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Power BI lets you quickly identify outliers and dive deeper into your data analysis.
Identification and investigation of outliers can facilitate better decision-making and enable your business to take informed actions based on accurate data.
Identify the outliers
There are several ways to identify outliers in Power BI, depending on the specific visualization or analysis you are using. Here are a few methods:
- Box and whisker plot: A box and whisker plot is a type of chart that displays the distribution of a dataset. It can also help identify outliers in the data. To create a box and whisker plot in Power BI, you can use the “Box and Whisker” visualization. Once you add your data to the visualization, any outliers will be shown as individual points outside of the box.
- Scatter plot: Another way to identify outliers is to create a scatter plot. Scatter plots show the relationship between two variables and can help identify any points that are significantly different from the rest. You can use the “Scatter chart” visualization to create a scatter plot in Power BI. Once you add your data to the visualization, you can use conditional formatting to highlight any outliers.
- Z-score analysis: Z-score analysis is a statistical method that can help identify outliers in a dataset. It calculates the number of standard deviations that a data point is away from the mean. A Z-score of more than three or less than negative three is often considered an outlier. To perform Z-score analysis in Power BI, you can use the “New column” feature to create a new column with the Z-score calculation. You can then filter or sort the data based on the Z-score column to identify outliers.
- Data profiling: Power BI has a data profiling feature that can help identify outliers in a dataset. Data profiling creates a summary of the data, including minimum and maximum values, average, median, and quartiles. It can also identify data points significantly different from the rest. To use data profiling in Power BI, select the data table, click “View” in the ribbon, and then select “Data Profiling.” The data profiling summary will then be displayed.
Investigate the outliers
Once you have identified the outliers, it’s essential to investigate why they deviate from the norm. There are several reasons this could happen.
- Extraordinary events: An unusually high revenue for a particular product might be due to a unique promotional campaign that went viral.
- Data entry errors: Incorrect or mistyped entries, such as misplaced decimal points, can lead to outliers.
- Changes in data collection methodology: There might be a shift in measurement units or how the data is collected, resulting in inconsistencies.
Analyze any patterns or trends
Analyze any patterns or trends in the data and examine the outliers more closely to understand their root causes. This will help you determine if the outliers should be considered part of your analysis or if they should be removed to avoid skewing your results.