Who should use the Automate data preparation workflow?
Teams or solo builders working on data 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 pageAutomate data analysis
Prepare inputs and settings through Automate data analysis before running automate data preparation.
Why this matters
Automate data analysis sets up the foundation for automate data preparation; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
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2Input · Step 2 of 7
Open task pageVisualize Data
Use Visualize Data to build supporting assets that improve automate data preparation quality.
Why this matters
Visualize Data strengthens automate data preparation by feeding better supporting material into the pipeline.
What you get
Supporting assets from visualize data are prepared and connected to the main workflow.
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3Input · Step 3 of 7
Open task pageExtract data from documents
Use Extract data from documents to build supporting assets that improve automate data preparation quality.
Why this matters
Extract data from documents strengthens automate data preparation by feeding better supporting material into the pipeline.
What you get
Supporting assets from extract data from documents are prepared and connected to the main workflow.
4Core · Step 4 of 7
Open task pageAutomate data preparation
Execute automate data preparation with Automate data preparation to produce the primary decision-ready insight.
Why this matters
This is the core step where automate data preparation 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 pageAnalyze genomic data
Refine and validate automate data preparation output using Analyze genomic data before final delivery.
Why this matters
Analyze genomic data 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.
6Refine · Step 6 of 7
Open task pageAnalyze qualitative data
Refine and validate automate data preparation output using Analyze qualitative data before final delivery.
Why this matters
Analyze qualitative data 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 pageAnalyze biological data
Package and ship the output through Analyze biological data so automate data preparation reaches end users.
Why this matters
Analyze biological 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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