How to Create a Project Portfolio Dashboard in Looker

Juggling an entire portfolio of projects can feel like spinning plates - look away for a moment, and something is bound to come crashing down. A project portfolio dashboard gives you a single source of truth to see how everything is progressing at a glance. This guide will walk you through how to create a comprehensive and interactive project portfolio dashboard in Looker, from gathering your data to building the final visualizations.

Before You Build: Planning Your Dashboard

Jumping straight into Looker without a plan is a recipe for a cluttered and confusing dashboard. A few minutes of prep work will save you hours of frustration later. A great dashboard answers important business questions, so start by defining what those questions are.

Your goal is to move from raw data to actionable insights. For a project portfolio, you're likely trying to answer questions like:

Which projects are on track, at risk, or behind schedule?
How are we tracking against our budget for each project and for the portfolio as a whole?
Are our resources allocated effectively, or is one team overloaded?
What is the overall health of our active projects by department or team?

Once you know the questions, you can identify the metrics and data needed to answer them.

Gathering the Essential Data

Your project data probably lives in several different places. The first step is to bring it all together into a database or data warehouse that Looker can connect to, such as BigQuery, Redshift, or Snowflake.

Common data sources for project management include:

Project Management Tools: Jira, Asana, Monday.com, Trello. This is where you'll find data on tasks, statuses, deadlines, and owners.
Financial Software: QuickBooks, NetSuite. This is the source for budget vs. actual spending.
Time-Tracking Software: Harvest, Everhour. This data gives you insight into resource allocation and hours logged.
Spreadsheets: Google Sheets or Excel are often used to bridge gaps or track high-level information that doesn't fit neatly into other systems.

Key Metrics for Your Portfolio Dashboard

To create a truly useful dashboard, you’ll want to track a mix of metrics covering timelines, financials, and project health. Here are a few must-haves:

Project Status: A simple categorical field (e.g., On Track, At Risk, Off Track, Complete).
Budget vs. Actuals: The total allocated budget compared to the actual amount spent to date.
Timeline Progress: The planned completion date versus the projected completion date.
Tasks Completed: The number of tasks completed out of the total number of tasks.
Resource Allocation: Hours assigned to each team member or department across all projects.
Project Owner/Manager: To allow for filtering and a clear line of responsibility.

Ensure your data is clean and standardized. For example, make sure the "Status" field uses the same naming convention (e.g., "On Track," not "On-Track" or "Tracking") across all your data sources. This consistency is vital for accurate modeling in Looker.

Building Your Looker Dashboard: A Step-by-Step Guide

With your data prepped and your plan in hand, you're ready to start building in Looker. The process involves modeling your data with LookML, creating individual reports (called Looks), and then arranging them into a dashboard.

Step 1: Connect Your Data Source

First, make sure Looker is connected to your data warehouse where your project data resides. In the Admin section of Looker, go to Database > Connections and add a new connection or verify your existing one. This involves providing credentials and configuring settings for your specific database (e.g., Google BigQuery, Amazon Redshift).

Step 2: Model Your Data with LookML

This is the most critical - and often most challenging - part of working with Looker. LookML (Looker Modeling Language) is how you define your data's structure, relationships, and calculations. It creates a semantic layer over your database, making it easy for non-technical users to explore data without writing SQL.

Create a LookML Project

Navigate to Develop > Manage LookML Projects and create a new project. Looker will generate a basic set of model and view files for you. Think of these files like this:

Models: Define the data connections and the Explores available for that model.
Views: Represent a single table from your database or a derived table. Here, you define dimensions (your data's attributes, like project_name or status) and measures (aggregations, like a count of projects or a sum of budgets).
Explores: Define the relationships between different views (e.g., joining your project data with your financial data) so you can analyze them together.

Define Your Dimensions and Measures

Open one of your view files. For each field in your project data table, you'll define it as a dimension or a measure. For example, a projects view might look something like this in concept:

This simple example defines a variety of dimensions for project details and a couple of measures to count projects and sum their budgets. The case statement within the status dimension shows how you can clean up and standardize your data directly within LookML.

Step 3: Create 'Looks' for Your Portfolio Visualizations

Once your model is saved and validated, you can start building visualizations from the "Explore" interface. A 'Look' is a single saved query and its visualization, like a single chart or data table.

Example Look 1: Project Status Overview

This visualization will give you a high-level view of portfolio health.

Navigate to an Explore containing your project data.
From the field picker on the left, select the Status dimension and the Projects Count measure.
Run the query. This will generate a simple data table showing the count of projects for each status.
Click the "Visualization" tab and choose a Pie Chart or Donut Chart to display the distribution clearly.
Customize the colors. For example, use green for "On Track," yellow for "At Risk," and red for "Off Track."
Give it a clear title like "Project Status," and click 'Save a Look.'

Example Look 2: Budget vs. Actual by Project

This table helps you spot budget overruns instantly.

In your Explore, select dimensions like Project Name and Project Manager.
Select your measures for Total Budget and Actual Spend. You may want to create a third calculated field to show the variance (Budget - Actual).
Run the query.
Choose the Table visualization type.
Use table calculations or conditional formatting to highlight projects where the actual spend exceeds the budget. For example, make rows with a negative variance turn red.
Save this Look with a name like "Budget vs. Actual Spend."

Step 4: Assemble Your Dashboard

With a few key Looks saved, it's time to put them all together on a dashboard.

Go to your folders and find the dropdown menu to create a new Dashboard.
Give your dashboard a name, such as "Project Portfolio Dashboard."
Once you're in the dashboard editor, click "Add Tile." You can either create a new query from scratch or, more efficiently, add the Looks you just saved.
Arrange your Looks on the screen. Place your most important, high-level charts (like the status donut chart) at the top for quick glanceability. More detailed tables can go below.

Step 5: Add Interactive Filters

A static dashboard is useful, but an interactive one is powerful. Filters allow you and your team to slice the data and find a specific project, check a specific manager’s portfolio, or review projects within a certain date range.

In dashboard edit mode, select "Add Filter."
Choose a field to filter by, for example, Project Manager, Department, or Project Status.
For each tile on the dashboard, configure which LookML field the filter should apply to. For instance, your new "Project Manager" filter should map to the project_manager_name dimension in all the relevant tiles.
Save your dashboard. Now, users can use the dropdown filters at the top of the dashboard to dynamically update all the visualizations.

Best Practices for an Effective Portfolio Dashboard

Keep it Simple: Don't try to cram every possible metric onto one screen. Focus on the most important indicators of portfolio health. Less is often more.
Tell a Story: Arrange your visuals logically. Start with a high-level summary at the top (overall status, total budget), then provide more granular details below (individual project status, resource burn).
Use the Right Visualization: Use pie or bar charts for comparisons, line charts for trends over time, and tables for detailed breakdowns.
Provide Context: A number like "$500,000 spent" is meaningless without context. Show it next to the "$750,000 budget" to make the insight instant. Use Looker's single value visualizations for displaying key KPIs prominently at the top of your dashboard.

Final Thoughts

Creating a project portfolio dashboard in Looker is a valuable exercise that centralizes your view of everything your teams are working on. By clarifying your goals, gathering the right data, carefully modeling it in LookML, and building clear visualizations, you can transform scattered spreadsheets and cluttered project tools into a single source of truth that drives better decision-making.

That said, Looker is an incredibly powerful tool, but it comes with a steep learning curve, especially the LookML part. We built Graphed to remove this exact friction. Instead of manually writing data models and building each chart click-by-click, our platform allows you to connect your data sources - like Jira, Asana, or your spreadsheets - and simply ask for what you want in plain English. For example, you could type "Create a project portfolio dashboard showing budget vs. actuals and project status by manager," and AI will generate the entire interactive dashboard for you in seconds, no configuration or LookML required.