Getting Started With Data Your Analysis in Tableau 10.0
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During my Corporate Tableau Training in Gurgaon , i get questions many time regarding Getting Started With Data Your Analysis in Tableau 10.0 . In this exercise we will try to visit the Analysis that Tableau help us Quickly Analyze for any particular data set. Again we use “Sample Superstore” as everyone is acquainted it with, but these analyses can be very well done with any other data set and tableau gives a quick picture regarding the data in no time. So let’s get started.
As soon as we connect Tableau to our Data Source below are the operations I prefer doing in order to get a Quick Picture.
1.Measure Name & Measure Value:
Double click on “Measure Values” in Measures. Make sure to convert any Non Measure into Dimension before doing this i.e. Say you have a field Item Id, since it is a number, Tableau will keep it in Measures implying it will do the different level of aggregations to it. However, “Item Id” is the unique identifier and you do not need to do any calculations on it, so this needs to be converted as Dimension.
The moment you click on Measure Values, you get a bar graph of Measure Values with Measure Names I.e. Discount, Profit, Quantity, Sales and Number of Records. This allows you to have a Quick Summary regarding the data set that you have.
So here you can see that you have given a discount of 1561, there are total 9994 records in your data set, a total profit of 286,397 has been made from a total sale 2,297,201 of 37,873 items.
So you get a Summary of the Data that you have.
2.Number of Records: Next in a fresh sheet you can double click on the “Number of Records”- that Tableau Automatically creates as a Measures. This gives you the Number of Record that is present in Excel.
Depending on the uniqueness of the data, the number of records can also give other information, for e.g. if our data set is such that we have a Distinct Customer Name in each record then the Number of records would give the Number of Customer, similarly if our Data set is such that each records give the Distinct Number of Order, then number of records give the total number of Orders Placed. So in that case we can even rename “Number of Records” as “Customer Count” or “Order Count” etc.
That being said in order to understand what the “Number of Records” is actually representing we need to understand the Granularity of our Data. Sample Superstore gives a good opportunity to explore the different level of granularity that real world data could be and i really believe that we need to spend sufficient time during these initial phase of Data Understanding , Data Mapping and Data Designing in order to get the Visualizations correctly from tableau and the way we desire.
3. Understanding the Granularity of the Data : Validate if “Order Id + Product Id” makes unique combination :
Can you find out what does each row in Sample Super Store signifies? Does this mean that each row is Unique for each Customer or Does each Row tells you Distinct Order Id or if the Order Id is getting Duplicated on what grounds??? Spend some time on this
To validate this, make a calculated field “OrderId + Product Id” as below and add “Number of Records” , and you will see that still this is not unique and for some lines we have more than 1 same product id per order
If you click on one of the “Distinct Product & Order” and right click and see the details in Full Data, you will see that the Order Id and Product Id combination is unique, but for the same Order id, the same product was asked in different Quantity or in other words for the same Order id, the same Product was initially asked to give 9 quantities and then said, give me 6 more (which is a valid scenario) .
So after analyzing the sample super store data we can say that each row signifies that it has Distinct Order Id for Distinct Product. So essentially the super store dataset shows the total count of Order and Product.
So the Question for this Analysis could be, how many times a Customer Ordered a certain quantity of Product Id initially and then later changed his mind and asked to Add additional quantities of that Product again for e.g. You called Pizza Hut or Domino’s Pizza and first asked to give 6 Corn Pizza, 1 Garlic Bread, 1 Pizza Burger and then you suddenly realized that you would need additional 9 Corn Pizza more. so this is the scenario in which you will have this data set.
Basically the Question that we can also Ask is
“Which Product has been Ordered the maximum Number of times in any Single Order”?
“We want to award the Customer that has placed an order for maximum quantity of any single product in One Single Order. Name the customer?”
The Answer is Customer “Sanjit Jacobs” on 16th March 2014, placed an order for 16 Xerox 1964. This is the single order with maximum quantity of any one product. Also while Ordering he first said give 14 Xerox and then later asked to add 2 more of them.And the solution for this can use Combined Field to create Unique combination of “OrderId and Product Id” and then check for the Quantity.
The reason we did spend so much of time understanding the Granularity of data is, I believe if you get to understand the granularity of your data set you are half done. This helps you in Data Understanding, Data Mapping and Data Designing. And if you get these 3 things done in the right way, getting the Desired Visualization becomes relatively very easy.
Once we are have a clear picture about the different Measures in our data set , the number of records , the granularity of the data and what the number of records actually represents we are all set to go further and explore the different possible insights that the data has to present. We will deal with these Data Insights in our subsequent blogs using Table Calculations and Calculated Field.
So we can see and appreciate the Beauty of Tableau 10.0 . There are many more cool features in Tableau 10.0
which we will keep visiting one by one . For that keep watching this space and if there
is any topic that you would like to be covered next , feel free to drop a message in the comment below .
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