Let’s face it, the days in which our clients were responsible for linguistic quality are over. With 80 languages and simship realities, few businesses can afford the financial cost of maintaining an in-house linguistic quality assurance team. Time-to-market turnarounds also have made in-house LQA increasingly prohibitive.
Is that bad news for language services providers — a blame game shift for when things go wrong — or the good news on how we are finally doing things right?
In my recent webinar, Global Brands: Pushing the Boundaries of Localization, I argued that today’s time-to-market and ROI pressures mean that linguistic quality has to be owned by suppliers. Moreover, this has given rise to two significant changes:
- Our job as language services partners has switched from reactively catching errors (quality assurance) to proactively avoiding mistakes (quality control).
- Our clients have embraced supplier self-certification as valid for quality control.
Proactive Quality Control
Moravia has written before about the distinctions between quality assurance and quality control, so I hope you click on that link to read more on what we have to say in that regard. But, in summary, this switch from LQA to LQC is about, one, defining brand-, product-, and locale-specific standards and, two, ensuring that everyone has the tools (human or tech) to make LQC happen.
Proactive quality control means assigning the right translators and linguists for the locale and subject matter targets. It also means providing the right resources so that they get the terminology right: translation memories, terminology lists, style guides, and glossaries. (By the way, we recently wrote about translation memories — whether you should “hold them or trash them” before using them on your upcoming jobs. Don’t forget to consider that too!) Look to the example provided by Airbnb, which provides its translating teams with screenshots of how the translations will look on the page, in context, to ensure appropriate language and fit.
Other proactive measures such as community moderation for customer feedback, target language metrics, and benchmarking also are part of the new quality control story.
When I talk about self-certification, I am often asked how that is even possible. Do you mean that you translate and do the quality control yourself?
Yes! It is happening in car manufacturing, it is happening in textiles, and it is happening in many other industries where the supplier delivers final products to clients. So why should it surprise anyone that it is happening in the language services industry as well?
Don’t worry: that self-certification has become a trend doesn’t mean that we’ve entered some QA Wild West! There are rules for when and how self certification should apply as well as clear processes to ensure the firewall between production and quality control.
One of our clients, for example, has established thresholds for self-certification that depend on the content type. For UA — or help documentation — they may randomly check 10 percent of the content; for UI — user interfaces — it is 20 percent. For marketing content — and perhaps unsurprisingly when you consider how highly visible this content is, how costly it is, or how important it is to the sales process — these random checks can range from 50 percent for simple marketing content to 100 percent for legal and multimedia content.
The target language itself also may trigger higher random checks. Those of you who are localization industry veterans will not be surprised to learn that Japanese translations are typically reviewed at 100 percent of content because of Japan’s high quality expectations.
The linguistic quality shift — whether in the expectation of quality control or in self-certification — would be impossible without key ingredients of trust and transparency. This is no secret sauce though: as both clients and language providers recognize the high return of long-term investment in the client-vendor partnership, the trust and transparency pieces become evident.
But what do you think? Is our industry on the right path with linguistic quality under this new paradigm? Share your thoughts below!