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VOOZH | about |
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VOOZH | about |
Data quality testing is essential in ETL operations since it helps evaluate the data flowing from source systems into more suitable data warehouses or storage systems. The process of ETL stands for Extract, Transform, and Load; extract means extracting data from one or multiple sources, transform means converting data into the format or structure in which the data is required to be stored, and the last step is load, means storing this data in its final destination. Any data quality problem at these stages will invariably cause serious business problems, such as wrong business decisions, legal implications, and organizational ineffectiveness.
According to a study by IBM, poor data quality costs the U.S. economy around $3.1 trillion annually. Additionally, Gartner reports that organizations believe poor data quality to be responsible for an average of $15 million per year in losses. In light of these figures, it is clear that billions of dollars can be lost due to poor-quality data, hence the relevance of data quality testing in ETL.
Table of Content
Ensuring data quality in ETL processes is vital for several reasons:
The following tests are required when it comes to ensuring the high quality of data within the data warehouse and, again, when using them for analysis, reporting, and decision-making purposes:
Tests | Description |
|---|---|
Uniqueness Test | It makes sure actual records are not duplicated in the data. This test is important in helping the database maintain the data quality since every record is unique. |
Completeness Test | Checks whether every column expected to have data input has a value and is not empty. This test helps to identify cases where there is missing information to avoid such data influencing other processes or analyses. |
Consistency Test | It shows that data conforms to a certain format, say date format, dimensional units, or even the naming of files, columns, or variables. |
Accuracy Test | Confirms that the values in the data match business reality or obey a list of business rules. This test is used in decision-making processes. |
Validity Test | In this case, it checks that the data meets the format and is in the correct format or meets the set rules where it has to match a certain format or range. For example, it is expected that a date should be formatted like this YYYY-MM-DD. |
Timeliness Test | Checks that the data used in the tool is current or for the right period, as the case may be. This is particularly so where the monitoring data must be accurate within provisional, real, or near-real time. |
Integrity Test | Checks that there are interplays between different entities of data, especially rules such as foreign keys and requirements of relational databases. |
Conformity Test | Checks for format, meaning that while sorting data, it provides that format or that it conforms to set business rules such as postal codes. |
Range Test | Confirms that the data is within the human expected range of the values of the measurand. For instance, checking the numbers in a set of data for age to fall between 0 and 120. |
Data Type Check | Ensures that the value entered belongs to the correct data type; for instance, where the field to be filled is numeric, the value entered should only be numeric. |
Testing data quality in ETL processes becomes vital for determining whether the data to be analyzed, reported, and used for decision-making fits the purpose. Thus, with the help of shared data quality issues, proper testing methods and techniques, tools, and technologies, the quality of data and the resulting information increases in the company.
