How to Create an Employee Dashboard in Looker with AI

Building an employee dashboard is one of the most effective ways to understand the health of your workforce and make informed, data-driven HR decisions. This article will guide you through creating a powerful employee dashboard in Looker and explain how AI is dramatically simplifying the entire process.

What Is an Employee Dashboard and Why Do You Need One?

An employee dashboard is a centralized, visual reporting tool that brings together all your most important workforce data in one place. Instead of spending hours digging through spreadsheets from your HRIS, ATS, and payroll systems, you get a real-time, at-a-glance view of key metrics. It's the difference between navigating with a map and navigating with a satellite GPS.

Creating one helps managers and HR teams easily:

Make strategic decisions: Spot trends in hiring, retention, and performance to allocate resources more effectively.
Improve employee engagement: Track satisfaction scores and turnover rates to identify and address issues before they become critical.
Streamline operations: Monitor metrics like time-to-hire and cost-per-hire to optimize your recruitment process.
Promote transparency: Share relevant performance and headcount data with team leads to empower them with the information they need.

Key Metrics for Your Employee Dashboard

Before you start building, you need to decide what you want to measure. While every business is different, most impactful employee dashboards include a mix of the following KPIs:

Headcount: The total number of employees, often broken down by department, location, or seniority level.
Employee Turnover Rate: The percentage of employees who leave the company over a specific period. You can segment this into voluntary vs. involuntary turnover.
Average Employee Tenure: How long employees typically stay with your company, a key indicator of satisfaction and stability.
Time to Hire: The average number of days it takes to fill an open position, from job posting to offer acceptance.
Cost per Hire: The total cost of recruiting - including ad spend, recruiter salaries, and software fees - divided by the number of hires.
Employee Satisfaction (eNPS): Data from surveys that measure how likely your employees are to recommend your company as a great place to work.
Absence Rate: The rate of unscheduled absences, which can signal issues with burnout or disengagement.
Performance Ratings: An aggregated view of employee performance review scores over time.

Preparing Your Data for Looker

Your dashboard is only as reliable as the data it's built on. Before jumping into Looker, you need to ensure your data is clean, centralized, and ready for analysis. This step, while often overlooked, is the foundation for everything that follows.

1. Identify Your Data Sources

Employee data is often scattered across multiple platforms. You'll likely need to pull information from:

Human Resource Information System (HRIS): Systems like Workday or BambooHR that store core employee data like start dates, roles, departments, termination dates, and demographic information.
Applicant Tracking System (ATS): Tools like Greenhouse or Lever that track your hiring funnel, from application to hire.
Survey Tools: Platforms like Culture Amp or Google Forms where employee engagement and eNPS data lives.
Payroll and Financial Systems: Applications like QuickBooks or ADP that contain compensation and cost data.

2. Centralize Your Data

Looker works best when it can connect to a single, structured data source. The industry-standard approach is to consolidate your data into a cloud data warehouse like Google BigQuery, Snowflake, or Amazon Redshift. This involves setting up data pipelines to automatically pull data from your different SaaS tools and load it into your warehouse.

For smaller teams or simpler projects, you might export CSVs from your different systems and centralize them in a tool like Google Sheets. While more manual, this can be a good starting point.

3. Clean and Structure Your Data

Make sure your data is standardized. Column names should be consistent across tables (e.g., use "employee_id" everywhere), dates should be in the same format, and fields like "Department" should use consistent naming conventions ("Sales" vs. "sales department"). This cleanup prevents frustrating errors during the modeling phase in Looker.

How to Build an Employee Dashboard in Looker (The Manual Way)

Once your data is prepared, you can start building in Looker. Looker (not to be confused with its simpler counterpart, Looker Studio) is an enterprise-grade BI platform that relies on its own modeling language, LookML, to define data relationships and metrics.

Step 1: Connect Looker to Your Data Warehouse

In the Admin panel of Looker, navigate to the Connections section and connect to your data warehouse (e.g., BigQuery). This requires providing credentials that allow Looker to query your data.

Step 2: Create a LookML Project and Model Your Data

This is the most time-consuming and technical step. You'll need to create a LookML project, which defines how Looker interprets and interacts with your database tables.

Create Views: For each table in your database (e.g., employees, hires, terminations), you will create a LookML "view" file. In this file, you define "dimensions" (the fields you group by, like department or start_date) and "measures" (the calculations you perform, like COUNT of employees or AVERAGE tenure).
Create a Model: The model file specifies the database connection and defines "Explores." An Explore joins different views together, allowing you to ask questions across multiple tables (e.g., joining your employees and hires views to analyze hiring trends by department).

Writing LookML requires an understanding of your data schema and can have a steep learning curve. It's what makes Looker incredibly powerful but also challenging for beginners.

Step 3: Create Visualizations (Tiles) from an Explore

With your LookML model in place, you can finally start building visuals for your dashboard.

Navigate to the Explore you created (e.g., "Employee Data").
From the left-hand panel, select the dimensions and measures you want in your chart. For example, to see headcount by department, you would select the department dimension and the employee_count measure.
Looker will generate a data table. Above it, select a visualization type, like a Bar Chart.
Customize the chart's colors, labels, and axes in the visualization settings.
Once you're happy with the chart, click the gear icon and select "Save to Dashboard."

Step 4: Assemble Your Dashboard

Create a new dashboard and then add the visualizations (called "tiles" in Looker) you just created. You will repeat Step 3 for every single metric you want to track - a scorecard for Total Headcount, a line graph for Turnover Trend, etc. Arrange the tiles logically on the dashboard canvas so they tell a clear story.

Step 5: Add Dashboard Filters

For your dashboard to be truly useful, users need to interact with it. Add dashboard filters to allow users to slice the data by common dimensions like Date Range, Department, or Location. This lets a manager for the engineering team, for example, view data specific to their own team.

The Shift to AI: Speeding Up Dashboard Creation

As you can see, the manual process in a powerful tool like Looker requires significant technical skill and time, especially in the data modeling (LookML) stage. This is where AI-driven analytics tools are completely changing the game. Instead of you needing to learn the software's language, the software is learning to understand yours.

Generative AI and Natural Language Prompts

The biggest advancement is the ability to generate reports using plain-English prompts. Instead of tediously selecting dimensions and measures from menus, you can simply describe what you want to see.

For example, instead of manually creating a visualization for turnover, you could just type:

"Show me a line chart of our monthly employee turnover rate for the last 12 months, broken down by voluntary and involuntary."

The AI interprets your request, writes the necessary query in the background, selects the right visualization, and presents you with the chart instantly. This turns a 15-minute task into a 15-second one.

Automated Data Modeling and Insights

The complexity of writing LookML models can create a serious bottleneck, leaving business users waiting on the data team. AI helps bridge this gap in several ways:

Automated Modeling: Newer AI tools can connect to your data sources and automatically infer the relationships between tables, generating a semantic model for you. It knows your Shopify orders table relates to your customers table without you needing to code the join.
Proactive Insights: Beyond just building what you ask for, AI can analyze your data and proactively flag important trends you might have missed. It might send you an alert like, "Time-to-hire for engineering roles increased by 30% this quarter," prompting you to investigate a potential issue in your pipeline.
Data Discovery: Instead of having to know exactly what question you want to ask, the AI can guide you through the process.

This approach democratizes data analysis, allowing anyone on the team - from an HR coordinator to a department head - to explore data and get answers without needing a data analyst to act as an intermediary.

Final Thoughts

Building an employee dashboard in Looker is a valuable exercise for any company looking to elevate its people analytics. The process demands well-prepared data and a solid grasp of LookML, but it delivers powerful, customizable insights that can guide strategy and improve company culture. The end result is a data-driven approach to managing your most valuable asset: your people.

Newer AI-powered tools are revolutionizing this whole process. We built Graphed to remove the technical hurdles that keep teams from getting answers from their data. Instead of spending weeks on data prep and modeling, you can connect your data sources in minutes and use natural language to build the dashboard you need. We've automated away the time-consuming tasks so you can stop manually building reports and start focusing on the insights that drive your business forward.