By Mihai Vlad, vice president of machine learning, SDL, part of RWS Holdings plc
We entered 2021 with renewed hope — vaccines on the horizon and a respite from the seemingly ever-long gloom of COVID-19 lockdowns and restrictions. In the absence of human-to-human contact, there’s no denying that technology has helped bring us all closer together. But our reliance on technology has forever changed the way companies will need to engage with us.
In our predictions for 2021, we set out three key themes that we foresaw would underpin future success. These can be applied to how businesses operate in a post-pandemic environment — to be agile in how they work and communicate with their customers and for companies to embrace intelligence by taking full advantage of the latest developments in artificial intelligence (AI) and machine learning.
Businesses that respond well to these changes will build better experiences for their employees, customers, and stakeholders. That in turn will build trust at a time when it more crucial than ever for customers to have confidence in the brands they interact with and for employees to trust each other.
Building trust, agility, and intelligence in your brand starts with a global understanding
Imagine you’re a customer experience leader within your organization. You need to understand your global audience — what they’re saying, what they’re thinking. What they like, and, of course, what they don’t like from the products you offer or the way you engage with them.
Perhaps you’re charged with regulatory compliance. You need to understand business risk based on employee communication and a mountain of internal policy documents across multiple languages and channels. Access to this intelligence is critical. But how do you work with this global data at scale within tight budget constraints? How can you trust that you have the full view if you are unable to incorporate the global perspective? It all starts with gaining intelligence from your data.
Make your data relevant
The processing and interpretation of information for any organization, whether public or private, is increasingly taking on higher and higher stakes. But it involves herculean volumes of data, often highly complex in nature, particularly in regulated areas of operation, and often in multiple languages.
Organizing this information and data is fairly easily accomplished by most data warehousing technologies. And business intelligence technology to mine the data for trends is now standard within organizations. But what if the data is in hundreds of languages, formats, and channels? Of course, the data warehouse can store the information, but can you make sense of it and understand that information?
Businesses and government organizations alike spend millions of investment dollars on enterprise software to manage data. Whether that is a global brand monitoring its customer sentiment across multiple territories or intelligence organizations monitoring critical security situations, curating data is one process and validating data is another crucial stage to make decisions due to the findings.
But managing data — and truly understanding data — are two very different things. Enter stage left the increasing reliance on neural machine translation (NMT) technology, which provides the ability to understand data — its meaning, intention, and purpose — providing greater agility to management teams to adapt to prevailing conditions more rapidly and more accurately.
In data, we trust — deploying more intelligent tech
The challenge lies in determining the veracity of the input data so that we can trust it — because making smart decisions means having smarter data to start with in the first place. Machine translation (MT) within the enterprise ecosystem allows us to do that. This technology has evolved at a critical pace to deliver not only greater efficiencies in accurate language translation but now provides the important intelligent layer which curates key insights from the content.
We call it smarter MT because it provides more precise intelligence for the user. Machine translation is pivoting from a solution exclusively for automatic translation to one that delivers both automatic translation and content insight. That means that a global brand manager will have greater insight to predict the effectiveness of the content they produce. In turn, the recipients of that content will benefit from the accuracy and nuances, no matter the language type or local context. This helps to avoid the pitfalls of embarrassing translations or cultural missteps.
Why organizations need a smarter machine translation solution
Decision-makers, strategists, analysts, and risk managers can benefit from the advance in smart-language AI in content analysis — working alongside existing enterprise software that can curate huge amounts of data in real-time from various sources identifying the quality of the data and the value it represents. This can be a complex operation when we consider the sheer increase in data across text, voice, and visual (broadcast) generated on a global basis.
For a brand manager following online sentiment across a global product portfolio or a cybersecurity risk manager monitoring domestic threats online across many languages, the volumes and veracity of data can seem overwhelming. During the current crisis, volumes have increased dramatically. The requirement for accuracy is even more imperative than ever before.
The latest innovation in translation technology from SDL (part of RWS Holdings plc), powered by SDL’s Linguistic AI™ and combined with market-leading neural machine translation, is designed to provide an enterprise-grade platform for real-time translation at high speed and volumes.
In addition, it produces content insights — an important functionality that provides an accurate summarizing of the “valued” data that this smart tech has identified — no matter the source language. Building this level of agility and intelligence is what companies need to foster that much-needed trust with customers. This combination also helps amplify the ROI of content in private and public organizations and makes machine translation a smarter choice for today’s global, digital content-intensive reality.