Certified Supply Chain Professional (CSCP) Practice Exam

What does data aggregation help to achieve in data analysis?

1. Increases data frequency and diversity

2. Smooths out peaks and valleys, reducing variance

3. Creates new data points for analysis

4. Eliminates outliers from datasets

The correct answer is: Smooths out peaks and valleys, reducing variance

Data aggregation plays a crucial role in data analysis by consolidating information from various sources into a single summary. This process helps to smooth out peaks and valleys in the data, effectively reducing variance. By averaging or summing data, aggregation allows analysts to identify trends over time without being overly influenced by fluctuating individual data points. When data is aggregated, it highlights the overall patterns and tendencies in the data set while minimizing the impact of anomalies or short-term fluctuations. This is particularly useful in forecasting and reporting, as it provides a clearer picture of underlying trends. As a result, stakeholders can make more informed decisions based on aggregated data, which often reflects a more stable and reliable overview of the dataset. In contrast, options like increasing data frequency and diversity, creating new data points, or eliminating outliers represent different processes or methodologies in data analysis that do not specifically relate to the primary effect of data aggregation, which is about achieving stability and clarity through smooth consolidation of data.