Which of the following ensures effective data quality assessment in SAP Analytics Cloud?

Implementing data profiling is crucial for effective data quality assessment in SAP Analytics Cloud because it involves analyzing and assessing the structure, content, and quality of data before it is used for analytics and reporting. Data profiling helps identify anomalies, inconsistencies, and inaccuracies within datasets, allowing data analysts to ensure that the data used for decision-making is reliable and trustworthy.

Through data profiling, organizations can gain insights into various aspects of the data, such as completeness, uniqueness, and accuracy. This process not only enhances the understanding of the data but also supports maintaining high standards of data governance. Ensuring that data meets quality standards is essential for producing credible analytics outcomes, thereby influencing business decisions positively.

On the other hand, while predictive modeling, user access controls, and visualization templates play important roles in the overall functionality of SAP Analytics Cloud, they do not directly contribute to assessing or improving data quality. Predictive modeling focuses on forecasting future trends based on historical data. User access controls are vital for managing who can see and manipulate data but do not assess its quality. Visualization templates are designed to present data effectively but do not involve the evaluation of the data itself. Thus, data profiling stands out as the most relevant approach for ensuring effective data quality assessment.