What you need
The file messy-contacts.csv from the Downloads on this page: 14 rows with first name, last name, email, company and city. Every person and company in it is made up, and
.exampleaddresses cannot exist.The CSV Deduplicator. It runs in the browser; the file is not uploaded.
Tasks
Load the file. Select all columns and switch off every "Ignore" rule. How many duplicates does the tool find, and whose?
Switch on Ignore capitalisation and Ignore extra spaces. How many duplicates now? Which new one appears, and what was different about its two rows?
Select only the email column, with the same two rules. How many unique contacts are left? Which person is now caught, and why was the row missed before?
One person is still in the list twice. Find them and explain why the email column cannot catch it.
Select only the company column and switch on Ignore punctuation as well. How many companies are on the list? When would you want one row per company instead of one per person?
Answers for teachers
One duplicate: Ben Okafor, whose two rows are identical.
Two duplicates. Anna Berger is added: her second row has lower-case names and an address with capital letters and spaces around it.
11 unique contacts. Carla Mendes is caught: her rows share the email address but differ in "Fabrikam Inc." and "Munich"/"München", so matching on all columns missed them.
Farid Haddad appears with two different addresses, farid.haddad@ and f.haddad@. Matching on email cannot know they are one person; first and last name together can, but that would also merge two different people who share a name.
Seven companies. One row per company suits a list of organisations to contact once, for example when inviting businesses to a school career fair.
Why it matters
Under the GDPR (Article 5), personal data must be accurate and kept up to date. Duplicate records break that easily: a person who unsubscribes in one row may still be contacted through the other.