Which feature in SAP Analytics Cloud helps identify and correct data quality issues before analysis?

Data cleansing is the feature in SAP Analytics Cloud designed specifically to identify and correct data quality issues before analysis. It involves processes that help remove inaccuracies, inconsistencies, and irrelevant data from datasets, ensuring that users analyze high-quality and reliable data. Proper data cleansing improves the integrity of the data being analyzed, leading to better-informed decision-making, accurate reporting, and trusted insights.

This feature includes tools for identifying duplicates, correcting errors, and standardizing data formats. By focusing on cleaning the data before any analytical processes begin, it minimizes the risk of drawing incorrect conclusions based on flawed data.

The other options, while they carry their own significance in data management and analysis, do not serve the primary purpose of correcting data quality issues in the same direct manner as data cleansing. For instance, data monitoring involves tracking data performance and anomalies, but it does not directly engage in correcting the underlying data quality issues. Smart discovery helps in generating insights from data but assumes that the data is already cleaned. Data auditing focuses on ensuring compliance and tracking changes over time rather than actively correcting data issues. Thus, data cleansing stands out as the essential feature for ensuring data quality for analysis.