Problem: Pieces of data may exist in multiple places, leading to duplicates when you bring everything together. This gets particularly messy when the entries are slightly different but still refer to the same lead, contact, etc.
Solution: Use a “List Contains” object to filter out rows that exist across multiple data sets. The “fuzzy” matching option enables you to select a match confidence threshold to help with entries that have only minor differences as described above.

Problem: Different data sources can use different date/timestamp formatting. This can cause missing, inaccurate, or duplicate entries when attempting to further analyze your data.
Solution: Standardize all of your dates by using a “Date Conversion” object to describe your desired date format, and applying it to all applicable date fields across your data sets. A common format we see is “YYYY-MM-DD”.

Problem: IDs or other fields shared by multiple data sets can be in different formats, making it impossible to combine the data sets into one.
Solution: Use a “Find/Replace” object to cleanup/standardize the mismatched fields using a regular expression. This will update each time new data comes in.

Problem: Often, email addresses are either incomplete or have obvious typos. Some common examples are “user@gmail”, “user@gmail.con”, and “”. You might be tempted to throw these entries out as junk, but that would mean getting rid of potentially valuable data. What if one of those leads could have been a $100k deal?
Solution: Use a “Find/Replace” object with multiple rules to clean up these common typos.

Problem: Notes fields may seem difficult to parse in an automated way, but you may not have time to go through each one manually.
Solution: Use a “Scoring” object to add scoring rules for each keyword you’re interested in.

Many forms ask for a contact’s full name (rather than first and last). This can help streamline data entry and improve form completion rates. However, it can cause problems when you want to use merge tags to send custom emails. “Hi John Smith” doesn’t feel as personal as “Hi John”.
Solution: You can use the “Name Parser” object to auto-detect the various pieces of a name field, and then split them up into multiple columns as desired.

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