Data Scrubbing

What is Data Scrubbing?

Data scrubbing refers to the process of correcting errors in a dataset. It is a background process where the content of the memory is checked periodically to detect inconsistencies and rectify the errors to produce a functional copy of data.

In technical terms, it is a Reliability, Availability and Serviceability (RAS) feature and a process to correct bits in the memory that are flipped erroneously due to some kind of transient fault resulting from a physical phenomenon.

Understanding Data Scrubbing

Understanding Data Scrubbing

Data scrubbing means cleaning the data areas in the computer memory when an application is closed.

This prevents others from getting valuable information such as usernames and passwords.

Data scrubbing is also referred to by different terms such as:

However, the verbatim may not be true in all respects.

Typically, data cleansing or cleaning is a process to tidy up data, but it is actually a less involved process.

This basically refers to rectifying or deleting data and is usually done by the data processionals, who do the following:

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In comparison, data scrubbing is a subset of data cleaning that uses actual tools to do deep cleaning over and above making simple corrections.

Steps:

The data scrubbing or cleansing process involves the following six major steps:

Functions

All these steps typically focus on three basic functions, such as:

Algorithm

Typically, a specific algorithm is followed for data scrubbing, which updates only those entries that have errors in them. When such an error is detected, the following happens:

What is Data Scrubbing

What is the Use of Data Scrubbing?

Data scrubbing is typically used to clean or modify a database and remove data that is inconsistent or incorrect. This important strategy detects and fixes errors to ensure that the database is accurate and remains accurate.

The primary use or objective of data scrubbing can therefore be summarized as a strategy that deals with data in a raw dataset that is either of the following:

Data scrubbing is ideally a portion of the data preparation process which produces precise and impregnable data that will help in making reliable business decisions and creating exact models and visualizations.

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Unclean data in a raw dataset can increase the overall cost of revenue by about 12%.

Data cleansing typically refers to a powerful analytical process that ensures a dataset consists of only valid and accurate data. Scalable, accessible, and repeatable data scrubbing is used for:

In addition to that, the process also helps the companies to build a strong foundation for the following:

All these will allow the businesses to have a clear vision of their future path or road to success.

Using the data scrubbing process not only produces consistent, well-structured and precise data that helps in making business decisions but it also helps in identifying the areas that need further improvement.

This will, in turn, help significantly in the following:

Therefore, data scrubbing is useful for any organization both now as well as in the future.

Conclusion

Data scrubbing is an important process to ensure the integrity of data stored in the memory of a computer system.

The process uses Error Correction Codes to verify, amend and write data.

This process helps businesses to do proper data analysis and take intelligent business decisions.