Data relationships model in Tableau - Real Business Scenario

Scenario Mode

👤 Your Role: You are a sales analyst at a retail company.

📋 Request: Your manager wants you to create a report that shows total sales by product category and region, using multiple data tables linked together.

📊 Data: You have three tables: Sales (OrderID, ProductID, RegionID, SalesAmount), Products (ProductID, ProductName, Category), and Regions (RegionID, RegionName).

🎯 Deliverable: Build a Tableau data model using relationships between these tables and create a dashboard showing total sales by product category and region.

Sample Data

Sales
OrderID	ProductID	RegionID	SalesAmount
1001	200	10	150
1002	201	11	200
1003	202	10	300
1004	200	12	250
1005	203	11	100

Products
ProductID	ProductName	Category
200	Chair	Furniture
201	Desk	Furniture
202	Lamp	Lighting
203	Pen	Office Supplies

Regions
RegionID	RegionName
10	East
11	West
12	South

1

Step 1: Connect the three tables (Sales, Products, Regions) into Tableau.

Import Sales, Products, and Regions tables into Tableau's data source.

Expected Result

All three tables are available in Tableau's data source pane.

2

Step 2: Create relationships between tables using common keys.

Link Sales.ProductID to Products.ProductID and Sales.RegionID to Regions.RegionID using Tableau's relationship feature.

Expected Result

Tables are related but not joined, enabling flexible analysis.

3

Step 3: Create a calculated field for Total Sales.

SUM([SalesAmount])

Expected Result

A measure named Total Sales that sums sales amounts.

4

Step 4: Build a worksheet showing Total Sales by Product Category and Region Name.

Rows: [Category], Columns: [RegionName], Values: Total Sales (SUM of SalesAmount)

Expected Result

A table showing total sales for each product category across regions.

5

Step 5: Create a dashboard to display the worksheet with clear titles and legends.

Add the worksheet to a new dashboard, add title 'Sales by Category and Region', ensure color legend is visible.

Expected Result

Dashboard clearly shows sales distribution by category and region.

Final Result

Dashboard: Sales by Category and Region

+----------------+---------+---------+---------+
| Category       | East    | West    | South   |
+----------------+---------+---------+---------+
| Furniture      | 150     | 200     | 250     |
| Lighting       | 300     | 0       | 0       |
| Office Supplies| 0       | 100     | 0       |
+----------------+---------+---------+---------+

Total Sales values are sums of SalesAmount from Sales table linked by ProductID and RegionID.

✓Furniture category has sales in all three regions, with South region having the highest sales.

✓Lighting sales are only in the East region.

✓Office Supplies sales appear only in the West region.

Bonus Challenge

Add a filter to the dashboard to allow users to select specific regions and see updated sales by category.

Show Hint

Use Tableau's filter action on RegionName and apply it to the worksheet in the dashboard.

Practice

1. What is the main purpose of using data relationships in Tableau?

easy

A. To delete unrelated tables automatically

B. To permanently combine tables into one

C. To create duplicate copies of data

D. To connect tables without merging them immediately

Solution

1. Step 1: Understand what data relationships do

   Data relationships link tables by matching key fields but keep tables separate until analysis.

2. Step 2: Compare with other options

   Options A, B, and C describe deleting, permanently combining, or duplicating, which are not the purpose of relationships.

3. Final Answer:

   To connect tables without merging them immediately -> Option D

4. Quick Check:

   Relationships connect tables without merging [OK]

Hint: Relationships link tables without merging data [OK]

Common Mistakes:

• Confusing relationships with joins
• Thinking relationships merge tables immediately
• Assuming relationships duplicate data

2. Which of the following is the correct way to create a relationship between two tables in Tableau?

easy

A. Write a SQL JOIN statement manually

B. Drag a field from one table to the matching field in another table

C. Copy data from one table and paste into another

D. Use the Data Interpreter to merge tables

Solution

1. Step 1: Identify how Tableau creates relationships

   Tableau allows creating relationships by dragging a key field from one table to the matching field in another.

2. Step 2: Eliminate incorrect methods

   Options A, C, and D describe manual SQL, copying data, or using Data Interpreter, which are not how relationships are created.

3. Final Answer:

   Drag a field from one table to the matching field in another table -> Option B

4. Quick Check:

   Drag matching fields to create relationships [OK]

Hint: Drag matching keys between tables to create relationships [OK]

Common Mistakes:

• Trying to write SQL JOINs instead of using drag-and-drop
• Copy-pasting data instead of linking tables
• Confusing Data Interpreter with relationships

3. Given two tables: Orders with fields OrderID, CustomerID and Customers with fields CustomerID, CustomerName, what will happen if you create a relationship on CustomerID and then create a view showing CustomerName and count of OrderID?

medium

A. The view shows each customer with the number of their orders

B. The view shows all orders without customer names

C. The view shows customer names but no order counts

D. The view causes an error due to missing join

Solution

1. Step 1: Understand relationship on CustomerID

   Relationship links Orders and Customers on CustomerID, allowing data from both tables to combine logically.

2. Step 2: Analyze the view with CustomerName and count(OrderID)

   Tableau aggregates orders per customer, showing customer names with their order counts.

3. Final Answer:

   The view shows each customer with the number of their orders -> Option A

4. Quick Check:

   Relationship on CustomerID aggregates orders by customer [OK]

Hint: Relationship on key fields enables combined aggregation [OK]

Common Mistakes:

• Expecting no aggregation without explicit join
• Thinking relationship causes errors
• Assuming customer names won't appear without join

4. You created a relationship between two tables on ProductID, but your view shows incorrect totals. What is the most likely cause?

medium

A. The relationship was created on non-matching fields

B. The tables are physically merged instead of related

C. You forgot to refresh the data source

D. The relationship uses fields with different data types

Solution

1. Step 1: Check the fields used in the relationship

   If the relationship is created on fields that do not match correctly, the data will not combine as expected, causing wrong totals.

2. Step 2: Evaluate other options

   Data type mismatch usually prevents relationship creation; physical merge creates a single table instead of relating; refreshing rarely fixes relationship logic errors.

3. Final Answer:

   The relationship was created on non-matching fields -> Option A

4. Quick Check:

   Incorrect totals often mean wrong relationship fields [OK]

Hint: Verify relationship fields match exactly to fix totals [OK]

Common Mistakes:

• Ignoring field mismatches in relationships
• Assuming refresh fixes relationship logic
• Confusing relationships with joins or merges

5. You have three tables: Sales (with SaleID, ProductID, DateID), Products (with ProductID, ProductName), and Dates (with DateID, Date). How should you set up relationships to analyze total sales by product name and date?

hard

A. Create a relationship from Products to Dates directly

B. Join all three tables into one large table before analysis

C. Create relationships from Sales to Products on ProductID and from Sales to Dates on DateID

D. Create relationships from Products to Sales on ProductName and from Dates to Sales on Date

Solution

1. Step 1: Identify keys for relationships

   Sales table contains foreign keys ProductID and DateID linking to Products and Dates tables respectively.

2. Step 2: Set relationships on matching keys

   Create relationships from Sales to Products on ProductID and from Sales to Dates on DateID to enable combined analysis.

3. Step 3: Eliminate incorrect options

   Joining all tables into one is not necessary; relating Products to Dates directly lacks a direct key; using ProductName and Date for relationships are incorrect keys.

4. Final Answer:

   Create relationships from Sales to Products on ProductID and from Sales to Dates on DateID -> Option C

5. Quick Check:

   Relationships use foreign keys from fact to dimension tables [OK]

Hint: Link fact table keys to dimension tables for analysis [OK]

Common Mistakes:

• Trying to relate dimension tables directly without fact table
• Using descriptive fields instead of keys for relationships
• Joining tables unnecessarily instead of relating