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Ever heard of Big Language? It’s a Big Data-affiliated term coined by customer experience management company SDL, to describe the challenge faced by the text analytics software community whose global customers want more in the way of supported languages.

“Text analytics is all about language, and the game is changing for translation,” says Brian Otis, vice president, Business Development, SDL Language Technologies. “If you look at all the user-generated content on the web, it’s not feasible to rely on human translation, even for many big companies. Machine language translation is the only economic and time-to-market and sane way to go.”

The vendor this week offers up its machine translation solution, SDL BeGlobal, as an integration option for text analytics software providers, so that they can keep up with more demands from global customers that want to understand what’s being said about them by customers in whatever language it’s being said, without busting their R&D budgets on fulfilling expectations for expanded language support.

Rather, Otis says, those dollars and in-house development resources should stay focused on refining capabilities to “extract the gold nuggets from the breadcrumbs on the ’Net and social media and data feeds.” Integrating with the SDL solution so that it front-ends their own software, he says, is a way for text analytics vendors to grow the number of languages they can support for their same algorithms more cost-effectively.

SDL BeGlobal currently supports some 40-plus languages, with over 80 language pairs. It can turn other languages into the one that the text analytics product natively consumes. Only about one-third of text analytics companies support more than one language now, Otis says, and that hurts when they want to gain new clients or better support existing customers that want a solution they can deploy globally, across languages. “It’s a hard problem,” he says. And it’s a better solution, he says, “to translate before ingesting to the language you ingest well, vs. changing your system to ingest more languages poorly.”

SDL acquired the technology behind SDL BeGlobal when it purchased the University of Southern California-incubated Language Weaver and its statistical machine translation software a couple of years ago. Language Weaver had a foothold in the government sector, but SDL recently has been focusing on bringing the technology to more commercial enterprises. It will do that first through text analytics companies who, he says, “want to evolve, create and expand their customer experience strategies. And the translation aspect is a key part of managing customers’ experiences.”

Text analytics vendors can access the technology as a SaaS solution, including easy to use APIs for integration, according to Otis. SDL also is building a developer portal for self-onboarding. “We are almost at a tipping point where supporting multiple languages has gone from a competitive differentiator to a mandate,” says Otis. “Our goal is to provide the highest-quality, fastest, easiest to access and best feature-function capabilities around translation engine – and be the ubiquitous translation engine on the planet.”