How to Use Quick Analysis in Looker with AI

Finding needle-in-a-haystack insights from your data can feel like a full-time job, even with powerful tools like Looker. Fortunately, Looker's Quick Analysis feature uses generative AI to do the initial heavy lifting for you, spotting trends and outliers in your data automatically. This guide will walk you through exactly how to use Quick Analysis to speed up your data exploration and uncover meaningful insights in just a few clicks.

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What Exactly is Quick Analysis in Looker?

Quick Analysis is an AI-powered feature built directly into Looker Explores. Instead of manually slicing and dicing your data dimensions and measures to find something interesting, you can run a query and let Looker’s AI analyze the results for you. It automatically generates plain-language summaries of what it finds, such as key contributors to a metric, unusual trends, or significant changes over time.

Think of it as having a data analyst instantly review your report and point out the highlights. It's designed to bridge the gap for users who aren't data scientists but need to make data-driven decisions. It scans your query results and presents its findings on easy-to-read "cards," helping you quickly understand the story your data is telling without building dozens of different visualizations.

The core benefit is speed. It tackles the first, often tedious, pass of data exploration, allowing you to jump straight to the parts of your data that actually matter.

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Before You Begin: Required Setup and Permissions

Before you can use Quick Analysis, there are a couple of things you or your Looker administrator need to have in place:

• Generative AI Features Enabled: Your Looker admin must enable the "Generative AI features" setting. You can ask them to check this under Admin > General > Labs. This is a simple toggle they need to switch on.
• User Permissions: You need at least explore permissions for the model you’re working with. If you can already use the Explore feature, you likely have the necessary permissions to also run Quick Analysis on your queries.

If you don’t see the Quick Analysis option, have a quick chat with your Looker administrator to make sure these settings are configured correctly.

How to Use Quick Analysis: A Step-by-Step Guide

Once you’ve confirmed the prerequisites are met, using the tool is straightforward. Here’s how to get your first AI-generated insights in under a minute.

Step 1: Open an Explore and Select Your Data

First, navigate to an Explore, which is Looker’s interface for building queries. Think of an Explore as a starting point for asking a question of your data. For this example, let's say we’re analyzing website traffic data over the last 90 days.

In the field picker on the left, select the data points you want to analyze. We'll start with a simple query:

• Dimension: Traffic Source
• Dimension: Created Date (set to "Day")
• Measure: Users Count

Step 2: Add Filters and Run Your Query

Next, add a filter to narrow down the data to a specific timeframe. In the "Filters" section, add a filter for Created Date and set it to "is in the last 90 days."

With your fields and filter in place, click the Run button. Looker will display a data table and a default visualization based on your query.

At this point, you could start manually pivoting, filtering, and changing chart types to find trends. But we're going to let the AI do it for us.

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Step 3: Launch Quick Analysis

Once your query results are loaded, you'll see a Quick Analysis button with a sparkle icon in the Explore's title bar. Click this button to start the AI analysis.

Looker will immediately start processing the data you just queried. In just a few moments, a panel will appear with the results.

Step 4: Review the AI-Generated Insights

The Quick Analysis panel displays several insights as individual "cards." Each card presents a finding in plain language, often accompanied by a simple visualization.

For our website traffic query, you might see insights like:

• Descriptive summary: A simple overview, such as "From your data, you are looking at the Total Users Count by Created Date and Traffic Source for the last 90 days."
• Factor analysis: "Google contributed the most to Users Count, accounting for 65% of the total in the last 90 days."
• Time-based trends: "Users count increased overall during this period, with the biggest jump occurring around November 25th."
• Comparison insights: It might compare the top two traffic sources, noting, "Google's user count consistently outperformed Direct traffic throughout the 90-day period."

Each of these cards gives you a verified, data-backed insight that you can use as a starting point for deeper investigation, share with your team, or add to a dashboard.

Practical Examples of Quick Analysis in Action

The real power of Quick Analysis comes from applying it to your specific business questions. Here are a few relatable examples of how different teams can use it.

Example 1: Analyzing Marketing Campaign Performance

• The Goal: A marketing manager wants to know which campaigns are performing best.
• The Initial Query:
• Potential AI Insights:

These insights instantly guide the manager. They can immediately investigate the ROI of the Holiday Sale campaign and try to replicate the success of the high-efficiency ad set.

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Example 2: Understanding E-commerce Sales Trends

• The Goal: An e-commerce director wants a quick overview of product sales this quarter.
• The Initial Query:
• Potential AI Insights:

Armed with this information, the director can investigate the October dip in Home Goods (was there a stock issue?) and consider running weekday promotions to smooth out sales revenue.

Tips for Getting the Best Results

Quick Analysis is powerful, but you can get even more value from it by keeping these simple tips in mind.

• Start with a Simple, Clear Query: The quality of the AI's insights depends on the quality of your initial data query. Instead of selecting 20 different fields at once, start with a focused question. For example, begin with Sales by Region before adding in Product Line, Customer Segment, and other variables.
• Use Insights as a Starting Point: The generated insights aren't the final answer, they're the beginning of a deeper investigation. If the AI flags a spike in website traffic from a specific country, use that insight to create a new, filtered Explore focused only on that country to understand what's driving it.
• Combine AI with Human Expertise: The AI can tell you what happened, but your job is to figure out why. If Quick Analysis points out a drop in sales, your knowledge of a recent marketing campaign, competitor actions, or seasonal trends provides the critical context the AI lacks.
• Add Interesting Finds to a Dashboard: If Quick Analysis surfaces a particularly useful insight, you can easily save its visualization to a new or existing Looker dashboard. This allows you to monitor that specific metric over time without having to re-run the analysis manually.

Final Thoughts

Looker's Quick Analysis demonstrates how AI can streamline data exploration, making it faster and more accessible for everyone on your team. It automates the first pass of analysis, helping you spot important trends, patterns, and outliers that you might have otherwise missed, all without requiring deep technical expertise.

We built Graphed to push this principle of simplicity even further. We believe that getting answers from your data shouldn't require learning a complex BI tool or even clicking through menus to build a query. Instead of building an Explore, we let you connect all your marketing and sales data - from Shopify and Salesforce to Google Analytics and Facebook Ads - and simply ask questions in plain English. Just type “Show me my top performing ad campaigns by ROI last month,” and we’ll instantly build a live dashboard for you. It’s all about turning hours of reporting work into quick, conversational insights so you can get back to growing your business.