In automation, data doesn’t always arrive in the shape you need. Sometimes it’s clean and structured. More often, it’s messy; nested JSON, text blobs, inconsistent formats, or values scattered across multiple sources.

The Transform Data block in Flowrunner gives you a toolkit for reshaping that raw input into something usable, predictable, and ready for downstream workflows.

What It Does

The Transform Data block applies operations to:

• Key/Value Structures → Map, switch, merge, or extract values.
• Arrays & Lists → Find max/min, filter, flatten, or iterate over elements.
• Strings → Check if they contain text, extract substrings, concatenate values.
  Dates & Times → Format, calculate offsets, or normalize.
• Logical Operations → Run conditional logic inline without building extra branches.
  
  

Each operation is configured through the Expression Editor, so you can mix dynamic values from earlier blocks with static input.

Why It Matters

Without transformations, automations often pass along raw, unhelpful data:

• IDs without context
• Strings too long for a message
• Nested objects buried under multiple keys
• Date values that don’t match your system’s format

Transformations give you control. Instead of just moving data around, you’re actively shaping it to fit your business logic.

This leads to:
✅ Cleaner outputs → Structured data that’s easy to interpret
✅ Easier debugging → Results labeled and visible in TestMonitor
✅ Smarter workflows → Downstream systems get the data they need, not whatever came in

Key Capabilities

The Transform Data block provides a library of operations for reshaping, cleaning, and manipulating data inside your automations. Operations cover:

• Key/Value Structures → update, map, and restructure objects
• Arrays & Lists → iterate, filter, or aggregate values
• Strings → contains, substring, replace, conversions
• Dates → formatting, offsets, comparisons
• Logic → condition checks, switches, comparisons

These operations make it possible to normalize and prepare data without relying on external scripts or custom code.

Notable Operations

Here are a few highlights:

• JSON Operations → reshape nested objects into clean structures
• Multi-Property Mapping → map multiple fields in a single operation
• Contains → check whether a string includes a target value
• Max → find the largest value in a list
• Substring → extract part of a string by index

All transformation results become available in the Expression Editor, ready to be used by any block that follows.

Real-World Examples

Data Mapper‍

Use the Switch operation to map internal codes to human-readable values:

• plan_code: PRO_01 → “Pro Plan”
• plan_code: ENT_99 → “Enterprise”

Find Max Value‍

Aggregate lists (like order totals or engagement scores) and extract the highest value with Max.

Substring Extraction‍

Pull just the first 6 characters from a long string,  perfect for trimming IDs, formatting user handles, or sanitizing inputs.

Contains Check‍

Detect whether a message includes a keyword (“unsubscribe,” “urgent,” etc.) and branch logic accordingly.

Implementation Tips

• Keep it local → Use multiple smaller transformations instead of one giant one. Easier to debug.
• Name clearly → Label transformers with intent (“ExtractCustomerID,” “FormatInvoiceDate”).
• Think ahead → Structure results for the next system consuming them.

The Bigger Picture

The Transform Data block is one of those features that makes Flowrunner feel less like wiring tools together and more like real engineering.

By shaping data at the right stage, you reduce complexity everywhere else: fewer errors, cleaner integrations, and more resilient workflows.

👉 Try it in your next flow: drop in a Transform Data block, experiment with a Switch or Substring operation, and see how much easier your automation becomes when your data is truly under control.

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