RightFielder Effortlessly Parses Free Form and Wrongly Fielded Data

Melissa RightFielder is a data parsing solution that leverages powerful entity recognition and algorithms to extract, parse, and standardize all your data streams - free-form textual data (unfielded) and fielded data. It also “right fields” each element – first name, middle name, last name, street address, city, state, ZIP, phone, email address, department, company, etc.

It’s like cleaning a dirty room with the wave of a magic wand — suddenly, everything is in its proper place.

  • Organize your data, no matter where it came from to ensure quality analytics across the enterprise
  • Move legacy data from old formats and reformat it for proper fielding to avoid time spent re-keying
  • Break up Big Data streams of long, complicated information strings to transform your unstructured data into a format that makes sense
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Parsing and Fielding

Free-Form (Unfielded) Data
Free-form data is not organized in a predefined manner like structured data. This type of data is messy and cumbersome to analyze, since in addition to free-form text, in may contain dates and other numbers. RightFielder organizes this data by extracting all the textual inputs and analyzing it, recognizing where one field ends and the next one begins.

Fielded Data
Just because there’s a name in one column and an address in another, doesn’t mean they automatically match up! RightFielder reorganizes fielded data into new columns using either predefined layout and field names or custom field names.

Data Parsing - Parsing and Fielding

What Data Types Does RightFielder Data Parsing Recognize?

  • Up to 3 lines of street addresses
  • City
  • State
  • Postal Code
  • Country
  • Personal names
  • Company & department names
  • Email address
  • Web address
  • Phone number
  • Custom data types like SSN, IPs, dates, account numbers, & more!

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Helpful Resources

Video

Data Cleansing Video

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Product Sheet

Data Parsing / Rightfielder Product Sheet

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