Data Flow Design
Data is the lifeblood of every workflow. Learn to move it cleanly.
What You'll Learn
- How data moves between workflow steps
- Transforming data formats without losing information
- The pass-through pattern and the accumulator pattern
- Avoiding the #1 data flow mistake
Data In, Data Out, Data Between
Your trigger fires and brings data with it — a form submission, a new row in a spreadsheet, a webhook payload. That data needs to flow through every step of your workflow, sometimes unchanged, sometimes transformed. Getting this flow right is the difference between a workflow that works and one that breaks at 2am.
Think of data flow like plumbing. Each step is a valve that receives water, does something with it, and sends it on. If the pipes don't match, you get leaks.
Two Essential Data Flow Patterns
Pass-Through: Data enters Step A and flows unchanged to Step B. Example: a customer email address from a signup form gets passed directly to your email platform. No transformation needed — just routing.
Accumulator: Each step adds new data to the payload. Step 1 gets the customer name. Step 2 looks up their order history. Step 3 combines both into a personalized message. The data grows richer at each stage.
Most real workflows use both patterns together. Some data passes through untouched while new data accumulates alongside it.
When Data Needs to Change Shape
Your CRM stores dates as "March 27, 2026." Your database expects "2026-03-27." Your email template wants "Thursday, March 27th." Same information, three formats. Data transformation handles these conversions so each tool gets data in the shape it expects.
AI adds a powerful layer here. Instead of writing rigid transformation rules, you can use AI to interpret messy, inconsistent data and normalize it. A customer writes "next Tuesday" in a form — AI converts that to an actual date. That's a transformation no simple rule could handle.
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