25 One Dataset, Five Dashboards

What This Chapter Covers

The same dataset can support many different analytical purposes depending on the audience, the question, and the decision context. This chapter demonstrates this principle concretely by building five distinct dashboards from a single source, the Superstore dataset used throughout this book, each designed for a different business function and audience. The five dashboards are: (1) an Executive Revenue Summary, (2) a Sales Operations Dashboard, (3) a Product Performance Dashboard, (4) a Customer Analytics Dashboard, and (5) a Logistics and Fulfilment Dashboard. Each dashboard uses the same underlying data but exposes different metrics, visualisation types, and interaction models appropriate to its specific audience.

flowchart TD
    A[Single Dataset <br> Superstore Orders] --> B[Executive <br> Revenue Summary]
    A --> C[Sales <br> Operations]
    A --> D[Product <br> Performance]
    A --> E[Customer <br> Analytics]
    A --> F[Logistics <br> & Fulfilment]
    style A fill:#e3f2fd,stroke:#1976D2
    style B fill:#e8f5e9,stroke:#388E3C
    style C fill:#fff9c4,stroke:#F9A825
    style D fill:#f3e5f5,stroke:#7B1FA2
    style E fill:#fce4ec,stroke:#C62828
    style F fill:#e0f7fa,stroke:#0097A7

25.1 Dashboard 1: Executive Revenue Summary

Audience and Purpose

Audience: CEO, CFO, VP of Sales, senior leaders who need a 60-second overview of business performance before a board meeting or quarterly review.

Key questions answered: Is the business growing? Are we on track against our annual revenue and profit targets? Which categories are performing and which are underperforming?

Design constraints: Senior leaders spend less than 2 minutes on a dashboard. The design must communicate the most critical findings without any explanation or training. Use large numbers, simple charts, and bold conditional formatting.

Dashboard structure: - Header (15% of canvas): Dashboard title + three KPI tiles: Total Revenue (with YoY % change), Profit Margin (with direction arrow), and Order Count. KPI tiles with green/red conditional backgrounds. - Primary chart (50% of canvas, full width): Monthly Revenue vs. Target line chart. One line for actuals, one dashed line for the rolling target. Forecast extension showing the next 3 months. - Supporting charts (35% of canvas, two columns): Left, Category revenue bar chart (sorted desc, coloured by above/below average margin). Right, Top 5 Sub-Categories by Revenue (simple ranked bar).

Key design choices: - Fixed size: 1366×768px (standard laptop full-screen). - No quick filters on this dashboard, executives should not need to filter; the default state should show the most relevant view. - Subscriptions configured: sent every Monday at 6:00 AM as a PDF to 5 executive recipients.

Key Calculated Fields for Executive Dashboard

Code

# YoY Revenue Growth Rate
(SUM([Sales]) - LOOKUP(SUM([Sales]), -52)) /
 ABS(LOOKUP(SUM([Sales]), -52))

Code

# KPI Status for conditional formatting
IF SUM([Sales]) >= [Monthly Revenue Target]
THEN "On Target"
ELSE "Below Target"
END

Code

# Profit Margin
SUM([Profit]) / SUM([Sales])

25.2 Dashboard 2: Sales Operations Dashboard

Audience and Purpose

Audience: Regional Sales Managers, Sales Directors, operational leaders who manage teams and territories.

Key questions answered: Which territories are performing and which are at risk? Who are the top and bottom salespeople this month? How are we tracking against this month’s target in each region?

Design constraints: Sales managers use this dashboard daily, sometimes multiple times per day. It must respond to quick filtering (by region, by month) and support drilldown to individual sales reps. Interactive exploration is a priority.

Dashboard structure: - Header: Monthly filter (date slider), Region quick filter (checkbox list). Title: “Sales Operations, Month Ending [dynamic date]”. - Primary chart (60% of canvas, left column): US State map coloured by % of monthly target achieved (sequential blue: above target; red: below target). Click a state to filter the right column. - Right column (40% of canvas, stacked): - Top right: Sales Rep performance bar chart (filtered by selected state from map). - Bottom right: Weekly trend line for the selected state showing the rolling target line.

Key interaction: Clicking a state on the map filters both the sales rep bar and the weekly trend line to that state. A “Clear Selection” button (parameter action) resets the state filter.

Key chart, Target Attainment Gauge:

Code

# Target attainment percentage for current month
SUM(IF DATEPART('month', [Order Date]) = DATEPART('month', TODAY())
    AND DATEPART('year', [Order Date]) = DATEPART('year', TODAY())
    THEN [Sales] ELSE 0 END) / [Monthly Revenue Target]

25.3 Dashboard 3: Product Performance Dashboard

Audience and Purpose

Audience: Head of Merchandising, Product Managers, Category Analysts, people who make decisions about product mix, pricing, and inventory.

Key questions answered: Which products generate the most revenue and profit? Which products have declining sales trends? Which products have the highest return rates? What is the relationship between discount rate and profit margin by product?

Design constraints: Product analysts are comfortable with complex charts and multiple dimensions. This dashboard can have more analytical depth and smaller mark sizes than the executive dashboard. Filtering by Category and Sub-Category is essential.

Dashboard structure: - Filter row: Category multi-select, Sub-Category search filter, Date range. - Primary chart (50% canvas, left): Product scatter plot, Sales on X, Profit Margin on Y, bubble size = Quantity Sold. One bubble per Sub-Category. Reference lines at average Sales and average Margin create four quadrants. Set action updates “Selected Products” set. - Right column (50% canvas, stacked): - Top: Sales trend by Sub-Category (line chart, last 24 months). Filtered by set action. - Middle: Discount vs. Margin relationship (scatter, filtered by selected Sub-Category). - Bottom: Return rate bar chart by Sub-Category.

Key analytical finding this dashboard surfaces: Products in the “High Revenue / Low Margin” quadrant (top-right of the scatter with below-average margin) are candidates for pricing review, they generate volume but not profit.

Discount-Margin scatter:

Code

# Average discount for the current mark context
AVG([Discount])

Code

# Profit margin for the scatter
SUM([Profit]) / SUM([Sales])

25.4 Dashboard 4: Customer Analytics Dashboard

Audience and Purpose

Audience: Customer Success team, CRM analysts, Marketing, people making decisions about customer acquisition, retention, and targeting.

Key questions answered: Who are the highest-value customers? Which customer segments have the best and worst margins? How has customer count and average order value changed over time? Are there customers at risk based on declining order frequency?

Design constraints: Customer data is sensitive, consider row-level security if this dashboard is published broadly. The audience is technically sophisticated and can handle multi-dimensional analytical views.

Dashboard structure: - Header: Segment filter, Region filter, Date range. - Primary chart (50% canvas, full height left): Customer scatter, Revenue on X, Order Count on Y, bubble size = Profit, colour = Segment. One bubble per customer. Clustering enabled (3 clusters). Hover action shows customer tooltip with full stats. - Right column (50% canvas, stacked): - Top: Customer count trend over time (line by Segment). - Middle: Average Order Value by Segment (bar, filtered by date). - Bottom: Customer Lifetime Value distribution (histogram).

Customer Lifetime Value calculation:

Code

# CLV: total revenue per customer in the data window
{FIXED [Customer ID]: SUM([Sales])}

At-risk customer identification:

Code

# Days since last order (for detecting inactive customers)
DATEDIFF('day',
  {FIXED [Customer ID]: MAX([Order Date])},
  TODAY()
)

25.5 Dashboard 5: Logistics and Fulfilment Dashboard

Audience and Purpose

Audience: Operations Director, Logistics Manager, Customer Service team, people managing the physical delivery of products.

Key questions answered: What is the average days-to-ship by Ship Mode and Region? Which orders are at risk of late delivery? What is the return rate, and which products and regions have the highest return rates? Are there seasonal patterns in fulfilment performance?

Design constraints: This dashboard may be used by operational staff who are not analysts, it should be simple, action-oriented, and highlight exceptions (late shipments, high-return-rate products) prominently.

Dashboard structure: - Header: Date range filter (defaulting to last 30 days). Urgency KPI tile: “Orders Shipped Late This Month: N” with red background if N > threshold. - Primary chart (50% canvas, left): Days to Ship box plot by Ship Mode and Region. Reference line at the SLA threshold (e.g., 5 days for Standard Class). Marks above the reference line coloured red. - Right column (50% canvas, stacked): - Top: Return Rate by Sub-Category (bar, sorted desc, red bars for rates above 10%). - Bottom: Monthly fulfilment volume with a line for on-time rate overlaid on a dual axis.

Days to Ship calculated field:

Code

# Fulfilment time in days
DATEDIFF('day', [Order Date], [Ship Date])

Late shipment flag:

Code

# Flag orders exceeding the SLA threshold
IF DATEDIFF('day', [Order Date], [Ship Date]) > [SLA Days]
THEN "Late"
ELSE "On Time"
END

25.6 Comparing the Five Dashboards

What Changes Across Dashboards, and What Stays the Same

Aspect	Executive	Sales Ops	Product	Customer	Logistics
Primary audience	C-suite	Sales managers	Product analysts	CRM team	Ops team
Primary question	Are we growing?	Are we on target?	What sells best?	Who are our best customers?	Are we delivering on time?
Primary chart type	Line + KPI	Map + bar	Scatter	Scatter	Box plot
Interactivity level	Low (read-only)	High (filter/drill)	Medium	Medium	Low (exception highlight)
Number of filters	0 (pre-filtered)	3	4	3	2
Update frequency	Weekly (subscription)	Daily (on demand)	Ad hoc	Weekly	Daily

What stays the same: The same Superstore data source, the same field names, the same calculated fields (reused across multiple dashboards), the same colour system (Tableau Blue primary, green/red semantic), and the same font hierarchy. Consistency across dashboards in the same workbook makes the analytics environment feel like a coherent system, not a collection of individual charts.

25.7 Summary

Key Concepts at a Glance

Dashboard	Primary Metric	Key Chart	Key Action
Executive Revenue	Revenue vs. Target	Line + KPI tiles	Subscription delivery
Sales Operations	Territory target attainment	State map	Click-state filter
Product Performance	Revenue / Margin quadrant	Scatter with clusters	Set action
Customer Analytics	Customer CLV and frequency	Customer scatter	Hover tooltip
Logistics	Days to Ship / Return Rate	Box plot + dual-axis	Exception highlighting

Shared Element	Value
Data source	Superstore (single connection)
Primary colour	Tableau Blue (#4E79A7)
Alert colour	Red (#E15759) / Green (#59A14F)
Font	Tableau Regular / Arial
Layout structure	Vertical container > Horizontal containers

Applying This in Practice

The most efficient way to build a multi-dashboard analytics environment is to build all the foundation work once: the Tableau Prep flow that cleans the data, the published certified data source with standardised field names, the core calculated fields (Profit Margin, Days to Ship, YoY Growth), and the workbook-level formatting defaults. With this foundation in place, each new dashboard is a new arrangement of existing building blocks, not a new data preparation project. Invest the time upfront in the foundation, and each subsequent dashboard delivers value at a fraction of the initial cost. This is the compound interest of analytics infrastructure.