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[LAST CHANCE] Data Layer: Modern Business, Defined

Data Scrubbing

Definition - What does Data Scrubbing mean?

Data scrubbing refers to the procedure of modifying or removing incomplete, incorrect, inaccurately formatted, or repeated data in a database. The key objective of data scrubbing is to make the data more accurate and consistent.

Data scrubbing is a vital strategy for ensuring that databases remain accurate. It is especially important in data-intensive industries, including telecommunications, insurance, banking and retailing. Data scrubbing systematically evaluates data for flaws or mistakes with the help of look-up tables, rules and algorithms.

Data scrubbing is also referred to as data cleansing.

Techopedia explains Data Scrubbing

Database errors are common, and may originate from the following:
  • Human errors during data entry
  • Database merging
  • Absence of industry-wide or company-specific data standards
  • Aged systems that contain obsolete data
In the past, data scrubbing was performed manually. This not only increased the time required to complete the process, but also made the process much more expensive and prone to errors. This led to the creation of effective data scrubbing tools, which systematically evaluate data for flaws that could not be identified in a manual cleaning process.

Generally, a database scrubbing tool consists of solutions that are ideal for rectifying several specific kinds of mistakes, like locating duplicate records, or replacing missing ZIP codes. Merging erroneous or corrupt data is the most complicated issue. It is even described as the "dirty data" problem because it costs organizations millions of dollars every year. This phenomenon is increasing with the introduction of more complex business environments with more systems and data. Data scrubbing helps organizations tackle such issues by providing powerful data scrubbing tools to identify and eradicate data flaws.

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