Data cleaning

Real-world data is messy. Columns you don't need, inconsistent spellings, duplicate values, and sensitive information all get in the way of good analysis. Genuics gives you tools to clean up your data directly in the platform so you don't have to go back to a spreadsheet.

Remove columns you don't need

If your dataset has columns that aren't useful for analysis - internal IDs, system timestamps, or fields with no data - you can remove them to keep things focused.

1. Open the dataset and switch to the Schema tab.
2. Find the column you want to remove.
3. Click the more menu (three dots) next to the column name.
4. Select Delete column.
5. Confirm the deletion.

Delete specific rows

Sometimes individual records need to be removed - test entries, duplicates, or rows with bad data.

1. Open the dataset and go to the Data tab.
2. Find the row or rows you want to remove. Use the search bar or scroll to locate them.
3. Select the rows by clicking the checkbox on the left side.
4. Click Delete selected rows in the toolbar.
5. Confirm the deletion.

Deleted rows are removed permanently and the dataset's row count updates immediately.

Remap and normalize values

Inconsistent values are one of the most common data quality problems. The same concept might appear as "US", "United States", "USA", and "U.S." in a single column. This fragments your charts and makes filters unreliable.

Genuics lets you remap values so they are consistent.

1. Open the dataset and switch to the Schema tab.
2. Click the column that has inconsistent values.
3. Click Remap values.
4. You'll see a list of all unique values in the column. For each value you want to normalize, type the replacement value. For example, map "US", "USA", and "U.S." to "United States".
5. Click Apply. Genuics updates every matching row in the dataset.

After remapping, all dashboards and reports that reference this column automatically reflect the updated values. You don't need to rebuild anything.

PII redaction

If your data contains personally identifiable information - names, email addresses, phone numbers - you may want to redact it before sharing dashboards with a wider audience.

1. Open the dataset and switch to the Schema tab.
2. Find the column that contains PII (e.g., an email or phone column).
3. Click the more menu next to the column and select Redact PII.
4. Confirm the redaction. Genuics replaces sensitive values with anonymized placeholders.

Redaction is especially useful for:

• Sharing datasets with external consultants or partners
• Complying with GDPR or other data privacy regulations
• Keeping AI analysis focused on patterns rather than individuals

How cleaning affects downstream dashboards

Every cleaning operation you perform updates the dataset in place. This means:

• Dashboards that reference the cleaned dataset automatically reflect the changes the next time they load. Normalized values appear correctly in filters and groupings.
• Reports pick up the updated data on their next run or refresh.
• AI Insights use the cleaned data for future analysis. If you remap values after insights have been generated, you can re-run insights to get updated results.
• Workflows that trigger on data thresholds evaluate against the current (cleaned) data.

There's no need to manually refresh or rebuild anything after cleaning - changes propagate automatically.

Best practices for data cleaning

• Clean before building dashboards. It's easier to normalize values and remove junk columns before you've built 10 widgets on top of the data.
• Use the Activity tab to track changes. Every cleaning operation is logged in the dataset's Activity tab, so you can see what was changed, when, and by whom.
• Don't over-delete columns. If you're unsure whether a column will be useful, leave it. Unused columns don't affect performance, and it's easier to ignore a column than to re-upload data to get it back.
• Coordinate with your team. If multiple people use the same dataset, communicate before making bulk changes. A value remap that makes sense for one team's analysis might break another team's dashboard.

Next steps

If you need to rename columns or change their types, head to Editing your schema. For an overview of column types and how they affect analysis, see Field types and mapping.