How to Create a Performance Dashboard in Looker with AI
Creating a performance dashboard in Looker allows you to centralize your key metrics and monitor your business health in real time. This guide will walk you through setting up a dashboard in Looker, from defining your goals to building the final visualizations, and explore how AI is changing the landscape of business intelligence.
What is a Performance Dashboard?
A performance dashboard is a snapshot of your most important business metrics (KPIs), presented in a simple, visual format. Think of it as the cockpit of your business - it tells you exactly where you're going, how fast you're getting there, and warns you of any potential issues along the way. Instead of swimming through endless spreadsheets, you get a clean, at-a-glance view of what truly matters.
These dashboards can be tailored for any team or goal:
A marketing performance dashboard might track website traffic, cost per lead, ad spend vs. revenue, and campaign conversion rates.
A sales performance dashboard would focus on new deals created, close rates, sales cycle length, and pipeline value.
An executive-level dashboard offers a high-level view, pulling in top-line revenue, profit margins, customer acquisition cost (CAC), and customer lifetime value (LTV) from across the organization.
The goal isn’t to show all your data, it's to show the right data so you can make faster, smarter decisions without getting lost in the noise.
Why Use Looker for Your Dashboard?
Looker (now part of Google Cloud) is a powerful, enterprise-grade business intelligence tool trusted by thousands of companies. It's known for its robust data governance and its unique approach to data modeling, called LookML.
Unlike some tools that require you to pull data into the platform, Looker connects directly to your company’s database or data warehouse (like BigQuery, Redshift, or Snowflake). Instead of writing one-off SQL queries for every chart, data teams create a centralized data model using LookML. This model defines all your business metrics, calculations, and data relationships in one place. For example, your data team can define "revenue" or "active user" once, and then anyone on the team can use that defined metric in their reports, ensuring everyone is working from the same source of truth.
This approach makes Looker incredibly powerful for organizations that need consistent, reliable reporting at scale. Once the heavy lifting is done upfront by a data analyst or engineer, business users can "explore" the data and build their own reports without having to write code.
Step-by-Step: Creating a Dashboard in Looker
Building a dashboard in Looker is a methodical process that flows from strategy to visualization. While the interface is powerful, it has a significant learning curve. Here’s a high-level overview of the steps involved, which typically require a collaboration between business teams and a data analyst.
Step 1: Define Your Goals and Key Performance Indicators (KPIs)
This is the most critical step. A dashboard without clear goals is just a collection of charts. Before you touch any data, ask yourself and your team:
What business question are we trying to answer?
What specific outcomes are we trying to drive? (e.g., increase website conversions, shorten the sales cycle, reduce customer churn)
Which 3-5 metrics will tell us if we are succeeding?
For a marketing team trying to prove the ROI of their campaigns, the core KPIs might be:
Total Spend by Campaign
Conversion Rate
Cost Per Acquisition (CPA)
Return on Ad Spend (ROAS)
Revenue Attributed to Marketing
Write these down first. They will be the blueprint for your entire dashboard.
Step 2: Connect to Your Data Source
Before you can visualize any data, Looker needs access to it. This step is almost always handled by a data engineer or administrator. They will configure a connection to your company’s SQL database or data warehouse where all your raw data resides. This could be data from your CRM, your advertising platforms, your product analytics, and more - all consolidated in one place like Google BigQuery or Amazon Redshift.
Step 3: Model Your Data with LookML
This is Looker's signature feature and where most of the technical work happens. Your data analyst will write LookML code to create a semantic layer on top of your database. In simple terms, they are translating raw database tables and columns into friendly, business-ready dimensions and measures.
For example, a raw database table called transactions might have a column named transaction_amount_cents. The LookML model transforms this by:
Defining a "measure" called "Total Revenue" that sums this column and divides by 100 to get a dollar amount.
Creating a "dimension" called "Transaction Date" by cleaning up the timestamp format.
Joining the
transactionstable to auserstable to link sales to customer information.
This governed model ensures that when someone in marketing and someone in finance both pull "Total Revenue," they get the exact same number calculated in the same way. It's what makes Looker reliable for large teams, but it’s also the most technically demanding part of the process.
Step 4: Build Your "Looks" (Individual Visualizations)
Once the LookML model is ready, business users can start building. In Looker, an individual chart or table is called a "Look." You create one using the "Explore" interface.
In an Explore, you'll see a list of all the business-friendly dimensions (attributes like "Country" or "Campaign Name") and measures (metrics like "Revenue" or "User Count") that your data team defined in the LookML model. You can then:
Select the dimensions and measures you need for your chart (e.g., "Revenue" and "Campaign Name").
Choose a visualization type (bar chart, line chart, pie chart, etc.).
Apply any necessary filters (e.g., filter for "Last 90 Days").
Click "Run" to see Looker generate the visualization by writing the SQL query for you in the background.
After you’ve created a Look you’re happy with, you can save it to a folder.
Step 5: Assemble Your Looks into a Dashboard
Now it's time to put all your pieces together. You can create a new dashboard and begin adding your saved Looks to it. This part is a drag-and-drop experience. You can arrange and resize each tile (Look) on the dashboard grid until you have a layout that tells a clear, cohesive story.
Start with your most important, high-level KPIs at the top, and then place supplementary, more granular charts below to provide additional context.
Step 6: Add Filters and Finalize
To make your dashboard interactive, you can add dashboard-level filters. The most common is a date range filter, which lets any viewer adjust the time frame for all the reports on the dashboard simultaneously. You could also add filters for things like “Campaign,” “Region,” or “Sales Rep” so that different team members can drill down into the data that’s most relevant to them.
How AI Is Augmenting the Looker Experience
Traditional tools like Looker require a lot of manual setup and proficiency. Recognizing this friction, Google is integrating generative AI features into its BI products, primarily through what’s now called Looker Studio and its Duet AI integration.
This technology assists users who are already working within the Looker environment. For example, instead of manually selecting dimensions and measures, a user might type a prompt like, "create a bar chart showing revenue by country for last quarter." The AI can then interpret this request and automatically generate the "Look." It can also provide automated text summaries of dashboards, explaining key trends and outliers in plain language.
These features are powerful for speeding up the workflow of existing analysts and technically savvy users. However, they're augmentations of the classic BI process, not a replacement for it. You still need the underlying data warehouse connection and the meticulously crafted LookML model before the AI can do its work.
The Challenge: What if You're Not a Data Analyst?
For all its power, the Looker workflow highlights a common challenge: building and managing dashboards in traditional BI tools is often slow and requires specialized technical skills. If you're on a marketing, sales, or founding team without a dedicated data analyst on standby, this process can feel out of reach.
The dreaded "reporting bottleneck" is a common pain point. You have a simple follow-up question, like "How did this new campaign affect sales in the UK?" but getting an answer means filing a ticket with the data team and waiting days for them to update the dashboard. This friction stops teams from exploring their data freely and moving quickly on new insights.
Final Thoughts
Building a quality performance dashboard involves a combination of clear strategic goals and the right technical tools. Platforms like Looker provide a robust, reliable framework for data governance and visualization, especially for large organizations with dedicated data teams to manage the underlying LookML models.
If your team doesn’t have data engineers and you need answers faster, we built Graphed to solve this exact problem. Instead of learning LookML or clicking through complex menus, you simply connect your data sources (like Google Analytics, Shopify, and Salesforce) and describe the dashboard you want in plain English. We turn your request into a live, real-time dashboard in seconds, handling all the technical work behind the scenes. It empowers anyone on your team to not only build reports but to ask follow-up questions and get instant insights, just like talking to an analyst.