Data relationships model in Tableau - Dashboard Guide

Dashboard Mode - Data relationships model

Business Question

How can we analyze sales performance by connecting customer, product, and sales data using Tableau's data relationships model?

Sample Data

Customers

CustomerID	Name	Region
C001	Alice	North
C002	Bob	South
C003	Charlie	East
C004	Diana	West

Products

ProductID	ProductName	Category
P001	Widget	Gadgets
P002	Gizmo	Gadgets
P003	Thingamajig	Tools

Sales

SaleID	CustomerID	ProductID	Quantity	SaleDate	Amount
S001	C001	P001	5	2024-01-10	500
S002	C002	P002	3	2024-01-15	300
S003	C001	P003	2	2024-01-20	200
S004	C003	P001	4	2024-01-25	400
S005	C004	P002	1	2024-01-30	100

Dashboard Components

• KPI Card: Total Sales Amount
  Formula: SUM([Amount])
  Result: 1500
• KPI Card: Total Quantity Sold
  Formula: SUM([Quantity])
  Result: 15
• Bar Chart: Sales Amount by Region
  Uses relationship between Sales and Customers on CustomerID.
  Aggregates SUM([Amount]) grouped by [Region].
  Results:
  - North: 700
  - South: 300
  - East: 400
  - West: 100
• Bar Chart: Sales Amount by Product Category
  Uses relationship between Sales and Products on ProductID.
  Aggregates SUM([Amount]) grouped by [Category].
  Results:
  - Gadgets: 1300
  - Tools: 200
• Table: Detailed Sales Records
  Shows SaleID, Customer Name, Product Name, Quantity, SaleDate, Amount.
  Uses relationships to pull Customer Name and Product Name from related tables.

Dashboard Layout

+----------------------+-------------------------+
| Total Sales Amount    | Total Quantity Sold     |
|       [1500]          |        [15]             |
+----------------------+-------------------------+
| Sales Amount by Region                      |
|  [Bar Chart]                               |
+--------------------------------------------+
| Sales Amount by Product Category            |
|  [Bar Chart]                               |
+--------------------------------------------+
| Detailed Sales Records                      |
|  [Table with SaleID, Customer, Product...] |
+--------------------------------------------+
    

Interactivity

Filters on Region and Product Category allow users to narrow down the data shown in all charts and tables. Selecting a region updates the Sales Amount by Region chart, the Product Category chart, and the detailed sales table to show only sales from that region. Similarly, filtering by product category updates all components to show only related sales.

Self Check

If you add a filter for Region = North, which components update and what is the new total sales amount?

Answer: The Sales Amount by Region chart will show only North region data (700). The Product Category chart and Detailed Sales Records table will update to show only sales from customers in the North region. The Total Sales Amount KPI will update to 700.

Key Result

A Tableau dashboard showing total sales, quantity, and sales breakdown by region and product category using data relationships.

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