Our Analysts' Insights

New technology developments are stirring up the language industry, but the buzz mostly centers around neural machine translation (NMT) and artificial intelligence (AI). Something else is afoot that most observers are not discussing: the shift from monolithic to distributed systems. Microservices addressed via REST APIs promise to increase agility by allowing swappable engines. During a recent CSA Research Colloquium at LocWorld, we put this new solution architecture in the context of enterprise ...

Finding, qualifying, and testing translators and interpreters represents a sizable investment for most language service providers (LSPs). That challenge is even greater for the fast-growing ones, any venturing into new markets, and those starting new service lines. For most LSPs, that means adding or enhancing the vendor management function to locate, vet, and retain linguists and other specialists. Providers typically introduce vendor manager positions by Stage 2 of the LSP Metrix, CSA Research...

CSA Research's 13th annual market study sizes the language services industry and calculates a variety of business measures. One is the growth rate for the industry overall, another is the performance of hundreds of individual suppliers that provide detailed revenue data to our survey (see figure). In this year's analysis, we estimated that the market will turn over US$43.08 billion in 2017, a rise of 6.97% over the last year. You can download a description of our quantitative research an...

Blockchain is a method for concatenating blocks of transactions, which are stored securely using encryption in a distributed system that prevents bad actors from unlawfully changing records. Most media reports focus on blockchain’s use in cryptocurrencies such as Bitcoin, where it originated. However, the language industry will be more directly affected by the advent of smart contracts, where blockchain methods allow applications to automatically negotiate and enforce transactions; for exampl...

For many years now, localization managers at enterprises have known how they should create content tailored for particular markets, even if they haven't actually done it. Instead, the model that evolved in the 1990s invested in optimizing existing content for translation.

Based on press coverage of recent advances in machine translation (MT), it may seem as if all the major developments happen in the United States. However, since the 1980s, much of the basic research in this area has either happened in European projects or been headed by alumni of groups in the region such as the German Research Center for Artificial Intelligence (DFKI), University of Aachen, and the University of Edinburgh.

The Indian e-commerce market continues to grow as the government there encourages its expansion and works to ensure that the country’s local languages are integrated into its Digital India and Make in India programs. Unlike its neighbor to the east – China – the Indian playing field is open to international companies, as the ongoing competition between Amazon and Flipkart and Uber and Ola Cabs demonstrates. Apple, Amazon, and Google have announced billions of dollars in additional investment ...

Big changes are afoot in cyberspace. In recent years, populations in Africa, Asia, and Oceania surged online with the aid of cheap data plans and government investments. In Myanmar, for example, the mobile grid went up in 2014 and a total population of 53 million now shows 33 million subscriptions, with 80% smartphone penetration and over 50% with active data plans. China initiatives, such as Belt and Road, Shanghai Cooperation Organization, and the Asia Infrastructure Investment Bank, are bring...

It was the memo heard around the world, the one that led Google’s CEO to cut short his vacation to deal with a firestorm of criticism, and the one that reignited the perennial and fiery debate about sexism, women’s role in the tech industry, and political correctness.

Buyers of language services crave the ability to measure translation quality in an objective way, get easy-to-digest reports on how it is tracking over time, and be able to drill down as needed for process improvement. However, quality control remains a mostly human-driven process – even when supported by QA technology – because humans have to sift through the reports these tools produce. What if there was another way to approach quality?