What is the outcome of successful data cleansing?

The outcome of successful data cleansing is improved data accuracy and quality. Data cleansing involves the detection and correction of errors or inconsistencies within datasets. By removing inaccuracies, duplicates, and irrelevant information, organizations can ensure that the data they are working with is reliable and precise.

This enhanced quality of data leads to better decision-making, as stakeholders can trust that the insights derived from the data reflect true and meaningful patterns. Improved accuracy also facilitates more effective analytics, predictive modeling, and operational efficiency, as dependable data supports sound business strategies.

In contrast, options such as increased data storage needs, complex data relationships, and slower data processing do not align with the goals or outcomes of data cleansing. Successful data cleansing aims to streamline data processes and enhance quality rather than complicate them or require additional storage resources. Therefore, the emphasis on improved data accuracy and quality is the hallmark of effective data cleansing efforts.