: etc., as well as dual names (Mr and Mrs John Smith). There are more than 190,00 first and last names from the US Census in the Name Object.

Explain in detail how your tool splits combined names from one record into two separate records.

Name Object has a built-in list of words that connect names (“and,” “or,” “&,” etc.). It is the presence of these connectors that tell the object that a dual name may exist in the input full name string.

If a file contains a name field with a “Mr. and Mrs. Tom Jones,” explain how your tool splits these into two separate records (e.g. One record for Mr. Tom Jones and a second record for Mrs. Tom Jones).

The respective parsed names would be split into these properties, one set per name.

Results for name ‘Mr. and Mrs. Tom Jones’:

Prefix: Mr.	Prefix2: Mrs.
First Name: Tom	First Name2: Tom
Middle Name:	Middle Name2:
Last Name: Jones	Last Name2: Jones
Suffix:	Suffix2:
Gender: M	Gender2: F