Clean Your Data Before It Reaches the Spreadsheet
Trim, replace, convert, and format extracted values automatically as part of your dataset.
The Problem with Raw Scraped Values
Raw scraped values are rarely ready for analysis. A price may arrive as $1,299.00 when your reporting system expects a numeric 1299. A date may appear as 12 Aug 2024 while your database needs 2024-08-12. Without transformation rules, these small differences force a manual cleanup step after every run.
RTILA X includes transformations that run during extraction, before the data ever reaches an export destination. You configure them once in the dataset builder, and the automation applies them to every row. The result is a standardized dataset that is ready for your spreadsheets, databases, or dashboards.
Useful Transformation Types
Transformations cover common cleanup tasks. You can trim whitespace, remove HTML tags, replace characters, extract numbers from strings, convert cases, and combine multiple fields. These rules can run in sequence, so a complex cleanup can be broken into simple, readable steps.
For example, you can strip a currency symbol from a price, remove internal spaces, convert the remaining text to a number, and then store it in a numeric column. That entire conversion happens automatically for every product row in the run.
Working with Dates and Numbers
Date and number handling is often the most valuable transformation. RTILA X can recognize common date patterns and convert them to a standard format. It can also parse localized number formats, such as comma-based thousands separators, and produce machine-readable values.
These conversions reduce errors when data moves between different tools. Instead of exporting free-form text and hoping your destination system interprets it correctly, you control the format before export.
Applying and Reviewing Transformations
Each transformation rule is visible in the dataset field configuration. You can see the input value, the transformation steps, and a preview of the output. If the output does not look right, you can adjust the rule and preview again before committing the full run.
This feedback loop makes transformations approachable even for users who are not comfortable writing formulas. You configure the cleanup visually, verify the result, and then let the automation handle the rest.