LanguageOps and ContentOps are terms that have been gaining increased attention recently. Is this just another fad or should you really care about these emerging concepts? As always, the answer isn’t black or white, even though the proponents and opponents often make it sound that way.
Modern organizations have gone agile, in software development, organizational development, and overall when they conduct more complex projects that do not lend themselves well to a “waterfall” based approach in ever changing environments. If you are operating in fast-moving, complex, international, and changing environments, you should care about LanguageOps and ContentOps.
So, what is this about? LanguageOps takes a more holistic view at how we create and use language data in organizations, the key misconception being that LanguageOps equals Translation. As a matter of fact, while they are indeed related, they are not the same at all. LanguageOps focuses on the premise that content (data) is omnipresent in an organization, and extensively that we have been using numeric data over the past decades. Language data can serve as a source of information, it can enable, and improve, new services such as AI applications and provide new and unprecedented (management) insights. Translation processes can be supported with LanguageOps, but they can also enhance data as input for training and enhancing data intense processes. They two can co-exist and enhance each other. A simple example is a taxonomy that is linked to segments, enhancing on the one hand the dataset for the training of machine translation systems that benefit from metadata, as well as a chatbot that is heavily reliant on context. Translation professionals often (wrongfully) feel threatened by LanguageOps because they fear that the ultimate goal may be the replacement of the human in the loop. Nothing could be further from the truth. Automation, as in any other industry, can make translators more productive and eliminate repetitive tasks, but the need for translators and language professionals to support the process has just increased by a magnitude as the number of use cases and the demand for data for a wide range of new applications increases.
ContentOps on the other hand, focuses on supporting global content operations across content types, tasks, languages, and organizations in an effective manner, and includes LanguageOps as a subcategory. As content volumes and formats have mushroomed in organizations over the past years, adding web, SEO, audiovisual, and more content formats to textual data, coordinating all efforts across an entire organization is challenging to say the least. Just consider the complexity of taking a product to market globally in an agile manner and having to provide all the supporting documentation, POS information, SEO, websites, creative promotional material, ads, audiovisual data, training, and more to support a global launch in a larger organization in an equally agile fashion. It is challenging, and a wide range of resources, suppliers, systems, and processes need to be coordinated and aligned in near real-time to deliver results on time, and within quality and budgetary expectations. ContentOps is focused on making these processes effective, agile, and transparent, while bridging technology silos. ContentOps is also concerned with enhancing human intelligence with AI, forging closer integration between machines and humans.
Therefore, should you care about LanguageOps and ContentOps? We strongly believe that the answer is yes, you should! Just about any larger organization struggles with the cost, effectiveness, and agility of its content processes. Effective ContentOps is what helps to address the key pain points. Suppliers, on the other hand, can benefit from these concepts if they are willing to expand their services portfolio, adding data operations to the portfolio. To that extent, ContentOps and LanguageOps present a major opportunity for the language industry as well as customers alike.