Languages

what does the future hold?

We have now started a new year, a new decade and a new phase in the maturity and growth of our industry. I see the following seven translation industry macro-changes in process:

1. An open translation platform: I see a lack of uniform standards on top of an open and shared platform as our industry’s primary productivity and innovation barrier. It is interesting to see a company like Google sharing the same thoughts. “A well-managed, closed system can deliver well designed products in the short-run – the iPod and iPhone being obvious examples – but eventually innovation in a closed system tends towards being incremental at best,” wrote Jonathan Rosenberg, a Google executive.

Our suggestion: GlobalSight and other open tools and translation systems including machine translation as the basis for this platform.

2. A new quality question: The traditional quality process is centered around asking a reviewer to compare the quality of the source and target language in a side-by-side comparison. This is a necessary step in the QA process, but is it the central question around which the quality process should be built?

Our suggestion: What would happen if we asked a reviewer what they thought about the target language version only – within the context of what sales and marketing is trying to accomplish with the target language? In this regard, we are connecting translation to the desired business outcome and not limiting our decision making to linguistic feedback only. Machine translation is more easily considered within this context

3. A new relationship with translators: I attended many meetings in 2009 where clients and vendors collaborated on the key challenges facing the client. These meetings were very beneficial, but in none of the meetings were any translators in attendance. If we are to truly optimize the translation supply chain to improve time, cost and quality- translators must be part of the solution.

Our suggestion: A translator portal for each client where all translators, regardless of vendor, can collaborate, train, share knowledge and share tools in order to increase productivity.

4. A new relationship with end-users of client services and products: The traditional translation process provides linguistic feedback and in-country subsidiary feedback, but rarely do we engage and receive feedback from actual users of the product or service.

Our suggestion: A big part of the potential in a crowdsourcing tool is to recruit, engage and reward end-users for their feedback about translated versions. The open source product CrowdSight can be such a tool.

5. Translation as a utility: The majority of information and applications are moving to the cloud with the supporting delivery model being on-demand. This has dramatically changed the translation and review process. Faster timelines and higher-quality are a requirement in this hyper-competitive, hyper-collaborative and ever-changing environment.

Our suggestion: An always-on translation utility built upon an open and collaborative platform using GlobalSight, the additional open source tools available and the TDA supercloud.

6. Business intelligence: as the saying goes, you can’t manage what you can’t monitor. Many large companies find it difficult to even calculate their total spend on translation, and I have yet to see a company be able to justify translation ROI in simple terms and metrics. We have failed as an industry in not being able to provide our clients with a way to quantify both value and translation ROI.

Our suggestion: Welocalize has developed a reporting tool called InSight. InSight is capable of pulling data from disparate systems in order to deliver the necessary key performance indicators which are vital to good decision making.

7. Machine translation: quite simply, we will not be able to keep up with the demand for translated material without machine translation. MT is not a magic wand; it is a productivity tool.

Our suggestion: Develop a program matching content types to desired business outcomes and invest in training translators to post edit. Translators will be more interested in machine translation if we can make it more productive/profitable for them.

Smith

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