Who should use the Data Cleaning workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
7-step pipeline — follow each step in order
1Setup · Step 1 of 7
Open task pageSQL Query Construction
Prepare inputs and settings through SQL Query Construction before running data cleaning.
Why this matters
SQL Query Construction sets up the foundation for data cleaning; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
2Input · Step 2 of 7
Open task pageFormula Generation
Use Formula Generation to build supporting assets that improve data cleaning quality.
Why this matters
Formula Generation strengthens data cleaning by feeding better supporting material into the pipeline.
What you get
Supporting assets from formula generation are prepared and connected to the main workflow.
3Input · Step 3 of 7
Open task pageGenerate synthetic data
Use Generate synthetic data to build supporting assets that improve data cleaning quality.
Why this matters
Generate synthetic data strengthens data cleaning by feeding better supporting material into the pipeline.
What you get
Supporting assets from generate synthetic data are prepared and connected to the main workflow.
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4Core · Step 4 of 7
Open task pageData Cleaning
Execute data cleaning with Data Cleaning to produce the primary decision-ready insight.
Why this matters
This is the core step where data cleaning actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass decision-ready insight is generated and ready for refinement in the next steps.
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5Refine · Step 5 of 7
Open task pageOrchestrate data workflows
Refine and validate data cleaning output using Orchestrate data workflows before final delivery.
Why this matters
Orchestrate data workflows adds quality control so issues are caught before the workflow is finalized.
What you get
The decision-ready insight is improved, validated, and prepared for final delivery.
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6Refine · Step 6 of 7
Open task pageAutomate data labeling
Refine and validate data cleaning output using Automate data labeling before final delivery.
Why this matters
Automate data labeling adds quality control so issues are caught before the workflow is finalized.
What you get
The decision-ready insight is improved, validated, and prepared for final delivery.
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7Deliver · Step 7 of 7
Open task pageAnnotate training data
Package and ship the output through Annotate training data so data cleaning reaches end users.
Why this matters
Annotate training data is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized decision-ready insight is ready for publishing, handoff, or integration.
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