Data Parsing

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.

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What We Do

Parse free-form textual data (unfielded) and fielded data

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

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.

What Data Types Does RightFielder Data Parsing Recognize?

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