Transform Data FAQs

Yes, the order of functions matters when mapping values. Functions in the Transform Data and Magical Import modules run in the order they are listed, from top to bottom. 

In the example below, placing the cleanup functions before the Map: Values functions ensures the field is already standardized when the mapping function runs, producing more reliable results than mapping first.

The image above shows the same Job Title field in step 2 of the Transform Data module with four cleanup functions applied before the Map: Values functions, in this order: Remove: Leading/trailing whitespace, Remove: Successive whitespace, Remove: Non-letters, and Format: Proper case person, followed by the Map: Values functions mapping the cleaned values to standardized text.

Yes. Best practice is to build separate templates for each field you want to format. This helps you to keep things organized and troubleshoot issues if they arise. If you end up with several related templates you'd like to run together automatically, you can create a Recipe.

In the Transform Data module, when a field has many variations, setting up functions to catch every variation in a single template can be hard to manage and error-prone. Trying to account for all variations in one pass makes it harder to track which variations have already been handled.

To reduce the number of variations before building functions in the Transform Data module, first analyze the field and use the Cleanse Data module to standardize the data and reduce the total number of variations. Once you've reduced the number of variations, you can use the Transform Data module to standardize the remaining values in bulk and automate the process going forward.

The image above shows step 2 of the Transform Data module, with three functions applied to the Company Name field: Format: Proper case company, Remove: Terms with a parameter of "llc|inc|inc.|llp," and Remove: Leading/trailing whitespace.

In the Transform Data and Magical Import modules, mapped values aren't standardized when the Existing Text field in a Map: Values function doesn't account for every variation in the field, including small differences like extra whitespace or errant keystrokes. The Map: Values function only matches the exact variations listed in the Existing Text field, so any variation not listed passes through the function unchanged.

The image above shows three Map: Values functions applied to the Job Title field in the Transform Data module under step 2, each listing raw variations in the Existing Text column (such as "vp sales" and "vp of sales") mapped to a single standardized value in the New Text column (such as "VP Sales"), with no cleanup functions applied before the mapping functions.

To ensure that all variations in a field are captured before applying a Map: Values function, you can use either of the following approaches:

• Use the Cleanse Data module to identify the variations that should be included in the Existing Text field of the Map: Values function.
• Add other Transform Data functions before the Map: Values function to reduce the number of variations. Removing non-letters, removing whitespace, or formatting the field in bulk before mapping can automatically consolidate small inconsistencies, so you need to list fewer variations manually.

Functions run in the order listed, so it works best to include cleanup functions before mapping ones.

The image above shows the same Job Title field in step 2 of the Transform Data module with four cleanup functions applied before the Map: Values functions, in this order: Remove: Leading/trailing whitespace, Remove: Successive whitespace, Remove: Non-letters, and Format: Proper case person, followed by the Map: Values functions mapping the cleaned values to standardized text.

In the Transform Data module, fields added to your Layout and Record Viewer do not appear in the Preview CSV because the CSV file always includes a fixed set of fields, regardless of what is in your Layout. The Preview CSV includes only record identifiers, a deeplink to the record in the CRM, and the Before and After values for the fields being transformed. You can't add or remove any other fields from the Preview CSV.

To see all the fields that are in your Layout and Record Viewer in the Transform Data module, use the Export button under step 1 instead of the Preview CSV. This exports the current data, including all Layout fields, without applying the transform updates, so you can review the full record data before the transformation runs. 

The image above shows the Step 1 Filter tab of the Transform Data module, with a Street Address filter set to "contains" and values including "street," "st.," "drive," "dr.," "avenue," and "av," and an arrow pointing to the Export button located next to the Search button below the filter fields.

Filtering data before running an operation ensures Insycle analyzes only the records you want to focus on. Otherwise, Insycle will look at all records of the selected record type in your CRM. 

There are a few reasons filtering is important:

1. You only want to change a segment of records that meet certain criteria. 
2. If you have thousands of records, the operations can take a very long time. It is more efficient and manageable to filter your data into chunks and use multiple templates than to run operations on your entire database.
3. There may be an overwhelming number of results. If you are trying to analyze your data, adding a filter or narrowing filter criteria lets you work with a relevant, reasonably sized subset of data.

Yes, Insycle can help you format phone numbers with country codes. For more information, see the Format Phone Numbers for Country Codes article.

Yes. If you are having trouble figuring out how to format a specific field, contact customer support via live chat for help building your template.

See the Function Catalog for more details.

See the Function Catalog for more details.

See the Function Catalog for more details.