David Benotmane is consultant for translation technology, working in Zurich,Switzerland.
He worked as manager of the translation unit for the Migros Group, the largest company in Switzerland. He has conceived a fully automated translation workflow platform with innovative functionalities in the SDL WorldServer. He reduced the costs of translations by up to 50% and at the same time increased the income for the external translators by up to 260%. Currently 80 translators work daily with the TMS.
He is now representing Glossa Group in Switzerland and is introducing the proofreading platform myproof® for Swiss companies and translators. He is currently developing an HRM-Platform and an eLearning-Platform for translators.
A significant check system for obtaining an objective assessment of translation quality
Globalization, the increasing significance of information and communication technologies, the fast turnaround of texts, budget cuts and outsourcing, but also new markets, new technological distribution and standardization possibilities as well as an exponentially growing demand worldwide for translations are just a few of the forces that designate these areas of conflict and between which the safeguarding of the translation quality has to be re-balanced.
Nevertheless, a trend reversal is becoming apparent – even among decision-makers with little linguistic training – triggered by, for example, delayed product launches or translation-related liability problems, which now affect every third company. A re-orientation is currently taking place beyond familiar process optimization measures, in which technology and language are given greater significance again as complementary, mutually supporting systems.
The myproof® system is an automated, objective and confidential procedure to register, check and evaluate multilingual texts, that delivers an objective statement on the quality of a translation. myproof® applies quality check to both source and target texts. The scoring method accordingly allows proofreaders to assess target texts to obtain penalties for issues in the target text, similar to the LISA QA Model and SAE-J2450 QA Model.