Google BigQuery is the serverless data warehouse many companies on Google Cloud use for their largest datasets. For asset-heavy businesses that often means telematics from vehicle fleets, readings from thousands of sensors and years of work order history, all in tables too large for a spreadsheet.
BigQuery can answer almost any reliability question in seconds, if someone writes the right SQL. In most maintenance teams nobody does. Agentic CMMS gives your AI agent a governed way into BigQuery: you ask about failure rates or backlog in plain words, the agent writes and runs the query against the datasets you allow and explains the answer.
What your AI agent can do with Google BigQuery
- Compute MTTR from raw events. "What was mean time to repair for each depot last quarter?" The agent works from your work order and downtime tables and returns one figure per depot.
- Find repeat failures. "Which vehicles had the same fault code more than twice in 60 days?" It groups telematics fault events by vehicle and code.
- Compare planned and reactive work. "How did the planned work share change after we introduced the new PM schedules in March?"
- Size the backlog. "How many labour hours of open work do we have at each site, by craft?"
- Look for early signs. "Did engine temperature trend up in the week before each of the last ten breakdowns?" The agent joins breakdowns with sensor readings.
- Explore a dataset. "What tables are in the maintenance dataset, and when were they last updated?"
- Prepare a table for a report. "Write a query that gives weekly MTBF per asset class for the dashboard." With write access and if your admins allow it, the agent can save the result as a new table.
Google BigQuery with your other systems
- When BigQuery flags a truck with rising fault codes, check its live location and status in Samsara before you pull it off the road.
- Compare long-term trends in BigQuery with current readings from AWS IoT SiteWise to decide whether a pump needs attention now.
- Open a work order in UpKeep for each asset the query identifies as a repeat failure.
- Check the same KPI in Looker, where it is defined in LookML, when you need the governed figure for a board report.
Large data, small questions
The value of BigQuery for maintenance lies in its history: you can see what happened before every failure in the last five years. The value of an agent is that people can finally ask about it. Point the connection at curated views, give it a read-only account, and let your planners and reliability engineers ask the questions they used to leave unasked.
Each person approves their own agent, owners and admins limit tools by role, and every query the agent runs is written to the activity log.