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How to Create a Compliance Dashboard in Looker

Cody Schneider••8 min read

Tracking compliance across your organization can often feel scattered and overwhelming. A well-built Looker dashboard centralizes all your key compliance metrics, transforming reactive check-ins into proactive oversight. This guide will walk you through planning your dashboard, modeling the data, and creating the right visualizations to give you a clear, real-time view of your compliance status.

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Why Use Looker for a Compliance Dashboard?

While spreadsheets can track small-scale compliance data, they struggle with real-time updates, multiple data sources, and user-specific access controls. Business intelligence tools are a huge step up, and Looker is particularly well-suited for compliance reporting for a few key reasons:

  • Centralized Data: Looker connects directly to your company's database, allowing you to pull in data from various sources (like your CRM, HR system, and backend application logs) into a single, unified view.
  • Real-Time Insights: Dashboards in Looker query your live data, so you’re always seeing the most current information, not a week-old export.
  • Controlled Access: You can set granular permissions, ensuring team members, auditors, or executives only see the data relevant to their roles.
  • Actionable Alerts: Looker allows you to set up alerts that trigger when a metric crosses a certain threshold - for example, notifying a supervisor if a critical security issue remains unresolved for more than 48 hours.

Planning Your Dashboard: Before You Build

Jumping straight into building without a clear plan is the fastest way to create a confusing and unused dashboard. Take some time to think through these three critical planning stages first.

1. Define Your Key Compliance Metrics (KPIs)

What do you actually need to track? "Compliance" is a broad term. Your dashboard should focus on the specific metrics that matter most to your business based on your industry and obligations. Sit down with stakeholders from IT, HR, Legal, and Finance to identify the most important questions your dashboard needs to answer. Common compliance KPIs often fall into these categories:

IT &amp, Security Compliance (e.g., SOC 2, ISO 27001)

  • Access Control: Number of active users with administrator vs. standard privileges.
  • Vulnerability Management: Time-to-patch for critical security vulnerabilities.
  • Data Access Logs: Number of unusual or failed access attempts to sensitive data.
  • Employee Onboarding/Offboarding: Percentage of new hires who have completed security training, average time to de-provision accounts for departing employees.

Data Privacy Compliance (e.g., GDPR, CCPA)

  • Consent Management: Percentage of new users who opt-in to marketing communications.
  • Data Subject Requests (DSRs): Average time to complete a "right to be forgotten" or data access request.
  • Cookie Compliance: Rate of valid cookie consent gathered from website visitors.

HR &amp, Internal Policy Compliance

  • Required Training: Completion rates for mandatory anti-harassment, ethics, or security training by department.
  • Policy Acknowledgement: Percentage of employees who have signed off on the latest employee handbook or acceptable use policy.

Don’t try to track everything. Start with your top 5-7 most critical metrics and build from there.

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2. Identify Your Data Sources

Next, figure out where the data for these KPIs lives. You’ll likely need to connect to multiple systems. For example:

  • Employee training completion rates? This data is in your Learning Management System (LMS) or HR platform (like BambooHR or Workday).
  • Time to patch vulnerabilities? This lives in your security scanning tool (like Qualys or Tenable) or your IT ticketing system (like Jira or ServiceNow).
  • GDPR consent rates? This might be stored in a field within your CRM (like Salesforce or HubSpot) or in your marketing automation platform (like Marketo).

Your data engineering team will need to ensure this data is piped into a centralized data warehouse (like BigQuery, Snowflake, or Redshift) that Looker can connect to.

3. Sketch Your Layout

Finally, draw a simple mock-up of what you want your dashboard to look like. Think about visual hierarchy. What’s the most important number? That should be big and at the top. How do you want to group your charts? It can be helpful to organize your dashboard into sections by compliance area (e.g., Security, HR, Data Privacy).

A simple layout might include:

  • A high-level "Overall Compliance Score" at the top.
  • A row of single-value tiles for key risk indicators.
  • Charts showing trends over time on the left.
  • Deeper dive tables and breakdowns on the right.

This simple sketch will serve as your blueprint and make the building process much faster.

Step-by-Step: Building Your Compliance Dashboard in Looker

With your plan complete, you’re ready to start building in Looker. The process generally follows these five steps, which bridge the gap between your raw data and a finished dashboard.

Step 1: Connect to Your Data

First, an admin in your Looker instance needs to establish the connection to your data warehouse. You do this in the Admin > Database > Connections panel. Looker provides detailed documentation for connecting to all major data platforms. Once connected, a Looker developer can use that connection to create a project.

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Step 2: Model the Data with LookML

This is what makes Looker unique and powerful. LookML (Looker Modeling Language) is where a developer defines your business logic. Instead of writing raw SQL in every chart, you define dimensions (your data's attributes, like "Department" or "Training Status") and measures (aggregations of your data, like "Count of Employees" or "Average Completion Time") once. This centralizes definitions and ensures everyone calculates metrics the same way.

For our HR compliance training metric, a developer might create a LookML view file named employee_training.view.lookml that looks something like this:

view: employee_training {
  sql_table_name: public.employee_training_records ,,

  dimension: employee_id {
    primary_key: yes
    type: number
    sql: ${TABLE}.employee_id ,,
  }

  dimension: department {
    type: string
    sql: ${TABLE}.department ,,
  }

  dimension: training_name {
    type: string
    sql: ${TABLE}.training_name ,,
  }

  dimension: completion_status {
    type: string
    case: {
      when: {
        sql: ${TABLE}.completed_date IS NOT NULL ,,
        label: "Completed"
      }
      else: "Incomplete"
    }
  }

  measure: completed_count {
    type: count_distinct
    sql: ${employee_id} ,,
    filters: [completion_status: "Completed"]
  }

  measure: assigned_count {
    type: count_distinct
    sql: ${employee_id} ,,
  }
}

This code defines familiar fields like department and smartly creates a custom dimension completion_status and reusable measures like completed_count. This is the foundational model that business users will use to build reports.

Step 3: Define Explores for Business Users

Once the LookML views are created, a developer groups them together into "Explores." An Explore is the starting point for a user to query data. For instance, you could create an "Employee Training" Explore that joins the employee_training view with an employees view. To the end user, this just looks like a webpage where they can select fields from a list, like "Department," "Training Name," and "Completed Count," to build a report without writing any code.

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Step 4: Build Your Dashboard Visuals ("Looks")

Now, using the Explores you’ve created, you can start building the individual charts and visuals (called "Looks" in Looker) for your dashboard. Navigate to your Explore and start selecting the dimensions and measures you planned out.

Choose the best visualization for each metric:

  • For Training Completion Rate: Use a Bar Chart to compare completion rates across different departments.
  • For Overall Compliance Score: A Single Value visualization with a comparison to the previous period works great to show top-level health at a glance.
  • For Open Security Issues by Severity: A Pie Chart or Donut Chart can quickly show the proportion of issues that are critical, high, medium, or low priority.
  • For Compliance Trends Over Time: A Line Chart is perfect for tracking a metric like the number of compliance incidents per month over the last year.
  • For New User Consent Rate: A Gauge Visualization can show you how close you are to your consent rate goal (e.g., 75%).

As you build each chart, save it to a new dashboard that you'll name something clear and descriptive like "Quarterly Compliance Review."

Step 5: Add Filters and Interactivity

A static dashboard provides a snapshot, but an interactive one provides answers. Add dashboard-level filters to let your users drill down into the data. Essential filters for a compliance dashboard include:

  • Date Range: Allow users to view data from "last 30 days," "this quarter," "last year," etc.
  • Department or Business Unit: Help managers see how their specific team is performing.
  • Compliance Type or Regulation: Let users filter the entire dashboard to see just GDPR-related issues or just SOC 2 controls.

You can also enable cross-filtering, which allows a user to click on a segment in one chart (like the "IT Department" in a bar chart) and have all the other charts on the dashboard automatically filter to just show data for that segment.

Final Thoughts

Building a robust compliance dashboard in Looker organizes your data, clarifies your status, and allows you to catch issues before they become problems. By planning your metrics, modeling your data carefully in LookML, and choosing clear, interactive visualizations, you can create a powerful central source of truth for your entire organization's compliance efforts.

The process of connecting sources and learning a new data modeling language can understandably feel like a steep climb. We built Graphed to remove that friction completely. You can connect your marketing and sales data sources in just a few clicks, and our AI-powered analyst builds dashboards for you based on simple, plain-English questions. Instead of writing code, you just ask things like, "show me a chart of user privacy consent rates from Salesforce this quarter," and get your answer in seconds.

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