Typical Localization Challenges and How Tech Can Help [Slideshare]

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From SQL info to digital multimedia, so much data is generated online each day that the translation industry barely makes a dent in it. We’re far from making enough content available in all languages. Although some of what isn’t translated today will never need to be translated, other untranslated content could be the missing link in a customer’s experience with a global brand.

That’s bad news for marketers, who are working to reach customers all over the world anytime, anywhere, with data-driven, hyper-personalized experiences. Nothing is more personal than an experience in the customer’s native language.

Thankfully, while big data presents an enormous challenge to localization professionals, it’s also the secret sauce that powers automated translation software and other linguistic tools that solve localization challenges, speeding up and optimizing otherwise tedious processes.

Whether automated language translation tools will take work away from translators is a concern in our field, but it could be a good thing. In this Slideshare, we’ve explored a few emerging technologies that, against popular belief, could make localization teams more valuable to their organizations.

Localization Challenges - Lionbridge Slideshare

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Typical Localization Challenges and How Tech Can Help – SlideShare Link

Learn how to communicate the benefits of faster, more efficient localization to stakeholders and make a case for revenue-driven tech in our whitepaper.



When it comes to localization, friction is the enemy

Creating content with a global audience in mind from the beginning vastly reduces costs. But many organizations become overwhelmed when it’s time to localize and struggle to get behind the concept. If done manually, the localization process can be long, repetitive, and arduous—filled with emails, phone calls, and file transfers. Manual practices are not only outdated but add costs that localization is designed to cut, with too much time dedicated to administrative tasks that take away from bottom line-driven responsibilities.

Technology has come a long way in removing this friction. Here’s a look at three of the most common challenges in localization and technologies that speed up the process, getting you to market faster.

Challenge #1: Inefficient business processes

Using email, phone, and FTP systems to manage, share, and track translation assets leads to inefficient processes—not to mention high administrative overhead and lack of visibility.

Challenge #2: Overwhelmed resources

Relying only on bilingual in-country specialists to review translated content places significant pressure on resources. Many of them also juggle a day job.

Challenge #3: Disparate content systems

Translating content across multiple, disparate systems can often be the most difficult part of the process—but it’s also the most critical.

Technology can take you to the next level

At each stage, CMS integration and automated workflows have the power to simplify inefficient, complex, and error-prone tasks.

But technology can also take you beyond the limitations of human translation, which is becoming more and more critical. The digital economy has created an explosion in the amount of content that might need to be translated: blogs, comments, forums, chat sessions, and social media posts to name a few. Providing hyper-targeted, personalized customer experiences in individuals’ own languages will require the use of advanced localization techniques.

Industry analysts estimate that human translators have the capacity to address only 0.00000000009% of the global content generated in a single day

Big data has increased the volume of content dramatically. At the same time, the following big-data-based innovations help close the growing gap between what’s generated and what’s actually translated.

Technology #1: Neural machine translation

As companies seek more efficient ways to deliver more content in more languages, NMT is evolving from a niche solution reserved for very large, global enterprises into a mainstream option.

NMT uses the power of deep learning and a higher volume of training data to build an artificial neural network. The network can find patterns, such as contextual clues around the source sentence, that help accelerate and improve translations.

Because big data yields so much information, NMT is able to identify complicated associations among these patterns that are beyond human ability to recognize.

Technology #2: Automated language QA

On the one hand, the growth of content exceeds human ability to review everything, and on the other, there’s no time to waste. Overcoming this challenge requires marrying human translation with technology that automates predictable language quality checks.

Automated language QA is a well-established, collaborative, and powerful quality control tool that uses pattern recognition and other approaches to identify potential problems—including broken or missing links, inconsistent terminology, and missing content.

An automated QA engine can detect more errors than human review alone. This helps reviewers focus less on mechanical issues and more on brand messaging.

Technology #3: Machine learning

ML algorithms are key not only for NMT, but also in every linguistic workflow step. Crucially, ML can map the right work at the right time to the best worker for the job.

Using linguistic big data, ML identifies which human resource has proven experience translating a particular type of content. But it can also identify the right linguistic resources and processes, focusing translators’ attention on areas with potential translatability issues.

In this way, everything is prepared from the beginning to assure the best possible quality.

Once you’ve established what tech you need, it’s time to get buy-in from your organization’s decision makers. Lay the groundwork for success by aligning these technologies with the people and processes best suited to deploy them.

How to make your case

It should come as no surprise that successful globalization requires commitment and coordination across your entire organization. But let’s face it—internal customers often put localization needs last.

This line of thinking means localization teams may get left out of strategic decisions.

Download our whitepaper, The Strategic Shift: Localization’s Fast Track to Driving Greater Business Value, to learn how to get stakeholders on board with linguistic technologies and get started on a revenue-driven strategy.

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