Examples of Dirty Data

E.

Dirty data is a term used to describe data that is inaccurate, incomplete, inconsistent, or contains errors. It is also referred to as poor data quality. The presence of dirty data can cause problems in data analysis, reporting, and decision-making.

The examples of dirty data are as follows:

A. Duplicate Records: These are the instances where there are multiple copies of the same data record in the database. This can happen due to human error, system issues, or database design flaws. Duplicate records can cause confusion and errors in data analysis, and can also waste valuable storage space.

B. Spelling/Punctuation Mistakes: These are typographical errors that occur when entering data. Examples include incorrect spelling of names or addresses, or the use of incorrect punctuation. These errors can be caused by human error or system limitations.

C. Incomplete Records: These are instances where the data is missing or incomplete. This can be due to human error, system issues, or data collection limitations. Incomplete records can cause problems in data analysis, reporting, and decision-making as it does not provide the full picture of the data.

D. Free Text Spelling Errors: This refers to the spelling errors in the unstructured text fields. These are fields that allow users to enter data in a free-text format, such as comments or notes. Free text spelling errors can be caused by human error or system limitations.

E. All of the Above: All of the examples above are considered dirty data. Data quality issues can have a significant impact on business operations and decision-making processes. Therefore, it is important to identify and address these issues as early as possible. Companies can use data cleaning processes or data quality tools to improve data quality and ensure the accuracy and completeness of their data.