Finding duplicate records

Updated 4 months ago

Ensuring that your database is free of duplicates is important. It helps you keep a single view of your supporters, ensures you don't accidentally contact someone twice, and keeps your reporting accurate.

Beacon makes it easy to find and merge these duplicates, using the standard filtering system.

link Finding duplicates

When filtering records (e.g. people), you can use the is not unique option to find records that have duplicate values.

For example, if you wanted to find a list of all organisations that have duplicate names, you could use the following filter:

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Similarly, you could do the same for people, based on their email addresses, names, or phone numbers:

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When you find duplicates, you can merge them to keep your data nice and tidy:

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link Considerations for different field types

The "is not unique" filter option is currently available for the following field types:

  • Person name
  • Short text
  • Email
  • Phone
  • Number
  • Currency
  • Percent
  • Location

As such, you can use this feature to find duplicate values for any kind of record (not just people and organisations!), including custom record types and fields that are specific to you.

There are few special considerations when using the "is not unique" option that are helpful to be aware of:

link Short text

  • Uniqueness is case-insensitive, so "Beacon" and "beacon" would count as the same value.

link Person name

  • Uniqueness is case-insensitive, so "Richard" and "richard" would count as the same value.
  • If you filter by "Name (full)", uniquess is determined by only the first and last name. The title (e.g. Mr) and middle name are ignored.

link Currency

  • Beacon makes it possible to store values across multiple currencies, so uniqueness is based on both the value and the currency code. (£5 and $5 are different)

link Location

  • UK postcodes are auto-formatted when they are saved into Beacon, so uniqueness will be correct, even if you entered "sw112ae" and "SW11 2AE" when saving the data.
  • All other location values are treated as-is, which means "London" and "Greater London" will be treated as two separate values.
  • Uniqueness is case-insensitive, so "10 Downing Street" and "10 downing street" would be treated as the same

You can use the above filters to find duplicates on an ad-hoc basis, but we'd recommend creating a view (e.g. "Duplicates - Email") so you can find duplicates quickly when they come up:

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You can also add a section to your Dashboard so you'll be aware of duplicates as they crop up:

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