Optimizing Language Quality: Is Your TMS the Weak Link?

Today’s translation business depends on specialized tech, and no tool is more important than a translation management system (TMS). 

Your in-house team may have an internal TMS, or your translation provider may use its own system to manage projects for you. Some TMSes are off-the-shelf products widely used in the industry. Others are internally developed, custom systems. 

Unfortunately, this indispensable technology can often clash with your goals for improving localization quality. 

A typical TMS has many limitations that complicate, slow down, and interfere with language quality assurance. These constraints can prevent your quality assessment team (preferably an unbiased third-party service) from performing its best possible work. 

How can localization programs reconcile their TMS with strong language quality management? First, let’s examine why this challenge exists and how it affects your localization program. Then, we’ll explore how changes in both mindset and technology can lead to better outcomes.  

When Your TMS and Your Language Quality Needs Collide

 A TMS is an essential tool for translation managers and teams. It centrally stores all content, promotes consistency, facilitates collaboration, and streamlines the process from beginning to end. 

Such systems offer built-in tools for quality control, such as spelling, grammar, and terminology checks. Some may include additional features like limited quality scoring.   

Nonetheless, TMSes weren’t developed with specialized or in-depth language quality assessment in mind. Most of them assume that translation teams are using a simple, standardized workflow with only limited editing and review at the end. As a result, they can easily get in the way of making a more sophisticated evaluation and analysis. 

Most standard TMSes lack advanced features to measure, analyze, and optimize language quality, such as fine-grained error categorization, customizable severity levels, and comprehensive quality reporting. They also lack the flexibility to accommodate industry-standard quality measurement frameworks such as DQF-MQM.

In addition, most typical TMSes don’t provide a comprehensive, visual way to view language quality evolution or quality trends over time. They only provide the raw quality data, without a built-in interpretation of what the compiled data actually means.

Using specialized language quality tools, external language quality teams can perform far more robust and customizable assessments. However, TMSes often struggle to integrate and exchange the right data freely with quality assessment software. 

Importing and exporting data, migrating data, and maintaining consistency between platforms in real time tend to be time-consuming chores. Linguistic assets such as glossaries and style guides are also stored in the TMS, so users can only access and update them inside the system. 

How Mismatched Tech Creates Barriers to Language Quality

In short, a standard TMS can be an excellent tool for translators, but it isn’t specifically designed for language quality management. Language quality assurance is usually treated as an accessory or even an afterthought. Meanwhile, its closed environment can make it difficult for outside quality assessment teams to apply their tools and techniques or access the necessary information. 

So, what are the consequences? Here are a few. 

• Constrained and superficial quality analysis. A typical TMS’s limited, out-of-the-box features allow for little customization, granularity, visibility, or in-depth analysis of translated data. Quality assessment teams may struggle to close the feedback loop effectively and improve the quality of translations over time. 
•  Inefficient workflows. Project managers and engineers have to jump through many hoops to deal with tasks such as exporting data. Migrating data and harmonizing data between platforms require intricate, labor-intensive workarounds. Third-party quality assessment teams may struggle to access data or assets housed in the translation team’s TMS. 
• Inconsistent processes. Language quality assessments work best when teams use repeatable, standardized processes that they can refine over time. By contrast, inflexible technology forces language quality experts to adapt to every client’s toolset. Scattered data is also hard to gather in a single place for analysis and quality tracking. This results in slower ramp-ups, more complicated onboarding, and less effective assessments.  

Even if you’re using an experienced third-party team, these problems can impact your language quality outcomes. They drive up costs, reduce the ROI of your language quality program, and obstruct your goals for consistent, high-quality localization. 

What’s the Solution? More Openness and Collaboration

Many problems can be avoided if TMS developers aim at promoting collaboration across teams and platforms, rather than a closed and linear process. In general, your localization program will benefit if you build your tech stack with language quality assurance in mind. 

• When building an internal TMS, remember that no one tool can do everything perfectly. Keep your system flexible, nimble, and customizable. Design it to play nicely with other tools that could improve your language quality workflows. 
• Consult your language quality team members (whether in-house or third-party) to understand what features and level of flexibility they need to do their best work. When outsourcing to a language services provider (LSP), ask how it ensures compatibility between its TMS and your language quality needs. 

However, these technical considerations are only a reflection of a broader challenge. Localization as a field needs a shift in mindset—away from closed systems and toward more openness and collaboration. 

• The traditional localization model assumes a one-stop shop, housing every step in the process under one roof. Quality assurance is usually left to the client’s LSP. If an outside team provides quality assessments, that team is expected to take a tech-agnostic approach, doing whatever it takes to adapt to a client’s or LSP’s systems. 
•  An open, collaborative model assumes multiple teams and third-party experts will participate in localization. Third-party quality assessments should be the norm, not a last-minute exception. External teams are expected to bring their tools and optimized processes to the table. Technologies are evaluated on their ability to adapt to language quality needs, not vice versa.  

The current generation of TMSes has mostly been developed to fit the traditional localization model. By adopting a more open mindset, localization industry leaders can create the conditions for more effective technologies to emerge.  

Technology and Mindset Go Hand in Hand

At Beyont, we believe that language quality management depends on collaboration, not rivalry, between translators and quality assessment teams. The tools we use should make this partnership easier and empower everyone involved in the process.

The challenge goes beyond TMSs alone. If we embrace openness, the right tech and mindset can fuel each other in a virtuous cycle. Flexible tools will enable easier collaboration across specialties, and a more cooperative mindset will shape the kinds of tools we buy and develop. 

As a localization leader, you can’t drive industry-wide changes alone. But you can play your part—by building your processes with language quality assessment in mind, developing collaboration-friendly tools and systems, and seeking outside partners who are willing to do the same.  

Looking for seamless collaboration that drives higher language quality? Contact Beyont to discuss our approach to language quality management.