How can you identify outliers in your ERP data using data visualization?

1 Why outliers matter

Outliers can affect the accuracy and reliability of your ERP data analysis and reporting. They can skew the mean, median, standard deviation, and other statistical measures of your data. They can also hide important trends, patterns, or relationships among your variables. Therefore, it is essential to detect and handle outliers appropriately in your ERP data before drawing any conclusions or making any decisions.

2 How to visualize outliers

When visualizing outliers in ERP data, there are various methods to consider, depending on the type and dimensionality of the data. Box plots are graphical representations of numerical variables, displaying the minimum, maximum, median, and quartiles of the data. Outliers can be seen as dots or stars outside the box or whiskers. Scatter plots are plots of two numerical variables on a Cartesian plane, with outliers represented as points far away from the main cluster or trend line. Histograms illustrate the frequency or count of a numerical variable in different bins or intervals, with outliers usually appearing as bins with very low or high frequencies. Bar charts show the frequency or count of a categorical variable in different groups or categories, with outliers typically being groups with very low or high frequencies compared to the rest of the data.

3 How to use data visualization tools

Data visualization tools such as Excel, Power BI, and Tableau can be used to create and customize different types of plots for your ERP data. Excel has built-in features and functions for creating charts and graphs, while Power BI allows you to connect, analyze, and visualize data from various sources. Tableau enables you to explore, analyze, and visualize ERP data in a user-friendly and intuitive way. With these tools, you can use formulas, filters, pivot tables, queries, measures, visuals, dashboards, stories, maps, and reports to create interactive and dynamic data visualizations that are stunning and insightful.

4 How to interpret outliers

Once you have visualized your outliers in your ERP data, you need to interpret them and decide how to deal with them. First, verify the data quality to see if the outliers are caused by data entry errors, missing values, incorrect formats, or inconsistent units. If so, you must correct or remove them from your data. Second, investigate the data context to determine if the outliers are meaningful or relevant to your business problem or question. Third, evaluate the data impact to assess if the outliers have a significant or negligible impact on your data summary or inference. If so, you should adjust or modify your data analysis and reporting methods to account for them.

5 How to handle outliers

Outliers are inevitable in any ERP data, but they can also provide valuable insights and opportunities for improvement. When dealing with outliers, it is important to consider the data quality, context, and impact. Common methods for handling outliers include deleting them from the data, replacing them with other values, transforming the data with a mathematical function, and clustering the outliers with similar data points. By using data visualization techniques, you can identify, interpret, and handle outliers effectively and efficiently in your ERP data analysis and reporting. However, deleting outliers can reduce the sample size and introduce bias into the analysis; replacing them can distort the distribution and variance of the data; transforming them can reduce their effect but make the data more normal or symmetrical; and clustering them can help identify subgroups of the data.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?