Localization quality assurance is a crucial initiative to take when expanding into new markets, but many people execute it incorrectly. They may focus on the wrong aspect or spend too much time on a single piece of content, causing them to miss essential checks and waste money. Localization quality assurance needs to be a big picture strategy, not a single step in the translation process.
This means making data-driven decisions when it comes to establishing your quality baseline. Through this approach, you can improve all your content rather than merely targeting certain elements on a singular basis. Of course, taking this data-driven approach means that you need the right platform to support and guide you as you establish your standards.
What Is Localization Quality Assurance?
Quality assurance is an involved process that works to maintain quality and consistency across your content and products. Generally, objective-based quality assurance checks are much simpler than their subjective-based counterparts. There are a few objective-based aspects in localization quality assurance like spelling, grammar, and structure that have clear requirements and are easy to check for, but most of the content revolves around making subjective decisions that affect your brand’s identity. This can lead to variations and inconsistencies when distinguishing what’s “wrong” or “right,” given that subjectivity changes from person to person. People will have different definitions of localization quality assurance based on their specific roles in the process. Typically, these roles fall into three groups:
Since project managers lead the localization process, they’ll most likely review workflow aspects of quality assurance. Specifically, they’ll ask:
- Are protocols followed?
- Were the right translators assigned?
- Was the project completed on a timely basis?
The project manager’s approach to quality assurance will typically center on employee management rather than the content itself. As a result, they may omit steps, resulting in lower-quality content.
A linguist is going to focus on a specific piece of content. They will typically take a more objective approach to quality, asking questions like:
- Is the work technically sound?
- Is it structured in a cohesive, understandable way?
- Does it meet the standards set by the client?
In some cases, they’ll use an automated spelling, terminology, and punctuation program to locate issues with the content. However, not all content is created equal. Some content will need to leverage the translation memory and corporate lexicon in order to ensure consistency.
Clients usually receive a copious amount of completed work back at the same time, which can present challenges when conducting quality assurance checks. To save time, clients will typically take a sample of the content and look to answer a few key questions:
- Is the piece accurate based on the product or service?
- Is the content branded appropriately for the audience?
- Were corporate lexicons and standards applied?
Of course, by just checking a sampling of content, the client could miss critical issues in parts not reviewed. If multiple translators worked on their project, quality could vary significantly across all those translations, creating a significant risk within the client review process.
One key group that’s missing here is the end-user. When you’re thinking about quality, it’s important to consider if the reader will be happy with the end-product. Since the main goals of content are to engage the user and align them to your brand, your quality assurance process should be built around what the consumers want.
How Does Data-Driven Decision Making Benefit Quality Assurance?
Reviewing the localization quality as a whole is the fastest, most cost-effective way to manage the process. There are three critical components of a good localization quality assurance program—data, trends, and inference:
- Data: Granular-level data will tell the company what pieces are published in what languages, and provide details on views, user reports, and leads generated from specific pages.
- Trends: Aggregating and analyzing the data received at the first level shows a company which trends occur within its content ecosystem.
- Inference: Once the organization has used the trends to draw conclusions, changes can be made to improve the end-user experience.
A comprehensive localization management platform will make it much easier to collect and analyze all this data, but it must offer the right features. Ideally, it should provide an end-to-end experience that can connect to other programs. For example, you may pull a report comparing Italy to Korea and see that in Korea, QA specialists made changes 20% of the time. In comparison, Italian QA only made changes at a 10% rate. Based on this data, it would be wisest to study Italy a bit more closely. They could have standards in place that can be applied across all languages to help streamline processes.
To ensure changes aren’t made unnecessarily, you need built-in accountability. When employees have to justify their decision to update and change content, they’ll be less likely to shift content based on their personal preferences. Instead, they’ll give concrete evidence that supports the need for the change. If the change proves beneficial, it can then be implemented for subsequent work.
Localization quality assurance encompasses big-picture governance. When you understand the data and trends of your content, you’ll be empowered to make smarter decisions regarding your standards, have the ability to streamline changes, and build transparency into your processes. A data-driven policy aligns everyone to a single cause, resulting in a professional-grade translation that engages the end-user.