What are dirty data?

Dates data is a term used to describe any type of electronic data that is outdated, incomplete or otherwise accurate. Data of this type can be created because of data entering errors, inability to regularly update data, or even enter the same data more than once. Incorrect data is sometimes nothing but punctuation errors in the text of electronic documents. In other cases, dirty data may be information that is deliberately misleading, such as attempts to edit accounting records so that investors and others present a specific picture.

For the most part, the accumulation of dirty data in any type of database is unintentional. Individuals who enter new information into the database can catch words, skip punctuation that is important to understand the intention of the text, or fail to comply with a specific formatting strategy. With situations of this type, the correction of incorrect information is a relatively simple process that requires nothing but a change of nEssential text and imposition of changes. Businesses sometimes manage this process by entering and performing the necessary updates.

DIG DATA can also occur due to failure of existing records when information changes. For example, if dealers are unable to update customer files when staff changes to the customer, these files are no longer accurate and are considered dirty. As in the repair of spelling and punctuation errors, granting time to eliminate outdated information and replacing the current data helps to increase the overall usability of the database.

There are situations where the creation of dirty data is intentional. Companies may decide to omit specific information from the database to create a specific perception of finances, such as highlighting the amount of generations of income for a given period but decide not to enter data concerning the amount of the collectionincome for the same period. In this type of dirty data, the information presented is accurate in terms of it, but are considered incomplete.

With some types of dirty data, it may be a decision that you will not have time and effort to make repairs. This is common when incorrect data have no impact on the company's ability to function properly or are no potential to cause any great anxiety. This means that almost any entity that maintains some type of database probably has at least a little dirty data dispersed with other information that is up to date and accurate.