How does it work?
Operational Data Analysis handles the number-crunching and pattern recognition that slows teams down. Ask questions like:
What's causing the spike in processing times this month?
Show me fulfillment trends for the last quarter by region.
Which vendors have the highest return rates?
Are there any anomalies in last week's production output?
How does this month's throughput compare to our six-month average?
What bottlenecks are affecting our order completion times?
Which processes have the longest cycle times?
The Agent analyzes your operational spreadsheets and connected data sources to deliver insights—without building custom reports or waiting on analyst bandwidth.
Who uses Operational Data Analysis?
Operational Data Analysis is built for operations teams who need to turn raw data into decisions:
Operations Managers identifying bottlenecks and process inefficiencies
Business Analysts tracking KPIs and spotting trends across departments
Supply Chain Teams monitoring inventory levels and fulfillment metrics
Finance Operations analyzing spend patterns and resource allocation
Project Managers tracking delivery timelines and capacity utilization
It's especially valuable when teams need answers fast—during planning cycles, executive reviews, or when investigating why a process isn't performing as expected.
How does Operational Data Analysis use your Knowledge Base?
Operational Data Analysis searches across your connected sources—including Google Sheets, SharePoint spreadsheets, Confluence documentation, Jira tickets, and more—to surface insights from your operational data.
Queries operational spreadsheets and databases for relevant metrics
Cross-references process documentation to provide context
Connects data to specific initiatives tracked in your project management tools
Surfaces historical data to identify patterns over time
Because the Knowledge Base syncs continuously, you're always analyzing the latest information—no manual exports or outdated reports.