Pareto Chart In Tableau 10

For Corporate training and Online Training contact at info@instrovate.com

During my Corporate Tableau Training in Gurgaon , i get questions many time regarding Pareto charts.

The Pareto principle is the 80-20 rule. It basically states that, roughly 80% of results come from 20% of the causes. For example, 80% of profits come from 20% of the products.

In Tableau, you can apply a table calculation to Profit data to create a chart that shows the percentage of total Profit that come from the top sub category. You can also see which subcategory account for 80% of total profit.  

We will follow the steps to create a Pareto Chart in Tableau on sample Super Store Data set. We all know that Sample Superstore is a mock up data for a sample super store, so it may or may not be the case that the Data does not follow the Pareto Principle.

Let’s do it for the Sample Superstore data set that comes up with Tableau 10 and we will use Tableau Desktop 10.0 to validate if the Data set that we are looking into follows the Pareto Principle or not. So the Question that we are seeking an answer here is “Does 80% of the Profit is Contributed by 20% of the Products Sub Category

 

Step 1: Drag Subcategory onto Columns and Profit on to rows

 
 
Step 2: Sort in Descending Order on the basis of profit

 
 

Step 3: Add a Table Calculation (Running Total) of Sum of Profit

 

So what we get till now is we have the “Running Total of Sum of Profit” i.e. Profit starts and the running total gives 100% of the profit that happened. So in order to know when 80% of the profit was done , we add a new secondary Table calculation on sum(profit) and change it to %.

 

Step 4: Change Running Total of Sum(Profit) from Actual Value to %. For this we add another Table calculation to it as below.

 

 
Step 5: Analyze the Chart that you created. Pareto Chart is ready but the Analysis remains.
 

 

So as we can see in the screen above, we can see the 80% of sales ( on the Y axis ) . And even without doing anything further we can see that 80% of sales on Y axis corresponds to 5 products of sub category i.e Copiers , Phones , Access, Paper , Binder, Chairs .

So out of total 17 Product Sub Category we have 6 product sub category that is reason for 80% of the profit and that makes 6/17 = 35% around. So we can say that Our Data does not follow the pareto principle as 80% of the Sales is being contributed by 35% of Product Sub Category and not 20% .

The Pareto Chart from Understanding perspective is complete, however if we want on the X- Axis instead of Product Sub Category Name we can show % so that, we can create trend lines of 80% on Y- Axis and 20% on X-axis to clearly see the 80-20 rule.

Now in order to calculate the % in x-axis , we have to do the same way as we calculate manually i.e. 6 / 17 i.e. index() / size () we will make a calculated field ParetoKPI and since we want to do it for “Sub Category” , we need to have the sub category in Marks shelf and compute Pareto KPI using “Sub Category”

Let’s do it in few steps as below

 
Step 6: To find out Percentage in x axis , create calculated field as ParetoKPI .

Step 7: Thereafter Drag Sub Category from Columns to Mark Shelf and ParetoKPI to columns and for this Compute Using “Sub Category”

 

 
 
 

Step 8:  In Marks Shelf change it to Line from Automatic and you get as below

So now we can see we have both Y axis and x axis in percent. We can format x axis to get in percentage.

 
Now we can drop 80% and 20% reference line on Yaxis and X axis

 

So as we can see 20% of Product Sub Category leads to nearly 55% of the Profit. Since Sample Superstore is a mock up data , pareto principle does not satisfy. Even in real data ,whether Pareto principle satisfies or not depends on the How Data is distributed.

 

For Corporate training and Online Training contact at info@instrovate.com

 

 

Write a comment

Comments

  • manicure (Monday, April 24 17 01:46 pm EDT)

    This paragraph is truly a pleasant one it assists new internet viewers, who are wishing in favor
    of blogging.

  • manicure (Thursday, April 27 17 05:43 am EDT)

    each time i used to read smaller articles or reviews
    that also clear their motive, and that is also happening with this piece of
    writing which I am reading at this time.

  • http://cathpunn.jimdo.com (Sunday, May 07 17 04:18 pm EDT)

    Hi my friend! I wish to say that this article is awesome, nice written and include approximately all vital infos.
    I would like to see extra posts like this.

Please enter the code
* Required fields

Subscribe to our mailing list

* indicates required

Looking for Corporate Training ? Reach out to us at Akriti.Lal@instrovate.com

Reach out to us if you are looking for Corporate Training to Build The Next Generation Analytical Workforce with an in-depth understanding of  Exploratory Data Analysis , Data Visualisation, Data Analytics , AI First , Machine Learning & Deep Learning Training & Consulting helping them to take Data Informed Decision at each stage of the business. 

We understand that At the present times , the Entire Industry is in a Tranformation stage with the Softwares  being rebuilt with Artificial Intelligence Capabilities .

We need SMART WORKFORCE for the SMART SOFTWARES to reap the maximum return . 

 

Whatsapp at +91-9953805788 or email at - akriti.lal@instrovate.com if you would like to know more . 

 

  • Corporate Tableau Training in Gurgaon
  • Corporate Data Analytics Training in Gurgaon
  • Corporate Microsoft Power BI Training in Gurgaon
  • Corporate Microstrategy Training in Gurgaon
  • Corporate Google Data Studio Training in Gurgaon
  • Corporate Python Training in Gurgaon
  • Corporate Advance Analytics in R Programming Training in Gurgaon
  • Corporate Machine Learning Training in Gurgaon
  • Corporate Deep Learning Training in Gurgaon
  • Corporate Data Visualization Training in Gurgaon

 

Address

Instrovate Technologies

Noida, Gurgaon

 

akriti.lal@instrovate.com 

Hit Your Refresh Button To Rise Higher
Print Print | Sitemap
All rights reserved @ Instrovate Technologies
GSTIN : 09AAECI6862K1Z1

Terms Of Services    Privacy Policy    Disclaimer     Refund Policy    


Call

E-mail