DotwaveReal analyst problem
Your client sent a CSV with dates in 6 different formats, 23 missing values, and 400 duplicate rows. In Excel, that’s 2 hours of formulas. In Dotwave, it’s a guided 4-step pipeline and a client-ready audit PDF.
The 4-step cleaning pipeline
- Step 1: Data Understanding — profile every column
- Step 2: Data Cleaning — fix nulls and duplicates
- Step 3: Preprocessing — handle outliers and encoding
- Step 4: Feature Engineering — create calculated columns
Every operation, logged in plain English
The audit trail records what changed, how many rows were affected, and whether it was you or the AI. Export it as a client PDF — attach it to your invoice.
- Removed 47 duplicate rows
- Filled 23 blank values in age with the median (43.0)
- Converted invoice_date to real dates
16 cleaning operations
- Remove duplicate rows
- Delete rows with blanks
- Fill with custom value
- Use the average
- Fill with median
- Use the most common value
- Drop column
- Label encode
- One-hot encode
- Turn into numbers
- Remove extra spaces
- Merge the look-alikes
- Rein in the extremes
- Delete outlier rows
- Turn into real dates
- Add calculated field
Non-destructive by design
Your original uploaded file is never modified. Cleaning is stored as a recipe. Remove any step at any time — even months later — and the data restores exactly.