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How to Create a Tracking Dashboard in Tableau with AI

Cody Schneider•

Creating a good tracking dashboard in Tableau can feel like both an art and a science, leaving many people stuck fiddling with chart settings for hours. The good news is that artificial intelligence is making this process much faster and more intuitive. This article will walk you through how to use Tableau's AI features to create dashboards more effectively, from planning your layout to uncovering hidden insights in your data.

First, What Is a Tracking Dashboard?

A tracking dashboard is a visual, at-a-glance report that monitors your key performance indicators (KPIs) over time. Instead of digging through dense spreadsheets or separate platform reports, a dashboard gives you a centralized view of what’s happening in your business.

Common examples include:

  • A marketing dashboard tracking website sessions, lead conversions, and ad channel performance.

  • A sales dashboard showing deal pipeline, win rates, and rep performance.

  • An e-commerce dashboard displaying revenue, average order value, and abandoned cart rates.

Tableau is a popular choice for building these because it’s incredibly powerful. You can connect to almost any data source and create beautiful, interactive visualizations. However, that power comes with a notoriously steep learning curve, which is where AI comes in to help.

Planning: The Most Important Step You Shouldn’t Skip

Jumping straight into Tableau without a plan is a recipe for a cluttered, confusing dashboard. Before you drag and drop a single chart, take a moment to answer these three questions:

1. What C-Level question are you answering?

A great dashboard answers a specific, high-level business question. Trying to show everything at once will only create noise. Start with a foundational question and work backward from there.

  • "Are our Facebook campaigns actually driving store sales?"

  • "How is our new sales team ramping up this quarter?"

  • "Is an increase in website traffic leading to more trial sign-ups?"

2. Who is the audience?

A dashboard for your CEO should look very different from one for your in-the-weeds marketing team. The CEO needs a high-level overview of revenue and ROI, while the marketing manager needs to see campaign-level metrics like Cost-Per-Click and conversion rates.

  • Executive (C-Suite): Focus on outcomes and bottom-line impact. Keep it simple with a few key charts showing revenue, customer acquisition cost (CAC), and overall ROI.

  • Managers: Focus on team and channel performance. Show performance by sales rep, traffic by marketing channel, or spend vs. return by ad campaign.

  • Individual Contributors (Analysts, Marketers): Focus on granular, tactical data. These dashboards can be more detailed, allowing users to drill down into specific ad sets, landing pages, or product SKUs.

3. What are your Key Performance Indicators (KPIs)?

Based on your primary business question and your audience, select 3-5 primary KPIs that will form the backbone of your dashboard. Any more than that and the core message gets lost.

For a marketing dashboard, you might choose:

  • Total Website Sessions

  • New Leads Generated

  • Cost Per Lead (CPL)

  • Conversion Rate (Lead to Customer)

  • Return On Ad Spend (ROAS)

The AI Assist: Speeding Up the Tableau Workflow

Traditionally, building a dashboard in Tableau involved a methodical, time-consuming process: connect your data, create individual charts one by one on different worksheets, and then carefully arrange them all on a dashboard canvas. While this method still works, AI features are changing the way we interact with data, both inside and outside of Tableau.

Using LLMs for Brainstorming and Planning

Sometimes, the hardest part is knowing where to start. Tools like ChatGPT can act as a knowledgeable brainstorming partner to help you outline your dashboard's structure before you even open Tableau.

Instead of staring at a blank screen, you can ask for suggestions like:

  • "I need to build a sales performance dashboard in Tableau for my manager. What are the 5 most important KPIs I should include, and what are the best chart types to visualize them?"

  • "Suggest a good layout for an e-commerce dashboard. I want to show sales trends, top-selling products, and marketing channel performance on a single screen."

This process gives you a blueprint and clarifies your thinking. You get a solid list of charts to build - like a bar chart for sales by rep, a line chart for monthly revenue, and a pie chart for channel breakdown - saving you from trial-and-error.

Important consideration: Treat these tools as creative assistants, not data processors. Never upload sensitive company data or CSVs to public AI models. Your prompts should be about structure and ideas, not about your specific, confidential numbers.

Using Tableau's Built-In AI: Ask Data and Explain Data

Once you are in Tableau, you can leverage its native AI to do the heavy lifting of chart creation and analysis. Tableau has two main features that make this work: Ask Data and Explain Data.

1. Ask Data: Create Charts with Plain English

Ask Data lets you build visualizations just by typing what you want to see. Instead of manually dragging data fields onto rows and columns, you can simply type a question, and Tableau will generate the chart for you. This dramatically lowers the barrier to entry for beginners and saves time for experienced users.

Here’s how it works:

  1. Connect your data source to Tableau (e.g., Salesforce, Google Analytics, or a SQL database).

  2. Open a new worksheet and select your data source.

  3. Instead of dragging and dropping fields, find the "Ask Data" option.

  4. Start typing! For example, if you're connected to Google Analytics data, you could type:

    • show me sessions in the last 90 days as a line chart

    • total users by country as a map

    • compare sessions from organic vs paid traffic this month

Tableau interprets your sentence and generates the corresponding visualization. This lets you quickly create all the "puzzle pieces" - your individual worksheets - before assembling them into your final tracking dashboard.

2. Explain Data: Find the "Why" Behind Your Numbers

After your dashboard is built, you'll inevitably spot something unexpected: a sudden spike in traffic, a dip in sales, or one sales rep outperforming all others. Finding the cause of that anomaly can require hours of manual deep-diving, slicing and dicing your data again and again.

Tableau’s Explain Data uses statistical models to automate this discovery process. You can select a single data point in your chart (like the lowest sales day of the month), and Tableau will analyze all its other data to surface potential explanations.

For example, if you use Explain Data on a dip in sales, it might automatically uncover insights like:

  • "The average Deal Size was significantly lower on this day."

  • "This day had an unusually low number of records from the 'West' Sales Region."

  • "The majority of records on this day came from the 'Low-Intent' Lead Source."

Without AI, you would have had to manually create separate charts for deal size, region, and lead source to find those connections yourself. Explain Data does it for you in seconds, turning your dashboard into not just a tracking tool, but an analytics discovery engine.

Putting It All Together: A Sales Dashboard Example

Let's imagine you're building a dashboard for a sales manager monitoring quarterly performance. The goal is to see which reps are hitting their targets and identify coaching opportunities.

  1. AI-Assisted Planning: You ask an LLM, "Suggest KPIs for a sales rep dashboard." It recommends tracking Revenue by Rep, Win Rate by Rep, and Average Deal Size. It also suggests using bar charts for comparison between reps and a line chart to show the total revenue trend over time.

  2. Connecting Data: You open Tableau and connect your company's Salesforce or HubSpot CRM data.

  3. Building Charts with AI: You use Ask Data to rapidly create your worksheets:

    • For the first chart, you type: "show me sum of deal amount by sales rep for this quarter as a horizontal bar chart"

    • For the second, you type: "show total closed deals by month this quarter as a line chart"

    • For the third, you create a new calculated field for "Win Rate" and then type: "what is the win rate for each rep?"

  4. Dashboard Assembly: You create a new dashboard and drag your three newly created worksheets onto the canvas, adjusting the layout as planned.

  5. Insight Generation with AI: While reviewing the dashboard, you notice one rep has the highest revenue but a surprisingly low win rate. You click on her bar in the Win Rate chart and run Explain Data. The AI automatically discovers that her Average Deal Size is 3x higher than anyone else's - she works fewer, larger deals. This crucial context transforms the conversation from "why is her win rate low?" to "how can we learn from her approach to securing larger deals?"

In this workflow, AI didn't replace your critical thinking, it augmented it at every step, allowing you to move faster and uncover deeper insights with less manual effort.

Final Thoughts

Building a tracking dashboard in Tableau has historically been a manual craft requiring significant time and technical skills. Integrating AI, whether for planning out ideas or using built-in features like Ask Data and Explain Data, can radically speed up the process and make powerful analysis more accessible for everyone on your team.

While AI features make complex tools like Tableau easier, we started Graphed because we believe getting insights shouldn't require learning a complex tool at all. We allow you to connect all your marketing and sales platforms in one click and then build entire real-time dashboards just by describing what you want in simple, natural language. It’s a way to get answers from all your data in seconds, skipping the workbook-and-chart setup entirely and going straight to the insights you need.