DATA OPS
Daily BigQuery revenue-dip scan posts a plain-English digest to Notion
Each morning it scans yesterday's BigQuery revenue and conversion metrics for unexpected dips, uses OpenAI to write a plain-English summary of what moved and why.
How it runs
The automated pipeline, trigger to output.
- TriggerMorning schedule fires
- ActionPull yesterday's metrics vs. baseline from BigQueryBigQuery
- LogicKeep only movements past the dip threshold
- ActionDraft plain-English digest with OpenAIOpenAI
- OutputPublish dated digest page to NotionNotion
What it does
Every morning it pulls the prior day's revenue, orders, and conversion metrics from BigQuery, compares each against its trailing baseline, and flags anything that dropped more than expected. It hands the flagged movements to OpenAI to generate a concise, non-technical narrative, then publishes a dated digest page in Notion that leadership can read over coffee without opening a dashboard.
When to use it
Use it when execs want a daily "what changed and should I care" readout instead of a wall of charts. It turns raw warehouse numbers into a short story about the business, escalating only the metrics that genuinely moved.
How it works
- 1A morning schedule fires.
- 2BigQuery returns yesterday's metrics versus their trailing baselines.
- 3A logic step keeps only the movements that exceed the dip threshold.
- 4OpenAI drafts a plain-English digest explaining what moved and the likely drivers.
- 5Notion publishes a dated digest page for the exec team.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect NotionPages, databases, comments.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Data Ops workflows
Weekly BigQuery Cost Trend Sheet and Exec Digest
Compiles week-over-week BigQuery scheduled-query cost by owner and dataset into a Google Sheet with trend columns.
Daily BigQuery Scheduled-Query Cost Attribution to Owners
Each morning, totals the prior day's on-demand bytes-billed per scheduled query, maps each query to its owner from a label, and posts a per-owner cost leaderboard to Slack.
BigQuery Per-Team Budget Breach Alert to PagerDuty
Tracks month-to-date BigQuery scheduled-query spend per team and, when a team crosses its monthly budget, pages the team's on-call in PagerDuty and snapshots the spend breakdown…
dbt source freshness watcher with severity-routed alerts
Checks Snowflake loaded-at timestamps against each dbt source's freshness SLA, then routes warnings to Slack and hard breaches to a PagerDuty incident so stale data never…
dbt orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
Raw Sensor Telemetry Archive to BigQuery
Captures every incoming building sensor reading via webhook, normalizes the payload into a consistent schema.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.