Why performance ensures usability in Tableau - Business Case Study

Scenario Mode

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

📋 Request: Your manager wants you to demonstrate how dashboard performance impacts user experience and usability.

📊 Data: You have sales data by region, product category, and month for the last year. The data includes sales amount, number of transactions, and customer counts.

🎯 Deliverable: Create two Tableau dashboards: one with optimized performance and one with poor performance. Show how faster loading and responsiveness improve usability.

Sample Data

Month	Region	Product Category	Sales Amount	Transactions	Customers
Jan	North	Electronics	12000	150	140
Jan	South	Clothing	8000	100	95
Feb	North	Clothing	9000	110	105
Feb	East	Electronics	15000	160	155
Mar	West	Clothing	7000	90	85
Mar	South	Electronics	13000	140	135
Apr	East	Clothing	8500	95	90
Apr	North	Electronics	14000	155	150
May	West	Electronics	16000	170	165
May	South	Clothing	7500	85	80
Jun	East	Electronics	15500	165	160
Jun	North	Clothing	9200	115	110

1

Step 1: Connect the sales data to Tableau and create a simple bar chart showing total Sales Amount by Region.

Rows: Region; Columns: SUM([Sales Amount])

Expected Result

Bar chart with 4 bars representing North, South, East, West with their total sales.

2

Step 2: Create a second chart showing Sales Amount by Product Category with filters for Month.

Rows: Product Category; Columns: SUM([Sales Amount]); Filter: Month

Expected Result

Bar chart showing sales for Electronics and Clothing, filterable by month.

3

Step 3: Build a dashboard combining both charts and add all months filter. Use quick filters for Region and Product Category.

Dashboard with two charts and filters for Month, Region, Product Category.

Expected Result

Dashboard with interactive filters and charts.

4

Step 4: Optimize performance by limiting data to last 6 months, use extracts instead of live connection, and minimize quick filters.

Data source: Extract filtered to last 6 months; Filters: Remove Region filter; Use context filter for Month.

Expected Result

Dashboard loads faster and filters respond quickly.

5

Step 5: Create a second dashboard with all data, many quick filters, and live connection to simulate poor performance.

Data source: Live connection full data; Filters: Month, Region, Product Category, Transactions; Multiple quick filters enabled.

Expected Result

Dashboard loads slowly and filters respond with delay.

6

Step 6: Compare both dashboards by measuring load time and user interaction speed.

Use Tableau Performance Recorder to measure load and filter response times.

Expected Result

Optimized dashboard loads under 3 seconds; unoptimized dashboard takes over 10 seconds.

7

Step 7: Present findings to manager explaining how faster dashboard improves usability by reducing wait time and frustration.

Summary report with performance metrics and user feedback notes.

Expected Result

Manager understands importance of performance for usability.

Final Result

Dashboard Performance Comparison

+----------------------+---------------------+
| Optimized Dashboard  | Unoptimized Dashboard|
+----------------------+---------------------+
| Load Time: 2.5 sec   | Load Time: 12 sec    |
| Filters respond fast | Filters respond slow |
| Smooth user experience| User frustration     |
+----------------------+---------------------+

✓Optimized dashboards load faster and respond quickly to user actions.

✓Faster dashboards improve user satisfaction and usability.

✓Too many filters and live connections can slow down dashboards.

✓Using data extracts and limiting data improves performance.

Bonus Challenge

Add a calculated field to show average sales per transaction and include it in the optimized dashboard with performance considerations.

Show Hint

Create a calculated field: [Sales Amount] / [Transactions] and add it as a tooltip or a small chart to avoid slowing down the dashboard.