Data is a key component of settlement administration. Whether you’re handling historic sales information, inventory, financial records, or personal information—a project’s success depends upon the quality of this data. Class member data can be cumbersome to parse through and format, as it often includes various types of information and must be cataloged properly. All disparate data must be ‘scrubbed’ to be useful. Data scrubbing is the intricate process of cleansing, segmenting, cataloging, and refining collected data.
6 main processes comprise a data scrub project:
Extraction
At the early phase of a project, extraction is the process of obtaining specific datasets from corporate databases using Structured Query Language (SQL) or other database connector technologies. Depending on the complexity or nature of the project, some situations may call for a more consultative approach to data handling and formatting.
Consolidation
Consolidation is taking data from different sources and formats and compiling it into a master file that is easy to read. This includes formatting data from large and often unstructured sets of information or improperly formatted data, into an organized legible arrangement of columns and rows.
Validation
Validation checks the legitimacy of the data. For settlement administration, validation can include comparing social security numbers with the Social Security Administration database to ensure legitimacy.
Cleansing
Cleansing data typically includes removing erroneous and invalid characters, position errors, or column misalignments. This can extend into replacing values and changing values to match the expected scenario data.
Enrichment
Enrichment is adding data from an external source to enhance the quality of the data. One example of enrichment is to provide the location, contact information, historical records, and previous addresses of a list of class members.
Normalization
Normalizing data typically includes the sense of refactoring and organizing the information into preset and preconfigured buckets or categorical assignments. For example, normalization of vehicle data would force values into forming a series of data points such as Year, Make, Model, and Trim. These 4 data points are considered the fully normalized edition of a vehicle's data.
The Impact of Technology on Data Scrubbing
While it is possible to manually scrub data using Excel, the process is time consuming for a data analyst and can be costly. Our technology platform leverages automation to efficiently scrub data, which significantly reduces the number of skilled hours required for a project.
The scope and time required for data scrubbing is correlated to the complexity, volume, and quality of the data received. With more than 15 years of working on class action settlement administration projects, our data team and technology can work with any dataset to ensure accuracy and functionality. Contact us today to learn how we can help you with your matter.
