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Data cleansing

Cleanse, normalise, deduplicate and merge. For Swiss company addresses, we can also match against official Swiss sources.

Cleansing

Incorrect and empty entries are deleted or flagged — whichever you prefer.

We agree with you up front what counts as an error. A process tailored to your dataset rather than an off-the-shelf rule.

Normalisation

Different spellings become uniform — for text and numeric attributes alike. It’s the first step towards a dataset on which analytics, databases and customer records actually work. You decide how fine-grained it gets.

Street — rawNo.Street — normalisedNo.
Musterstr. 7—Musterstrasse7
Musterstrase7Musterstrasse7
muster-strasse 77Musterstrasse7

Deduplication

Duplicate entries disappear. We agree beforehand how strict the detection should be — too strict and real customers get deleted, too loose and dead records remain.

Matching

Matching, also called record linkage, merges data from different systems or files. You get one unified view of your data, with consistent keys identifying the same records if you wish.

For Swiss company addresses, we match your data against official Swiss sources. You receive current, reliable company data.

How we work

It starts with a thorough conversation. We go through the dataset with you and discuss every step before we begin.

Contact

Let’s talk about your data

Book a conversation →
Address

Im Hasensprung 19a
8478 Thalheim an der Thur

Phone

+41 43 548 72 08

Email

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