Qrew Tip #03: Unlocking Insights with Data Analyzer

Community Manager

11 days ago

✏️ Learn how to leverage Data Analyzer to streamline maintenance operations and proactively manage work orders.

Managing work orders effectively is crucial for any maintenance team, especially when it comes to identifying critical issues before they escalate. Quickbase’s Data Analyzer simplifies this process by offering quick insights and deeper predictive analytics. In this guide, we’ll walk through how to leverage Data Analyzer to streamline maintenance operations and proactively manage work orders.

Step 1: Accessing Quick Insights

Navigate to the Work Orders table.
Select Work Order Priority and open Quick Insights.
Choose a value to predict (in this case, “Critical”).
Generate insights with a single click.

Step 2: Installing and Configuring Data Analyzer

To get started with Data Analyzer:

Install the Data Analyzer application.
Navigate to App Settings > Plugins > Advanced Features.
Install the Data Analyzer plugin, name it, and confirm requirements are met.
Open the ML Models Table—it auto-fills with relevant data.
Save your settings and return to the homepage.

Step 3: Creating and Analyzing a Predictive Model

Once Data Analyzer is set up, create a new predictive model:

Select the application and table (e.g., Work Order Priority).
Define the prediction criteria (Critical Work Orders).
Name your model and start the analysis.

The model generates key insights, including:

General Information (creation date, performance rating).
Factor Insights (variables contributing to critical work orders).
Model Details (work order type, job site, asset group, etc.).

Step 4: Deploying Predictive Insights

Generating insights is just the beginning. To make data actionable:

Copy the predictive formula from Data Analyzer.
Go back to the CMMS application and open the Work Orders Table.
Create a new field and paste the formula.
Adjust settings (set decimal places to zero and format as a percentage).
Save changes.

Step 5: Turning Data into Actionable Reports

With the predictive model in place, you can now:

Track the likelihood of a work order becoming critical.
Filter work orders with a prediction probability of 50% or higher.
Create dashboard reports for proactive decision-making.

By integrating these insights, maintenance teams can shift from reactive to predictive maintenance, reducing downtime and improving operational efficiency.