What are common data quality dimensions?

Common data quality dimensions refer to the specific attributes or characteristics that are used to evaluate and ensure the quality of data in business intelligence processes. Accuracy and consistency are key dimensions of data quality for several reasons.

Accuracy measures how correctly the data reflects the real-world scenarios or events it is meant to represent. If data is not accurate, it can lead to faulty analysis, misleading insights, and ultimately poor decision-making. For example, if a customer database incorrectly lists customer purchase histories due to inaccurate data entry, this could lead to ineffective marketing strategies.

Consistency relates to the uniformity of data across different datasets or within the same dataset over time. Inconsistent data can arise when different systems or departments maintain separate records that do not align. For instance, if one department has a customer’s contact information as "John Smith" and another department lists it as "Jon Smith," it creates discrepancies that could affect communications and reporting.

By focusing on accuracy and consistency, organizations can improve their data quality, leading to more reliable analyses and stronger business insights. This focus supports the overall goals of business intelligence to enhance decision-making and strategic planning. The other options, while they may pertain to various aspects of business operations and data handling, do not directly relate to the core dimensions of