Data parsing is the process of converting data from one format to another with the intention of simplifying it and making it more comprehendible.
Parsing is a technical capability that, according to Gartner analyst Jason Medd, can be broken down into three categories in the context of data management.
The first is data set level parsing. Medd said that an example of this kind of parsing is converting a comma-separated values file into Excel in order to change it from a comma delimited string to a set of columns that are more easy to view and manipulate.
The next category, record level parsing, happens when receiving text information that requires further breakdown.
“An example would be a name and email address combination (John Doe <email@example.com>). Parsing could be applied to separate the name and email into discrete fields allowing you to create an email and address it to John Doe,” Medd explained.
The final category is attribute level parsing which Medd said could be used to further break down John and Doe into a separate first and last name.
According to Medd, parsing has become an essential part of data management. “However, it is also highly technical,” he explained. “As a result, it is often embedded as an automated function in most applications or just provided as a technical function for developers to access.”
Standardization is another important aspect of data management. This process works to transform data taken from different sources and various formats into one, consistent format and is broken into the same three categories.
“Standardization can refer to the type of system or file format being used to transmit information,” Medd said. “It can also refer to how data is to be structured as part of a data model or to how a specific attribute of a record can be formatted.”
In order to simplify the process of data parsing and standardization, the data company Melissa released Melissa RightFielder.
The solution works to leverage powerful entity recognition and algorithms to extract, parse, and standardize data streams.
Additionally, it “right fields” each separate element such as first name, middle name, last name, street address, city, state, zip code, phone number, email address, department, company, and more.
With Melissa RightFielder, organizations gain the ability to:
- Organize data, regardless of where it originated from
- Move legacy data from old formats and reformat it to avoid time spent re-keying
- Break up data streams of complicated information in order to transform unstructured data into a format that makes sense
Melissa also offers several other solutions that help customers to manage their data and enhance data quality. These solutions serve several purposes, including address verification, name verification, profiling, phone verification, generalized data cleansing, email verification, customer data management, and more.
Melissa has also been recognized in the 2021 Gartner Magic Quadrant as well as the G2 2022 Grid Report where the company scored 89% in Ease of Use, 91% in Quality of Support, 96% in Ease of Doing Business with, and 93% in Meets Requirements.
To learn more about Melissa and get started with their data parsing and standardization tools, visit the website.
Content provided by SD Times and Melissa