How to Create a Risk Management Dashboard in Looker with AI

Building a static risk report in a spreadsheet is like looking at a single photograph of a speeding train - it only tells you where it was, not where it's going. To truly manage business risks, you need a live video feed, and that comes from a dynamic dashboard. This guide will show you how to build a powerful risk management dashboard in Looker and how AI can help you find insights hidden in your data.

Why Use a Dedicated Tool like Looker for Risk Management?

Many teams still track risks manually in Google Sheets or Excel. Every week, someone downloads data from Salesforce, Shopify, and QuickBooks, spends hours cleaning it up and mashing it together in a spreadsheet, then presents the findings in a meeting. By the time someone asks a follow-up question, the data is already old, and the process has to start all over again. Half the week is gone just trying to figure out what happened last week.

A tool like Looker changes the game. Connected directly to your live data sources, a Looker dashboard gives you a real-time command center for your business health. Instead of looking backward, you can see what’s happening right now.

Here’s why it’s a better approach:

A Single Source of Truth: Everyone in the company sees the same data and the same metrics, updated in real time. This ends the debate over whose spreadsheet is the “right” one.
Reliable Business Logic: Looker uses its own modeling language, LookML, to define metrics. You can define what a high-risk customer or active lead means once, and that definition is applied consistently across all reports.
Automated & Real-Time: The dashboard refreshes automatically. No more manual CSV downloads. This frees up your team from the data-wrangling treadmill so they can focus on analysis and strategy.
Custom Alerts: You can set up notifications for when a key risk metric crosses a certain threshold, allowing you to react instantly instead of discovering a problem days later.

Step 1: Plan Your Dashboard by Identifying Your Key Risk Indicators (KRIs)

A good dashboard tells a story, and you need to write the outline before you start building. Begin by asking two questions: “What risks do we need to monitor?” and “Who is this dashboard for?” The KRIs for an executive team will be higher-level than those for a frontline marketing or sales manager.

Group your potential risks into categories and define the specific metrics you’ll use to track them. Your data is likely spread across different platforms, so note where each piece of information lives.

Financial Risk Indicators

These metrics give you a view of your company’s financial health and stability. Often, this data comes from accounting software like QuickBooks, payment processors like Stripe, or your CRM.

Cash Burn Rate: How quickly are you spending cash reserves? This is critical for startups and businesses managing tight cash flow. (Data Source: QuickBooks, Financial Statements)
Accounts Receivable (AR) Aging: How many invoices are overdue? A rising number of payments past 30, 60, or 90 days can signal a cash flow crisis. (Data Source: QuickBooks, Stripe)
Revenue Concentration: What percentage of revenue comes from your top 5 customers? Over-reliance on a few large clients is a major risk. (Data Source: Salesforce, HubSpot, Stripe)

Operational Risk Indicators

Operational risks relate to your company's day-to-day processes, systems, and people.

Customer Support Ticket Volume & Resolution Time: A sudden spike in support tickets can indicate a product issue, while slowing resolution times point to a strained team. (Data Source: Zendesk, HubSpot Service Hub)
System Uptime/Downtime: For SaaS or e-commerce businesses, any downtime directly translates to lost revenue and customer trust. (Data Source: Uptime monitoring tools, server logs)
Employee Turnover Rate: High turnover, particularly in key departments, can disrupt projects and impact morale. (Data Source: HR systems like BambooHR, or even spreadsheets)

Sales & Marketing Risk Indicators

These KRIs focus on the health of your customer acquisition and retention engine.

Customer Churn Rate: What percentage of customers are you losing each month or quarter? This is arguably the most important metric for any recurring revenue business. (Data Source: Shopify, Stripe, Salesforce)
Sales Pipeline Velocity: How quickly are deals moving from one stage to the next? A slowdown can predict a future revenue shortfall. (Data Source: Salesforce, HubSpot CRM)
Marketing Channel Dependency: Does 90% of your website traffic come from Google SEO? If a single algorithm update can sink your business, that's a risk worth tracking. (Data Source: Google Analytics)

Once you have your list of KRIs, sketch out a simple layout on a whiteboard or piece of paper. Place the most important, high-level numbers at the top, and organize related charts together in sections for clarity.

Step 2: Connect Your Data and Model it in LookML

Looker works by connecting to a central data warehouse (like Google BigQuery, Snowflake, or Redshift) where all your data sources are collected. Setting up this data pipeline is often the most technical part of the process, but it’s what enables real-time analysis across different platforms.

Once connected, you build a model using LookML. Don't let the term intimidate you. Think of LookML as a translation layer. It sits on top of your messy database tables and allows you to define your business rules in a straightforward way. For example, instead of a data analyst writing complex SQL code to calculate revenue, you define the Revenue metric once in LookML. From then on, any user can simply drag Revenue into a report without needing to know the underlying programming.

This is where you’ll define your KRIs as specific metrics and dimensions. For instance, you could create a dimension called Customer Risk Tier based on purchase frequency and lifetime value. This makes your data much easier – and safer – for business users to explore.

Step 3: Build Your Risk Dashboard Visualizations in Looker

With your data connected and modeled, building the dashboard is the fun part. Here’s a basic flow for how you might build out a few tiles.

Example 1: A Big Number Tile for At-Risk Customers

Let's create a tile that shows the number of high-value customers who haven’t made a purchase in 90 days.

Start in an Explore: An Explore in Looker is a starting point for a query. Choose an Explore that contains customer and order data.
Select your data: From the field list on the left, select Customer ID (or a similar dimension).
Apply filters: Set up two filters:
- Last Purchase Date is more than 90 days ago
- Lifetime Value is greater than $1,000
Run the query: Looker will now show you a table of all customers meeting this at-risk criteria.
Visualize and Save: Change the visualization type to Single Value. Looker will automatically show you the count of customer IDs. Give it a clear name like High-Value Customers At Risk and save it to your dashboard.

Example 2: A Line Chart for Lead Conversion Rate Trends

A single number is useful, but a trend tells a better story. Let's see if your lead-to-customer conversion rate is getting better or worse.

Start in an Explore with your sales and marketing data.
Select your data: Choose a time-based dimension like Lead Creation Date (Month) and a measure you’ve created in LookML for Lead to Customer Conversion Rate.
Run and visualize: Run the query and select the Line Chart visualization. You'll instantly see how your conversion rate has trended over time.
Customize and Save: Add a trend line to see the overall direction. A sustained downward trend is a clear red flag. Name it and save it to your risk dashboard.

Example 3: Setting Up an Alert for Spikes in Support Tickets

A dashboard is useless if you don't look at it. Alerts automate the monitoring for you.

Create the tile first: Build a chart that shows daily support ticket volume.
Set the alert: Click the bell icon on the tile and configure the alert conditions. For example, you can set a condition like: "Alert me if the value increases by more than 30% compared to the previous day."
Configure notifications: Set it up to send an email or a Slack notification to your customer support team lead. Now, they’ll be notified automatically of unusual activity.

Continue this process for all your KRIs, using different visualization types (bar charts, pie charts, maps) that best represent the data.

Beyond the Dashboard: Using AI for Deeper Risk Analysis

Having a well-built dashboard is a huge leap forward. But it's often the start of a deeper investigation, not the end. When you see a spike in churn, your immediate next question is, "Why?" Answering that in Looker requires you to go back to the Explores, slice the data by different dimensions (Which product? Which region? Which marketing cohort?), and search for the root cause.

This is where the learning curve for traditional BI tools becomes steep. Becoming proficient at this kind of ad-hoc analysis in Looker or Tableau can take dozens of hours of training. Non-technical users often can't answer their own follow-up questions and must rely on a data analyst, creating a bottleneck.

This is precisely the problem AI is beginning to solve. Modern analytics tools are making it possible to have a conversation with your data. Instead of navigating menus and applying filters, you can simply ask questions in plain English:

"What was our churn rate last month for customers acquired through Facebook Ads?"
"Show me our on-time delivery rate by warehouse for the past six months."
"Compare revenue from new customers vs. repeat customers in Q3."

This conversational approach radically lowers the barrier to entry. Your entire team, from the CEO to a junior marketing associate, can drill down into the data and uncover risks without needing a data science degree. It empowers the people closest to the problems to find the answers themselves, leading to a much more agile and data-informed organization.

Final Thoughts

Building a Looker risk management dashboard transforms your abstract business risks into concrete metrics you can monitor and act upon daily. It replaces outdated, manual reporting with a live, centralized view that helps your entire organization make smarter, proactive decisions instead of constantly reacting to problems.

While Looker is an incredibly robust platform, the time and technical expertise required to build and maintain these powerful dashboards can be a significant hurdle. At Graphed, we're focused on demolishing that barrier. After a one-click process to connect your data sources, you can build real-time dashboards just by describing what you want to see. Instead of a multi-step process to filter and visualize your at-risk customers, you can just ask, "Show me a list of customers who spent over $1,000 but haven't bought anything in the last 90 days," and get the answer instantly. This frees you up to spend your time managing risk, not learning how to become a BI expert.