RightFielder Object:FAQ

From Melissa Data Wiki
Jump to: navigation, search

← RightFielder Object

Contents

What is the RightFielder Object and how will it improve my data quality?

Answer
The Right Fielder Object is a programming library used to reorganize data so that each field contains the correct kind of contact information.
Typical uses for this library are: Real-time data entry re-fielding. When users fill out web forms incorrectly, RightFielder can, for example, identify that a second address line was accidently typed into the CityStateZip field. Data Storage. Now that RightFielder has identified the wrong datatype, you can store it in the correct field of your database preventing costly waste when trying to verify addresses, presort your mailing list, or running queries to retrieve records for a particular zip code – which could have been stored incorrectly in another field. RightFielder should also be used to clean up existing databases where data entry or appending records has made field data storage inconsistent.


Does my source data have to be formatted or delimited for RightFielder to correctly identify distinct fields and data types?

Answer
RightFielder will have a much easier time with data already separated by table fields or columns. For single line inputs, you can help RightFielder accuracy by specifying an expected delimiter. In the absence of delimiters or fields, the more complete and valid an input is, the better chance of RightFielder identifying the data type.


If one of my input fields contains Fullnames, will it re-field them into separate FIRST and LAST name fields?

Answer
No, if field data contains a first name or a fullname, it will be recognized as a name data type, but will not split out the last names. We have another solution, Name Object, which splits fullnames into parsed first and last name fields.


How does RightFielder differentiate a fields contents between different Data types?

Answer
RightFielder uses programmed logic and a compiled data file to intelligently distinguish what each word is. Each word is compared to the other words in the field, as well as neighboring data in the previous and next fields. So ‘Johnson, Robert’ will be recognized as a name while ‘Johnson Corporation’ will be recognized as a company.


Do I have any control over how RightFielder processes and recognizes data types in my file?

Answer
Absolutely! In addition to internal data recognition logic, recognized key words are maintained in an accompanying data file, which you can override to change how each keyword is recognized. This is done through the configuration file where you can add new entries, and edit or delete existing ones. You can also, through a set of SetAccept methods, tell the Object what data types you are expecting as input data, or which data types should not be recognized and returned as output types.


How can I evaluate what was identified and parsed?

Answer
The Results Code output property will tell you if the RightFielder Parse was a success, the record was incomplete, or if unrecognized data was found and could not be fielded into one of the output properties.


Should I process every field in my database, and output every possible data type the Object allows?

Answer
No, processing only the input fields which need to be corrected and creating the proper number of outputs will greatly increase the accuracy. In other words, if only names, companies and addresses are comingled in the first three fields, only process those three fields and don’t create an output phone or country field.


Will RightFielder re-field an Address and a City State Zip all in one input field into separate output fields?

Answer
Yes. RightFielder can now be used as a free form parser, and will now recognize the unique data types contained in a run on string.


What will RightFielder do with multiple occurrences of the same data type?

Answer
What if my data contains multiple personal names; or for other records multiple company and department names; or multiple email addresses and phone numbers? Right Fielder will not discard or place them incorrect output properties, but instead will dynamically create multiple output properties for the respective identified data. You only need to call a GetNext method to retrieve multiple instances.


What data types does RF recognize?

Answer
RightFielder will identify Names, Addresses, Departments, Companies, Addresses, Cities, States and Provinces, Zip and Postal codes, Phone numbers, and Countries. It will now also recognize URLs, email addresses and custom defined types via defined data patterns – like a social security number or a formatted date.


Will it re-field International data?

Answer
While RightFielder does an excellent job with domestic US and Canadian data, recognizing the data types of other countries is beyond the scope of this program. It will however, flag Countries if the name of the Country is in one of the input fields, most accurately if that name is the last non-numeric word in the last input token. This is not to say that if you have a compiled list of international key words of the data types we process, and what they represent, you can’t make RightFielder work. You are welcome to massively edit or override the accompanying data file through the Right Fielder configuration file.


Is there any example code?

Answer
The demo is distributed with example projects for many popular programming languages like, C++, FoxPro, C#, Visual Basic, ASP and SQL Server. We also provide working examples and wrappers for interfaces such as Java, .NET, Perl PHP, Python and Ruby. While limited in practical usage, these examples show all steps needed from Initializing the Object to returning the Output results.
Personal tools
Namespaces

Variants
Actions
Navigation
Products
Support