We are aware that Power BI has some AI visuals as well as standard visuals. Standard visuals are nothing but the normal bar, line, area charts.
But these normal visuals become extraordinary in Power BI with some hidden features and definitely it is working intelligently.
Let’s learn those insight ingredients of standard visuals.
I consider the US Superstore dataset from Kaggle.
- Let’s start with the Get Data option under the Home tab. As this is a CSV file, select the Text/CSV option from the drop-down list
- Select the file named US Superstore data.csv
- After selecting the file, data will be displayed in the below format
- Click on Load and save data.
- In this data model, create one date table with required columns. You can follow my blog about date table creation.
When you are creating any standard view like line, area, bar, you observe how data is behaving differently from year to year, month to month or category wise.
To deeply understand these differences, you may have to manually dig down to a detailed level of data and try to find out the reason behind this.
But Power BI makes our life easy. It provides one insight feature to standard visuals, that helps to understand the reason for the data variation.
Let us understand this with some example.
Create Line Chart
Create one line chart using date hierarchy and Total Sales value.
Analyze — Explain the Increase
- In the above example, if you right-click on the increasing data point for example data for the year 2016 or 2017, you can find different options.
- Out of all options, select “Analyze” and this will be extended with the “Explain the increase” option.
- Click on this “Explain the increase” option, a list of visuals open with multiple chart options
- These visuals mainly consist of the selected measure value which is mentioned in the line chart and relationship with other dimension values like the product, category, customer etc.
- Each visual provide the explanation for which data points, sales value is in increasing order.
- If you want, you can select a more effective visual type out of the waterfall, scatter, 100% stacked column, ribbon chart.
Analyze — Explain the Decrease
- In the above example, if you right-click on the decreasing data point for example data for the year 2015, you can find different options.
- Out of all options, select “Analyze” and this will be extended with the “Explain the decrease” option.
- Click on this “Explain the decrease” option, a list of visuals open with multiple chart options
- A similar type of explanation with visuals are available to find out the reason for decreasing data points.
Find the distribution difference
- Create one bar chart with sales and category dimension.
- Now right-click on the chart and a list of drop-down options are available.
- Select “Analyze” and the extended option is available “Find where this distribution is different”.
- Click on this extended option and a list of visuals are available like before with all the detailed explanation.
- It is providing the comparison details.
- If you click on the yellow Comparing Proportions button, the button colour changes to grey and the text to Comparing absolute values
Add Analyze visuals to your report page
This is a very interesting part of this blog.
With some clicks, you will get some ready to serve insight reports for your customer.
Understanding of Type of Analyze Feature
Till now, you have used a line chart and a bar chart. So based on data whether it is discrete or continuous, we can define types like below.
- Explain the increase/decrease: Line chart, area chart, stacked area chart; only for the continuous category, like date/time, on the axis
- Find where this distribution is different: Stacked bar chart, stacked column chart, clustered bar chart, clustered column chart
- If continuos category represents with bar or column chart, you can analyze both the increase/decrease and different distributions.
Please find the code in the below location
In this blog, we understand how hidden features of a standard visual help to provide an insightful explanation with detailed data points.
In my next blog, we will learn more about AI and Power BI.
If you have any questions related to this project, please feel free to post your comments.
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