When you make structural changes to a dataset—like adding calculated columns, modifying formulas, or changing column mappings—you need to reprocess the dataset to apply those changes to all your historical data.

What Reprocessing Does

Reprocessing rebuilds your dataset from scratch:

Clears existing data - All processed rows are removed from the dataset
Replays source reports - Every source file/report is re-ingested in chronological order
Applies current calculations - Your new or modified calculated columns run against all data
Rebuilds snapshots - If snapshotting is enabled, snapshot history is preserved and recalculated

This ensures your entire dataset—including historical data—reflects your current column definitions and formulas.

When to Reprocess

You need to reprocess when you:

Add a new calculated column
Modify an existing calculated column formula
Change column include/exclude settings
Update a VLOOKUP table used in calculations
Fix a formula error

You don't need to reprocess when you:

New source data arrives (this processes automatically)
You create charts or visualizations
You change dataset name or description
You modify column display settings (like hiding columns in Explorer)

How to Reprocess

Step 1: Make Your Changes

First, make your structural changes in the dataset settings:

Go to your dataset
Click Settings → Calculated Columns
Add or modify your columns and formulas
Click Save Changes

Step 2: Initiate Reprocessing

After saving, click the Reprocess Data button.

Step 3: Monitor Progress

A progress indicator appears on the Datasets page showing:

Number of source reports being processed
Current processing stage
Estimated time remaining

Step 4: Verify Results

Once complete:

Go to the Preview Data tab to spot-check your calculations
Open Explorer to verify your new columns appear correctly
Check that historical data has the expected values

Processing Time

Reprocessing time depends on:

Factor	Impact
Number of source reports	More reports = longer processing
Dataset size (rows)	Larger datasets take more time
Formula complexity	Complex VLOOKUPs add processing time
External sheet references	Google Sheets lookups add latency

Typical processing times:

Small dataset (< 10K rows, 10 reports): 30 seconds - 2 minutes
Medium dataset (10K-100K rows, 50 reports): 2-10 minutes
Large dataset (100K+ rows, 100+ reports): 10-30 minutes

For very large datasets, consider processing during off-peak hours.

Best Practices

Test Before Full Reprocess

Before reprocessing a large dataset:

Use the Preview Data feature to test your formula on sample rows
Verify the formula logic is correct before committing to a full reprocess
Fix any errors in preview mode—it's much faster than reprocessing twice

Plan for Blended Datasets

If your dataset is a source for other blended datasets:

Those downstream datasets may also need reprocessing
Check the "Reprocess when source updates" option on blended datasets to automate this

Snapshot Considerations

Reprocessing a snapshot dataset:

Preserves your snapshot history
Recalculates values within each snapshot based on new formulas
Does NOT change which records were captured in each snapshot

Avoid Reprocessing Loops

If Dataset A blends with Dataset B, and both have "auto-reprocess" enabled:

Be careful not to create circular dependencies
One dataset should be the "source" and the other the "consumer"

Troubleshooting

Reprocessing Takes Too Long

Simplify complex formulas where possible
Consider breaking large calculated column sheets into smaller, focused columns
Check if external VLOOKUP references are slowing things down

Calculated Column Shows Errors After Reprocess

Open the Calculated Columns editor and check the Values preview row
Look for #REF!, #VALUE!, or #N/A errors
Verify that column references still exist (columns may have been renamed in source)

Data Looks Different After Reprocess

Check if source report format changed between uploads
Verify column mappings are still correct
Review any VLOOKUP tables to ensure they have the expected values

Reprocessing Data