The Translation Automation User Society (TAUS), now in its 14th year, is a forum for localization thought leaders to meet and discuss trends, news, technical challenges and opportunities in the industry. This year, Katka Gasova (our Linguistic Services Director) and I (VP of Partnership and Custom Solutions) attended the TAUS Annual Conference in[…]
In the first post in our series on demystifying localization trends, we talked about the use—and abuse—of the term ‘real time’, and what real-time localization really looks like. Real-time localization is, in the end, not easy to pin down. (We explain it with concrete examples.)
Like any digital, data-driven business process, localization has its fair share of jargon. We hear about ‘agile’ this and ‘hyper-localized’ that. But one of the most common examples you’ll hear in our industry is ‘real-time localization’ or ‘localization in real time’. You’ve probably used the phrase yourself, but would you be confident explaining[…]
In our “always on” content and development worlds, where the term “digital marketing” is essentially redundant, content shelf life is a fraction of what it only recently was, and “sim-ship” concepts have essentially been replaced by continuous development. To keep up in all your markets, your localization needs to be just as agile as your[…]
It’s a fact: the shift of software and content development from lengthy waterfall release cycles to continuous release models has been accelerating, and it’s becoming increasingly vital that enterprises quickly publish updates to all supported languages.
What's in store for the language industry? Can we really predict what lies ahead?
How does one forecast the future? Is it possible to plumb the depths of our language industry to predict what lies on the business seas ahead? What is clouding our crystal balls?
Machine learning is a hot topic, much discussed in conference rooms and intellectual forums by futurists, inventors, and disrupters — in other words, TAUS attendees.
Translation memory (TM) is an established language technology known to save costs and time, and it usually improves quality, but not all organizations are able to leverage translation memory to the fullest. Why does this happen?
In my last post, Localization Metrics 101: A Crash Course in the Basics, I proposed that tracking basic indicators like on-time deliveries, average language quality scores, and throughput were vital to establishing a solid localization program. Once you have gotten these basics down — especially narrowing your focus to the metrics that are[…]