Workflow Automation Checklist for Analytics Teams

Automation projects usually start with a single report and balloon into dozens of recurring jobs. The more stakeholders you support, the more important governance and observability become. Here is a quick checklist we use when helping customers scale up their ActionOR workflows.

1. Track sources and refresh cadence

Every dataset should have an owner and an expected refresh interval. Annotate it directly in your workflow so on-call operators know when to expect fresh data.

2. Validate before transformation

Add schema validation and sample counting as the first step in your workflow. Catching row drift early saves your downstream charts from mysterious dips or spikes.

3. Document the happy path

Use notes or inline comments to describe the primary intent of the workflow. Future teammates can then experiment with confidence and avoid removing critical steps.

4. Alert on anomalies, not everything

Choose thresholds that matter—failed runs, missed SLAs, or sudden output variance. Excessive alerts train teams to ignore the real incidents when they appear.

5. Keep humans in the loop

Automations shine when the right people can intervene. Publish a dashboard, send a Slack summary, or route approvals into your CRM once the workflow completes.

Following these steps will help your spreadsheet automations stay healthy even as new data sources come online. Ready to evolve the checklist into action? Download our free tools and explore what a full ActionOR workflow can automate for you.