How is high-quality data generally defined?

High-quality data is typically defined as data that meets the intended use and purpose, which means it is relevant and appropriate for the specific context in which it will be applied. This definition encompasses several key aspects of data quality, such as accuracy, completeness, consistency, and timeliness. When data aligns well with the intended objectives, it ensures that decision-makers can rely on it for analysis, reporting, and overall strategy formulation.

Data that serves its intended purpose allows organizations to make informed decisions based on insights derived from that data. For example, data that is collected for customer analysis needs to effectively reflect customer behavior and preferences to be deemed high quality within that context. Similarly, in a clinical setting, health data must accurately represent patient health conditions for effective medical decision-making.

Other aspects, such as being verifiable, answering questions effectively, or being free from errors, contribute to the overall quality of data but do not fully encompass its ability to serve its purpose. Therefore, the focus on intended use and purpose highlights the essential role that contextual relevance plays in determining the quality of data in data-driven decision-making.