Multiple data sources in Tableau - Real Business Scenario

Scenario Mode

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

📋 Request: Your manager wants a report that shows total sales by product category and customer region. The sales data and customer data are in separate sources.

📊 Data: You have two data sources: Sales data with columns OrderID, ProductCategory, SalesAmount, CustomerID; Customer data with columns CustomerID, Region.

🎯 Deliverable: Create a Tableau dashboard that combines these two data sources to show total sales by product category and region.

Sample Data

OrderID	ProductCategory	SalesAmount	CustomerID
1001	Electronics	250	C001
1002	Clothing	100	C002
1003	Electronics	300	C003
1004	Home	150	C001
1005	Clothing	200	C004
1006	Home	100	C002

CustomerID	Region
C001	North
C002	South
C003	East
C004	West

1

Step 1: Connect to the Sales data source in Tableau.

Use 'Connect' > 'Text file' or database connection to load Sales data with columns OrderID, ProductCategory, SalesAmount, CustomerID.

Expected Result

Sales data is loaded and visible in Tableau data pane.

2

Step 2: Connect to the Customer data source in Tableau.

Use 'Connect' > 'Text file' or database connection to load Customer data with columns CustomerID, Region.

Expected Result

Customer data is loaded and visible in Tableau data pane.

3

Step 3: Create a relationship between Sales and Customer data sources on CustomerID.

In Data menu, use 'Edit Relationships' and set CustomerID from Sales to CustomerID from Customer data.

Expected Result

Tableau links Sales and Customer data on CustomerID for combined analysis.

4

Step 4: Create a new worksheet to build the view.

Drag ProductCategory from Sales data to Rows shelf.

Expected Result

Product categories are listed on rows.

5

Step 5: Add Region from Customer data to Columns shelf.

Drag Region from Customer data source to Columns shelf.

Expected Result

Regions are shown as columns.

6

Step 6: Add SUM of SalesAmount to Text or Label on Marks card.

Drag SalesAmount from Sales data to Text on Marks card and set aggregation to SUM.

Expected Result

Total sales amounts appear in the grid by product category and region.

7

Step 7: Format the view for clarity and add a title 'Sales by Product Category and Region'.

Use Format pane to adjust fonts and add worksheet title.

Expected Result

Clean, readable table showing total sales by category and region.

8

Step 8: Create a dashboard and add the worksheet to it.

Use 'New Dashboard', drag the worksheet onto the dashboard canvas.

Expected Result

Dashboard displays the combined sales by product category and region.

Final Result

Dashboard: Sales by Product Category and Region

+----------------+--------+--------+--------+--------+
| ProductCategory| North  | South  | East   | West   |
+----------------+--------+--------+--------+--------+
| Electronics    | 400    |        | 300    |        |
| Clothing       |        | 100    |        | 200    |
| Home           | 150    | 100    |        |        |
+----------------+--------+--------+--------+--------+

✓Electronics sales are strong in North and East regions.

✓Clothing sales are mainly in South and West regions.

✓Home products sell mostly in North and South regions.

Bonus Challenge

Add a filter to the dashboard to select specific regions and see sales update dynamically.

Show Hint

Use a Region filter on the dashboard and set it to apply to all relevant worksheets.

Practice

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

easy

A. To create multiple dashboards separately

B. To combine data from different places into one view

C. To increase the size of a single data file

D. To export data to Excel automatically

Solution

1. Step 1: Understand the concept of multiple data sources

   Multiple data sources allow Tableau to bring together data from different origins.

2. Step 2: Identify the main benefit

   This lets users analyze combined data in one view without merging files physically.

3. Final Answer:

   To combine data from different places into one view -> Option B

4. Quick Check:

   Multiple data sources = Combine data [OK]

Hint: Multiple data sources combine data from different places [OK]

Common Mistakes:

• Thinking it only creates separate dashboards
• Believing it increases file size
• Confusing it with exporting data

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

easy

A. Drag a field from one source to a matching field in the other source

B. Use the Data menu to export both sources to CSV

C. Manually merge the data files outside Tableau

D. Create a calculated field with SUM() in both sources

Solution

1. Step 1: Identify how Tableau links data sources

   Tableau uses relationships by matching fields between sources to combine data logically.

2. Step 2: Recognize the correct method

   Dragging a field from one source to a matching field in another creates this relationship inside Tableau.

3. Final Answer:

   Drag a field from one source to a matching field in the other source -> Option A

4. Quick Check:

   Relationship creation = Drag matching fields [OK]

Hint: Drag matching fields to create relationships [OK]

Common Mistakes:

• Trying to export data to CSV for relationships
• Merging files outside Tableau instead of inside
• Using calculated fields incorrectly for relationships

3. Given two data sources: Sales and Targets, blended on 'Region', what will happen if a region exists in Sales but not in Targets when blending?

medium

A. The region will be excluded from the view entirely

B. Tableau will throw an error and stop loading data

C. Sales data for that region will show, but Target values will be null

D. Target values will be duplicated for that region

Solution

1. Step 1: Understand blending behavior with unmatched keys

   When blending, if a key exists in the primary source but not in the secondary, Tableau shows primary data but leaves secondary fields blank.

2. Step 2: Apply this to the region example

   Sales data for the unmatched region appears, but Targets show null because no matching region exists there.

3. Final Answer:

   Sales data for that region will show, but Target values will be null -> Option C

4. Quick Check:

   Blending unmatched keys = Primary shows, secondary null [OK]

Hint: Unmatched blend keys show primary data, secondary null [OK]

Common Mistakes:

• Assuming unmatched regions are excluded
• Expecting errors on unmatched keys
• Thinking secondary data duplicates

4. You created a blend between two data sources but notice the data is not matching correctly. What is the most likely cause?

medium

A. You used a calculated field instead of a dimension

B. You forgot to refresh the data extract

C. The dashboard has too many sheets

D. The linking fields have different data types or mismatched values

Solution

1. Step 1: Identify common blending issues

   Blending depends on matching fields; if data types or values differ, matches fail.

2. Step 2: Recognize the cause of mismatched data

   Different data types (e.g., string vs number) or inconsistent values prevent correct blending.

3. Final Answer:

   The linking fields have different data types or mismatched values -> Option D

4. Quick Check:

   Blending mismatch = Data type/value mismatch [OK]

Hint: Check data types and values of linking fields first [OK]

Common Mistakes:

• Blaming dashboard complexity
• Ignoring data refresh needs
• Confusing calculated fields with linking fields

5. You want to analyze sales data from two different databases: one with daily sales and another with monthly targets. How should you combine these in Tableau to compare performance effectively?

hard

A. Use relationships to link the data sources on the month field and blend the data

B. Join the two databases on the day field using an inner join

C. Export both datasets to Excel and merge manually before importing

D. Create separate dashboards for daily sales and monthly targets without linking

Solution

1. Step 1: Identify the best way to combine different granularity data

   Daily sales and monthly targets have different time levels, so joining on day would exclude unmatched data.

2. Step 2: Use relationships and blending on month field

   Linking on month lets Tableau combine data logically without losing unmatched days, enabling effective comparison.

3. Final Answer:

   Use relationships to link the data sources on the month field and blend the data -> Option A

4. Quick Check:

   Different granularity = Use relationships and blend [OK]

Hint: Link on common granularity (month) and blend data [OK]

Common Mistakes:

• Joining on day excludes unmatched data
• Merging outside Tableau wastes time
• Keeping dashboards separate loses combined insight