Looker BI (Beta) turns your warehoused marketing data into interactive dashboards and automated alerts, powered by Google's Looker. This guide explains the key Looker ideas in plain language, shows how your Power My Analytics data flows into Looker, walks through what you set up yourself today, and recommends starter templates so you can get your first dashboard live quickly.

Looker BI vs. Data Studio: Which Is Which?

• Data Studio (formerly Looker Studio) is a free reporting tool available on every plan. It connects to Power My Analytics directly. If you are building reports today, this is probably what you are using.
• Looker BI is a premium, enterprise-grade Looker workspace available on the Custom plan. It reads your data from your own BigQuery dataset and is built for deeper, governed analytics with custom explores and automated alerts.

Who Looker BI Is For

Because Looker BI includes hands-on onboarding and ongoing support from our team, it carries premium pricing. To talk through pricing and confirm it is the right fit, contact our sales team at sales@powermyanalytics.com before getting started.

Looker Concepts in Plain Language

• Project: The container that holds your Looker setup and its link to your data. We create and manage your project for you.
• Connection: The secure link between Looker and your BigQuery dataset (where your Power My Analytics data is stored).
• Model and View: The behind-the-scenes definitions that tell Looker what your data means: which tables to read, what each field is, and how to calculate totals. Our team sets these up so you do not have to.
• Explore: A guided, point-and-click area where you ask questions of one part of your data. Think of it as a sandbox: pick what you want to see, and Looker builds the result.
• Dimension: A way to group or slice your data, such as date, campaign, or country.
• Measure: A number Looker calculates for you, such as total spend, clicks, or revenue.
• Dashboard: A saved page of tiles (charts, tables, and scorecards) that you assemble from your Explores.
• Folder and Group: How access is organized so your team sees your content, and only your content.
• Alert: A rule that emails you automatically when a metric crosses a threshold you choose.

How Your Power My Analytics Data Reaches Looker

1. Connect your sources in the Hub. Go to Sources > + Add new source and connect the platforms you report on (Google Ads, Facebook Ads, GA4, Shopify, and more). Power My Analytics collects and warehouses your data automatically.
2. Shape your data in Data Builder. Build datasets and data tables (single-source or blended) so your numbers are clean and report-ready. See the Data Builder Guide.
3. Export to BigQuery. With BigQuery exports enabled on your Custom plan, your Data Builder datasets are written into your own BigQuery dataset.
4. Looker reads BigQuery. Looker BI connects to that BigQuery dataset and turns it into Explores and dashboards.

Looker BI reads from BigQuery, so a working BigQuery export is the foundation of everything else. This is the main difference from Data Studio, which connects to Power My Analytics directly.

Setting Up Looker BI: What You Do, and What We Do

What you set up yourself (today)

1. Confirm your prerequisites: a Custom plan with BigQuery exports enabled, a Google Cloud project you own, and your Power My Analytics data exported into a BigQuery dataset.
2. Create a BigQuery service account, key, and grant access. This is a one-time setup in the Google Cloud Console that lets Looker read your data securely. The exact click-by-click steps live in our companion article, Using Looker BI with Power My Analytics.
3. Send us your details: your Google Cloud project ID, your service account JSON key file, the BigQuery dataset name you shared, the email addresses to invite, and (optionally) a Git repository URL if you want to own your LookML code.

What our team sets up for you (today)

• We build your Looker workspace and connect it to your BigQuery dataset.
• We create your Explores and views so your data is ready to analyze.
• We invite each email address you provided to your workspace.

Coming soon: We are actively expanding Looker BI so that more of this setup becomes self-service over time, including managing more of your own Looker project directly. For now, project setup is handled by our team to keep your data isolated and your connection reliable during Beta. We will update this guide as new self-service options ship.

Getting the Right Data in Place for Looker

• Build a dataset per reporting area (for example, one for paid advertising, one for e-commerce) so your Explores stay focused.
• Use a growing date range for datasets feeding Looker, so your reports expand as new data arrives. (See date range types in the Data Builder Guide.)
• Create a BigQuery export for each dataset you want available in Looker, so the data lands where Looker can read it.

Recommended First Templates

Building Your First Dashboard in Looker

1. Open an Explore for the data area you want to analyze (we set these up for each of your data tables).
2. Choose your dimensions and measures, for example Date plus Spend plus Conversions.
3. Pick a visualization (line chart, bar chart, table, or scorecard).
4. Save it to a dashboard, or add it as a tile to an existing dashboard.
5. Set an alert (optional) on any tile so Looker emails you when a metric crosses your threshold.

Power users: You can also explore your Power My Analytics data in plain language using the PMA MCP server (Beta) with an AI assistant such as Claude or ChatGPT. It is a fast way to sanity-check numbers or spot trends before you build them into a Looker dashboard. Look for the MCP page (Beta) in your Hub's left navigation to connect an assistant.

Logging In

Questions?

Related Articles

• Using Looker BI with Power My Analytics (the prerequisites and onboarding companion to this guide)
• Custom Plan Guide
• Data Builder Guide
• Power My Analytics' Sovereign SQL Warehouse Solution