Esther Labrie is language specialist and content manager at Quadient. Joining the company in 2010, Esther specialized in upcoming themes in online marketing like digital communications, omni-channel and Big Data. Esther creates content that focuses on building a bridge between online marketing and customer centric selling. She enjoys music and literature and likes to spend time with friends and family.
For the English-speaking world, diacritics are mostly these annoying marks that you cannot find on your keyboard. But for many others diacritics are an essential part of their language. In fact, incorrect use or omission of diacritics may have consequences for pronunciation as well as semantics. That’s where Quadient comes in. Because for those who know the importance of data quality, dealing with diacritics isn’t trivial, or just a matter of courtesy. It is essential for your business’ success. This blog lets you have a peek into one of the linguistic challenges that Quadient helps their global customers face.
Whether you are part of a corporation doing business on a global level or a bank or insurance company required to screen their customers and customer transactions against government-provided lists of suspected terrorists or money launderers: eventually you will encounter either non-Latin script or language-specific diacritics.
A diacritic – also diacritical mark or an accent– is a mark added to a letter in the Latin script, in order to change the sound-value of the letter to which they are added. In other words: it alters the pronunciation.
Examples are the accent grave used in French (Helène) or the caron or háček used
in Slavic languages (Dušan). When storing customer data with diacritics, oftentimes rewriting rules are applied (mostly due to the inability or impossibility to represent the particular diacritic). These rewriting rules are country-specific and usually infered from the pronunciation. For example, the German name Müller will be “normalized” to Muller in the Netherlands, whereas the habit in Germany itself is to add the letter e after the diacritic vowel: Mueller. This must be taken into consideration when comparing “normalized” data with data containing original diacritics. More information about the matching of global data can be found here.
The difference may seem futile, but the incorrect representation of diacritics poses many problems. In some cases the spelling of a name will make the difference between keeping a customer or losing a customer. Getting your global data right means:
- you are matching and merging the right records;
- you are targeting the right person with the right offer;
- customers are not going to feel like the organization doesn’t know them;
- you'll prevent customers from getting the feeling that the organization just doesn’t care…