Your first clean and export

This article takes you from a freshly uploaded dataset to a clean Excel file you can hand off. You will meet the 4-step pipeline, use the first two steps to fix missing values, and export the result. It is the shortest complete loop in Dotwave, and once you have done it once the rest of the product makes sense.

The 4-step pipeline

When you open a dataset, Dotwave presents cleaning as four ordered steps. Each has a distinct job:

1. Understand your data — get an honest picture of what you are working with before you touch anything.
2. Clean the data — fix the concrete problems: missing values, duplicates, and inconsistent entries.
3. Preprocess — reshape and standardize columns so the data is ready for analysis.
4. Feature engineering — derive new columns that make your dashboards more useful.

You do not have to complete every step for every dataset. For a first clean, the first two steps are usually all you need.

Step 1 — Understand your data

Step 1 is a summary of your dataset's health. At a glance you see the total row count, the column count, the total number of null values across the data, and how many duplicate rows exist. These four numbers tell you how much work is ahead before you commit to any transformation.

Above the detail you can switch between the Numeric and Categorical column tabs. The numeric tab shows distributions and ranges for measures like revenue or quantity; the categorical tab shows the distinct values in text columns, which is where you spot inconsistent spellings and stray categories. Spend a moment here — a clear read of the problems makes the next step obvious.

Step 2 — Clean the data

Step 2 is where you fix things. The Missing values table lists every column that contains nulls, how many it has, and a suggested fix appropriate to the column's type. For a numeric column with gaps, a common and safe choice is to fill the missing entries with the column's median.

Median is a good default because, unlike the mean, it is not dragged around by outliers — a handful of very large values will not distort the number you use to fill the gaps.

Export as Excel

With the missing values handled, you can export the cleaned data. Open the Export menu and choose Download as Excel (.xlsx). Dotwave generates a workbook with a single sheet named Cleaned Data, and the cleaned values — including the medians you just filled in — are written into the file. Open it in Excel and the nulls you fixed are gone. If you prefer a plain-text handoff, the same menu offers a CSV download instead.