Core Workflows
What Data Analysis is meant to do
Data Analysis turns datasets into structured blocks the rest of the platform can reason about.
What this helps with: Help users use uploaded data in a way that supports research and impact tracking.
What to upload
Data Analysis is for structured datasets the system can profile and transform into usable signals.
CSV and TXT are the supported file types for direct dataset analysis.
The goal is to create meaningful blocks and metrics the rest of the platform can use.
This is different from uploading documents into the evidence layer.
What it produces
The output of Data Analysis is not just a file preview. It should become usable project intelligence.
Analysis blocks can hold metrics, charts, and synthesized interpretations.
Those blocks can support downstream views such as Impact Tracker.
The quality of labels and metric values matters because downstream surfaces rely on them.
Common problems
Most failures come from bad file shape or weak labels.
Make sure files are plain CSV or TXT where expected.
Use clean headers and meaningful values.
If the resulting blocks look vague, the downstream use will also be weak.