There are several common Excel challenges that data analysts may face when working with their datasets.
Here are a few examples of scenarios where these challenges may arise:
#1: Large or complex datasets: Working with large or complex datasets can be time-consuming and challenging, as it may take longer to analyze and manipulate the data, and it may be more difficult to identify trends and patterns.
#2: Inconsistent or incomplete data: Inconsistent or incomplete data can make it difficult to accurately analyze and report on supply chain performance. For example, if some data points are missing or incorrectly entered, it may be difficult to calculate accurate metrics or make informed decisions.
#3: Manual data entry and analysis: Many supply chain analysts still rely on manual data entry and analysis, which can be time-consuming and prone to errors. This can be especially challenging when working with large datasets or when data needs to be updated frequently.
#4: Lack of standardization: Without standardization in data formatting, terminology, and reporting, it can be difficult to accurately compare and analyze data from different sources or over time.
#5: Limited data visualization options: Excel’s built-in data visualization options may not be sufficient for some supply chain analysts, who may need more advanced or customized charts and graphs to effectively communicate their findings.