Whenever the matter is associated with localization challenges and technology, friction always prove to be the enemy in disguise. Creating content following the notion of global audience from beginning can reduce costs vastly. But, there are multiple organizations becoming overwhelmed during time to struggle and localize for getting behind concept. If performed manually, localization can be repetitive, long and arduous, filled with calls, emails and file transfer. Manual practices are not out dated but will add more cost to localization with dedication to administer some tasks from bottom line based responsibilities.
In terms of localization challenges, technology has come a long way to help remove friction. There are three major challenges in localization for speeding up the process and getting you to master a lot faster.
✓ Challenge 1: Ineffective Business Processes
With the help of phone, email and FTP systems for sharing, managing and tracking translation assets can lead to ineffective process. It will also add high administrative cost and visibility lack.
✓ Challenge 2: Dissimilar Content Systems
Before you head towards global content strategy, make sure to cover other challenges first. Translating content across disparate and multiple systems can be most difficult part of this procedure and also most critical.
✓ Challenge 3: Great Pressure On Resources
Relying on bilingual in-country specialists for reviewing translated content places will definitely add some pressure on resources. Most of them might be juggling day job.
How Technology Helps
Technology is designed to completely take you to next level. At every stage of the CMS integration and some automated workflows will have power to just simplify some complex, inefficient and error based leaks. You need to be aware of the machine translation fails as well and right from the beginning to avoid mistakes later.
However, technology can also take you outside the current limitations of human translation, which is more critical these days. The digitalized economy has created proper explosion in amount of content, which need to be well translated. Some examples are comments, forums, chats, blogs, session and social media posts. Offering some of the personalized and hyper targeted customer experiences in own languages will need use of advanced localization techniques.
According to industry analysts, human translators will have capacity to address around 0.00000000009% of the globalized content every day. Big data has further increased volume of content and at same time, the following big data based innovations will help close growing gap between what is generated and what is translated actually.
☞ Technology 1: QA In Automated Language
The content growth exceeds human ability for reviewing everything and there is no time for wasting. Overcoming such challenges will need to marry human translation with some technology for automating predictable language quality checks. Automated language form of QA is collaborative, established well and comes with powerful quality control using pattern recognition and approaches for identifying problems.
It comprises of missing or broken links, missing content and inconsistent terminology. Automated QA engine can further detect errors that human can review alone. It helps reviewers to focus prime on mechanical problems less and more towards brand messaging.
☞ Technology 2: Neural Machine Translation
Before you deal with translation for business process, there are some points for you to address first. As companies are asking for effective ways to deliver content in multiple languages, NMT is growing from niche solution reserved for some globalized enterprises into mainstream examples. It uses power of deep learning and some higher volume of training data for building artificial neutral network. It can find patterns like contextual clues around source sentence for accelerating and improving translations.
As big data helps in yielding much information, this service is able to identify some complicated forms among such patterns which are not for human ability to recognize.
☞ Technology 3: Learning Through Machine
ML based algorithms are major point not just for NMT but for linguistic workflow step. ML will map right work at proper time to best worker for job. Using this form of linguistic big data will help ML to identify human resources with proven experiences in translating particular content type. It can also identity right linguistic resources and process with focus on potential translating issues. So, all are prepared right from start or ensuring best quality possible.
After you have established the case that you are actually looking for and the tech you need, the time has come to buy in from decision makers or the organizations. You have to lay the groundwork for success by aligning all mentioned technologies with people and then process best suited for deploying them. You need help with language translation in every manner possible, just to get the work done with ease.
Successful form of globalization is always in need of coordination and commitment across entire organization. Internal customers might out localization needs at the end. It might mean that the localization teams might get left out when it comes to making some of the proven and positive strategic decision making. Just be sure to catch up with the best experts over here and things might turn to work out right in your favor and as you have asked for it. You will love the results involved in this section at the same time and right for your use now.
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