Localization Technology

SDL Tados 2021

Venga Global Launches AI Program to Meet Data, ML Needs

Localization Technology

Leveraging human-assisted multilingual data collection, Venga AI will power natural language processing AI and machine learning.

Global leader in translation and localization Venga Global announced this week the launch of Venga AI to meet growing data transformation and machine learning needs. Expanding data annotation, collection, and validation for artificial intelligence (AI) and machine learning (ML) services, the company looks to improve localization approaches, especially for languages other than English.

“We started offering data services in 2016 focused around natural language processing and data translation,” says Venga chief sales and marketing officer Antoine Rey. “We have learned, adapted, and developed technology with great success to bring quality clean data to top AI and data companies. We are excited to now publicly offer our expanded roster of services including data annotation and validation for text, image, video, and audio.”

Working in translation, localization, and creative services in over 150 languages, Venga partners with clients to “streamline global communication.” With expertise in natural language processing (NLP), the company builds custom programs for enterprise clients to provide human-assisted clean data collection, annotation, and validation for ML. These programs are supported by a production team, innovative tools and technology, a specialized supply chain, and an ISO-certified quality assurance team.

The announcement of the AI program notes the growing need for clean data to feed into machine learning algorithms, especially in sectors producing medical diagnostics, autonomous vehicles, and voice search. With roots already in the translation industry, Venga has pivoted to providing data services in recent years, leveraging local human networks to create accurate data sets.

Venga chief operating officer Chris Phillips credited the company’s “ability to ramp up from zero to thousands of trained resources in very short time periods” as “key to our success. We achieve this through stringent vetting, testing, and training of quality resources and optimize our technology stack project by project to create efficient and controlled NLP data collection.”

Venga Global earned recognition last year by the National LGBT Chamber of Commerce (NGLCC) Supplier Diversity Initiative, gaining certification as an LGBT Business Enterprise (LGBTBE).

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MultiLingual creates go-to news and resources for language industry professionals.


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An Interview With Christoffer Nilsson

Business News, Localization Technology, Multimedia Translation

By the looks of his LinkedIn profile, Christoffer Nilsson is nothing short of a true startup success story. Christoffer NilssonEven before graduating from Lund University, Sweden, he had co-founded Atod AB and Keyfactor AB, both game-related companies. Chris went on to become CEO of Warthog Sweden, managing director of Eidos Studios, and has managed the development of 20+ commercial video game projects. Since 2009, he has been managing director of LocalizeDirect, currently developing localization tools for the games industry.

We reached out to hear more about Gridly, a new CMS for digital games that is now running in beta and recently drew a $1.1 million investment from IKEA Family Foundation and other venture capitalists.

Gridly aims to become a competitive CMS for multilingual game projects. How do you foresee distinguishing Gridly from other systems?
The main differentiator is that we built a headless CMS tailor-made for the games industry. There are great tools to help developers with version control of simple files, like for your 3D meshes and textures. Gridly manages structured data, say an in-app purchase object that requires a combination of data types such as a name, a price, an image showing the item, and a description that needs translation into multiple languages. Gridly can then give business analysts access to change the price, and have translators and proofreaders edit the target languages, as well as keep track if any translation needs to be updated due to changes in the source string.

What is behind Gridly’s focus on game localization?
We chose to build localization into Gridly at the core, as localization is such a key element in the update cycle of games. It is also very hard to manage with a conventional file-based version control system. Gridly actually version controls every single string separately, making it easy to roll back to an earlier version. For more than ten years, we’ve been offering a localization management system to game developers called LocDirect. Many of the best game developers in the world are using LocDirect. So with Gridly, we took all the learnings and best practices from LocDirect and built into Gridly.

Besides the games industry specialty, are you trying to focus on a specific geographic area with this new CMS?
No, we have clients in more than 60 countries, so it is a global product.

Will Gridly offer anything innovative with regards to workflow?
Yes, we’re making it very easy for developers to customize Gridly and set up their workflows. We also offer strong support for multi-step localization, where you may start translating from Chinese to English, and then from English, go global. We also have support for managing audio in the localization flow.

How was the connection made with Entreprenörinvest? What is their interest in the language or gaming industry?
We went out to look for a partner who could provide “smart money” and be part of our journey onward. About 12 months ago, we started discussions with Jan Andersson, who is on the board of directors of both Entreprenörinvest and Innovum Invest. Jan had previously founded and exited a large software company in our region, so he had been on our radar for quite a while. They liked the combination of being part of the growing game sector with a de-risked entity. One could say that we’re selling the shovels to the game gold-diggers.


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Marjolein realized early on that the Netherlands was too small for her. After traveling to 30+ countries over the span of 10 years she moved to the United States in 2014. She holds a degree in Communication from the University of Rotterdam and has long had an affinity for creative writing.


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Localization lessons from a software startup

Localization Technology, Multimedia Translation

When I started working in a startup company as a technical writer, little did I know that in a few short years I would be managing software localizers working on three continents. What I learned in that role makes for an interesting tale and might be helpful to someone in a similar situation. If you are currently working in a startup, or hope to do so one day, please let me share a few things that might be useful to you. MultiLingual‘s issue on startups just went live, so you can check that out as well.

A bit about the company

The company was a survivor of the dot-com bust. Its flagship product was a web-based e-procurement product. There were two kinds of customers — individual corporations and e-commerce exchange sites. The large corporations would install the product on their own servers, and allow their suppliers to respond to request for proposals (RFPs) or participate in reverse auctions — auctions where suppliers tried to secure sales by lowering their prices in competition against other suppliers. The e-commerce exchanges would allow their own customers to do the same on systems that they (the exchanges) maintained.

In the early years, all the customers were based in the United States. The development team was aware that internationalizing the product would be a good idea, but this was certainly not a priority. In the first months of the company’s existence it, after all, was easy to understand why. The technology was new, not widely understood and the payoff from a significant investment was uncertain.

That all changed, of course, once one customer — an e-commerce exchange for mining and oil companies — was eager to pay for user interfaces in Portuguese and Spanish. Internationalization and translation were suddenly spoken of in almost reverent tones by everyone. Around this time, I became involved in the effort, probably because the online help was by far the largest chunk of text requiring translation.

Internationalization issues

While the initial internationalization proceeded smoothly, this simple coding requirement became the source of problems as the product evolved and the code base grew. Sometimes junior engineers or contract employees would forget to externalize strings in this or that module. Errors like these usually led to a reprimand and to reminders that all functionality would eventually be translated.

While these minor mishaps were perhaps excusable in less experienced coders working to meet tight deadlines, that was not the case for other offenses. The most grievous sin against internationalization, for example, was committed by a senior consultant. An audit revealed that he had left over 2,000 error messages embedded in the code. This caused significant delays in translating and testing the product.

No text in image files

One of the mistakes that we made early on was to use literally hundreds of PNG files in the product. At that time, the web was so new that the idea of graphics inserted between text or overlaying text seemed like a great idea. While this strategy made for some attractive web pages, it became a huge burden once we began translating the product.

Fortunately, after one or two rounds of translation, the illustrator found a way to automate the creation of the localized images. Later on, the development team worked to remove text entirely from images and enter it in resource files. While exceptions to this rule were sometimes made in response to a customer request, it was largely respected and helped reduced translation costs.

Customer-specific translations

Even when every string was properly externalized, translated and delivered, it sometimes happened that a customer would want to change previously approved translations for one reason or another. Originally, this was seen as a software problem. A bug report was filed by a field engineer and processed accordingly, eventually getting assigned to localization. Then the original translation was replaced with the requested one.

Since some terms employed in the localized UIs were neologisms, it is not surprising that customers would want to change them as the technology was adopted and the language describing it matured. For some customers, however, opening a ticket and waiting for a patch release was too time-consuming a process to change a text string. They wanted to change translations on their own whenever they saw the need, or in response to criticisms from their suppliers.

In response to this demand, and in response to the growing number of bug reports that were simply language issues, the development team created a tool that allowed authorized users to change localized text interactively. By clicking a text string on the served browser page, an authorized user could enter a new string and save it directly to the localized RESX file. While not every customer decided to use this tool, it did considerably reduce the number of language bugs that were submitted.

localization software exampleIn addition to providing this tool, we adopted a more customer focused approach to translation. This was especially important since in several of the target languages — particularly Slovenian, Turkish and Russian — there were at that time no standard, agreed-upon translation for certain e-procurement terms. In the case of Slovenian, for example, the translator discovered that he was first to introduce several terms into the language by working directly with the customer who had demanded this language.

Updating resource files

As noted above, many of the customers were large corporations and drove development of customer-specific functionality. This led to custom resource files in addition to standard resource files. Their demands often led to features that were incorporated into the main product.

In the early days, when there was just one customer that wanted the user interface translated into Spanish and Portuguese, updating the standard and custom resource files was a relatively easy task. Over time, as the number of supported languages grew, and as the number of customers grew maintaining and updating the resource files became a full-time task.

It was necessary to create a tool that would compare the English resource files — both base and custom ones — with the localized resource files in order to extract the strings requiring translation. Once those strings were translated, the tool would need to re-integrate them into the existing localized resource files.

We looked for an off-the-shelf software package that could do this, but at the time there were none that met our needs. Happily enough, an engineer volunteered to create a Java-based utility that performed all these tasks remarkably well. Since the number of resources eventually swelled to over 12,000, you can imagine that this utility was essential.

Figure 2 shows the translation update workflow. As shown below, the tool performed two separate comparisons in order to generate a delta or diff file containing the strings to be translated. It compared the current English resource file to the previously translated resource file to capture the resource strings that had been added. Then it compared the strings in the current English resource file to the strings in the previously translated file to see if any of the strings had changed. Then it compared the English files against each of the localized resource files in order to determine which strings needed to be added, deleted or modified for each language.

The diff files containing the new and modified strings were sent out for translation. The agency translators used translation memory from previous assignments to translate the strings. After delivery, the translation tool integrated the newly translated diffs into the existing translated resource files.

localization software

Lowering translation costs

While this tool worked well from a technical perspective, the turnaround for agency translations was often slow, or at least too slow for some of our customers. Each translation job required a quote, and each quote required approval. The lag between initial quote request and delivered translations could sometimes exceed eight business days, even for a relatively small translation job.

Delays such as this and the relatively high cost of agency translations did not escape the attention of upper management. One of the conditions of profit maximization is cost minimization and the CFOs of startup companies are acutely aware of this. The end result for localization was constant pressure to lower translation costs.

One of the more interesting attempts at doing this involved the creation of a localization group based in India. The startup, which had by then become profitable, bought a small Indian company that had useful industry data. The CEO of this company had on occasion hired locally based translators for short-term assignments. He suggested that we do the same.

I hesitated at first, knowing from experience how complicated it was to translate the user interface and online help. At the insistence of upper management, however, I slowly assembled a team of translators. This team consisted mostly of expatriates who were living in India for various reasons.

Since these recruits were largely new to professional translation, we organized training for them both on our software products and on the various tools that we used in-house. While few had had previous exposure to software development, most were able to learn enough to adapt to the demands of the job.

After the initial ramp up, we were able to lower translation costs and speed up translation turnaround. This was possible because the translation memories built up from earlier rounds of translation enabled the team to leverage existing translations when formulating new ones. This strategy worked especially well when the remote translators worked on software updates.

The takeaway

If you should find yourself responsible for localization in a startup company, there are four things that you are likely to encounter:

  • A lack of knowledge of internationalization
  • Coding lapses on the part of the development team
  • Customer concerns such as complaints about translated strings
  • A persistent demand to lower translation costs

To deal with these issues, it would be best to employ the following strategies:

  • Explain whatever needs explaining to whomever
  • Remind development managers to enforce internationalization coding standards
  • Remember that the customer is king
  • Defend the need for quality translation but be open to new approaches, whether technological or labor-related
  • Have fun — so many people would relish the chance to do what you do

Kevin Donovan has worked in localization for over 15 years. He has managed translation teams working on healthcare and business (B2B) software. He has also written articles for the Wall Street Journal and Computer Graphics World.

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Localization tech predictions for 2019

AI, Blockchain, Localization Technology, Machine Learning

Happy 2019!

Let’s look over the past year to get a sense of how much progress we have made, and what progress may lie before us. AI has become a norm in our lives and is now part of the vernacular more than ever. People have accepted that their lives interact multiple times a day with AI, and that such technology is becoming ubiquitous. 

What does 2019 hold for us? Well, if we pay attention to the predictions of 16th-century seer Nostradamus, we should brace ourselves for flooding, wars and a strike by a meteor. Nothing all that cheerful but, assuming we survive all of that, what does 2019 hold for technology trends?


We’ve seen an explosion in the use of AI in the delivery of neural machine translation during 2018; expect this to continue in 2019 and beyond. AI is the catch-all term to cover machine learning (ML) and deep learning. Machine learning is an over-arching term for the training of computers, using algorithms, to parse data, learn from it and make informed decisions based on accrued learning. Examples of ML in action is Netflix showing you what you might want to watch next. Or Amazon suggesting books you might want to buy.

Within the localization industry, the use of AI in the form of machine translation (MT) in several forms has significantly improved translation quality outputs, sped up translation of huge quantities of data and reduced the price of translation to make it economically viable.

AI refers to computer systems built to mimic human neural abilities and to perform tasks such as image recognition, parsing speech forms, discerning patterns from complex data sets and informing accurate decision-making. What’s more, AI can do these tasks faster, cheaper and more accurately than humans. Although AI has been around since the 1950s, it can be truly said that it has now come of age. This maturity has been propelled by the ever-increasing computational power now available in the cloud. 

According to Forbes, five out of six people use AI technology each day. These services include such things as navigation apps, streaming services (such as Netflix), smartphone personal assistants, dating apps and even smart home devices (such as remote-activated home security systems). Additionally, AI is used in recommendation engines used by eCommerce sites to schedule trains, to predict maintenance cycles and for other mission-critical business tasks. 

For the localization industry, AI will become a highly-integrated component of MT systems. The role of the human translator will continue evolving to that of an editor of MT texts, rather than translator of raw texts. In addition, pricing models will continue to move from the traditional price per word based on word volumes to pricing on a time-measured rate. MT will become an integral part of the standard workflow. The reality of real-time translation — driven by such technology as the internet of things (IOT) — will see project managers and editors managing workflows of projects required by customers who need a constant flow of updated information. MT will become part of the translation process just as much as other computer-aided translation tools did in the past. And, as ever, evolving technology will bring with it a desire for speedier and cost-effective solutions. 

Machine learning 

localization tech

ML will continue to grow as a tool used by most localization departments as the requirement for the speedy translations of large datasets continues to be a driver in the industry.  

ML is a subset of AI: with ML, computers are automated to learn to do something that they are not initially programmed to do. So, ML is an over-arching term for the training of computers to use smart algorithms to automate actions, to parse complex data and to learn patterns from this learning thus enabling the computer to make informed decisions based on this accrued knowledge. ML can be broadly broken down into two types of learning: supervised and non-supervised learning. 

For supervised machine learning, the training data is pre-labelled and consists of an aligned input data set and desired output data set. For example, an input data set could be a translation memory. An ML algorithm analyses the training data and maps how to convert future inputs to match the learned, desired output data sets. 

Unsupervised ML is like supervised ML; however, the input data sets are not pre-classified or labelled. The goal of unsupervised machine learning is to find hidden structures in the unlabelled data. 

So how does this impact the localization industry? Well, suppose you want to build a translation system to translate from Zulu to French, without any Zulu-French training data. The solution is, you can combine both supervised and unsupervised approaches to achieve this. You can use an English-Zulu data set in combination with an English-French data set, and using unsupervised machine learning, the system can learn how to translate from Zulu into French. 

This approach is commonly referred to as “zero-shot” machine learning — expect to hear more about this in 2019 for MT systems for long-tail languages. 


While blockchain is most widely known as the technology behind cryptocurrencies, it offers security that is useful in many other ways. 

In simple terms, blockchain can be described as data you can add to, but not take away from or change. These blocks of data can be “chained” together to create incredible secure data repositories. Not being able to change any previous blocks is what makes it so secure. 

This enhanced security is why blockchain is used for cryptocurrencies. It is also why it will play a significant role in localization where it will used to protect information such as a client’s financial details, and to protect and preserve translation memories; especially in translation memories used in distributed translation workflow scenarios. 

Edge computing 

Cloud computing has now become mainstream: most of all global companies now rely on this centralized hosting structure for machine learning and powerful computational power. This cloud market is dominated by just a few gigantic companies such as Amazon, Microsoft, Google and IBM. However, now that we’ve been using cloud computing for some time, companies have realized that accessing all data from a central repository introduces a time-delay latency, which in turn slows down the delivery of services which can, in turn, increase costs. The “round trip” made by cloud-based data is seen by many of today’s companies as a hindrance to their business growth. 

Technology stands still for no man, and so, for many, the cloud has reached its peak as a service for some technologies. The cloud will continue to be used to analyze and process huge swathes of data, but the advent of the IoT (connected security systems, electronic appliances, vending machines, automated lighting), where data processing needs to be high speed, if not real time, demands a different model. So the logical and necessary next move is to move this data processing to the edge. The edge simply means that data processing is moving from a far-away storage location to a geographical site closer to the data source. The advent of powerful computer chips that allows such processing to be done locally has expedited this move to the edge. Indeed, many of today’s cloud setups automatically look to place the processing of data at the optimum edge site for that data’s requirements. 

So, edge computing solves the latency problem by simply moving the data processing closer to home. Closer to home means less time spent uploading and downloading data. Instead of the centralized storage model, which has hitherto driven AI, companies are moving their data into the “local community” to be processed. This move will undoubtedly make data access much faster and facilitate the growing demand for real-time computing. 

How will this impact localization? Well, in 2019 we can expect to see the edge model used in domain-adapted MT systems, and distributed translation workflows that are designed to meet the increasing demand for data distribution in real-time. 


We are on the verge of an explosion in the use of AI. The inevitable growth of AI will fundamentally re-shape how companies manage translation workflows; the very engine of their work process. Real-time translations will often become the norm. 

I also predict that changes will happen at a human level; for example, the role of the translator will change from that of translator of raw text to that of editor of huge volumes of high-quality MT-produced text. This will be a beneficial change, allowing translators to increase their capacity and so increase their income. In 2019, we predict that the overall transformation effected by the advent of AI at all levels of the industry will bring with it an increased velocity of production, an improved efficiency in the delivery of translations, and a reduction in the cost of translating huge volumes of data. 

We hope you all have a very successful 2019! 

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Tony O’Dowd is the founder and chief architect of KantanMT.com, a cloud-based custom machine translation solutions provider. He is a serial entrepreneur and localization veteran, with almost 30 years’ experience working in the localization industry.

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Translation chatbots and the US election

Localization Technology, Multimedia Translation

The Dallas News reported that TV ads in Spanish are targeting Latino voters in this year’s tight midterm races. However, results are “mixed.”

“TV ads on their own are not enough to attract Latino voters. Instead, grass-roots engagement early will be more effective to reach those who do not typically vote in midterm elections,” Jenny Manrique’s article stated.

With political campaigns suggesting that texting young voters can be an effective method for getting the vote out, translation chatbots may actually play a role in this year’s elections. Not because the chatbots themselves are sending messages… people don’t mind real political texts in some circumstances, but they may dislike getting political spam from bots. Potentially, however, having a translation bot aid a real human interaction is a little different. And for the first time in any election, Facebook Messenger is now providing the opportunity for people to have Spanish-English messages automatically translated.

It’s anticipated that 80% of all businesses will use chatbots by 2020. They are now available on almost every platform, and are more intuitive than ever. Nonprofits use them as well, including to interact with voters in Spanish on voter ID laws.

Even though some of the biggest chatbots, like Siri and Alexa, are relatively new, this technology actually dates back to the mid 20th century.

In 1950, Alan Touring theorized that an intelligent machine would be indistinguishable from a human in a text-only conversation. In 1966, MIT Professor, Joseph Weizenbaum invented Eliza, the world’s first chatterbot, which imitated the language of a therapist using only 200 lines of code.

Chatbots have come a long way since then. However, still in its infancy is the translation bot. For a translation bot to be 100% accurate, it must identify innuendos, syntax, grammar and inflection. For this reason, Facebook announced its first translation bot only this year, and it has rolled out only one language pair: English-Spanish, which it’s offering on Messenger to US users.  

Translation bots are not quite there yet. But they are ever improving. This infographic explains where translation bots started and where they are today.

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Rilind Elezaj is an experienced digital marketing specialist in the marketing and advertising industry. He integrates web development and other digital marketing solutions to create hybrid strategies.

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Humor and AI: Does it travel?

Localization Technology, Personalization and Design

Conversational interfaces such as chatbots and voice assistants present many localization challenges — humor, for example. And that’s not even considering if the original content was all that funny to begin with.The secret to AI comedy must be in the data Click To Tweet

Humor: The final frontier

“Are there any Scottish people in the audience?”

Always a great start to a presentation at a conference. The response I received was, “You’re going to show that Scottish Elevator Voice UI video, right?”

I wasn’t.

Instead, I used the top jokes from the 2018 Edinburgh Festival Fringe as my opener to a workshop at ConverCon 18 on the subject of artificial intelligence (AI), personality, and conversational UI.

Of course, humor is an integral dimension of human personality and therefore part of that natural, conversational human-machine dialog. But humor has been called the final barrier for AI for good reason. There are many challenges.

I began my ConverCon workshop by telling the best joke from the Fringe.

“Working at the Jobcentre has to be a tense job — knowing that if you get fired, you still have to come in the next day.”

As soon as I recited the joke, I realized that it may not have been that funny to my global audience. Had they any idea what a Jobcentre is? It’s a British public service. In Ireland, the equivalent, an Intreo Centre, is offered by the Department of Work Affairs and Social Protection. In the United States, it might be called a WorkForce Center or One-Stop Center.

Conversational UI and the secret to comedy

Real US English examples of conversational interfaces, chatbots and AI can be tricky when it comes to humor.

Take this processing message from the Meekan scheduling robot on Slack. It makes a “witty” reference to hacking into TSA servers and No Fly Lists. I really winced at that one. I know what the TSA and No Fly Lists are, and I still didn’t get the joke.

Meekan scheduling robot on Slack (Image by Ultan O'Broin)

Meekan scheduling robot on Slack (Image by Ultan O’Broin)

This got me thinking about the challenges of humor and AI. If the secret to human comedy is timing, then the secret to AI comedy must be in the data, as well as the context.

Humor does have a place in conversational interaction, even in the most seemingly unlikely interactions, for example, Woebot. But humor needs to be done right.

Humor is not only the final frontier for AI, it’s a human personality trait that is easily lost in translation. Worse still, even in the original language, humor is not always that funny to everyone in a native audience. Of course, you don’t have to be Geert Hofstede to realize that humor doesn’t travel across cultures, but machines don’t get that. Yet.

So, as the localization industry rises to the challenge of dealing with AI, personality, humor, and the realization that no UI is the best UI of all, we can expect new talents will flourish to ensure that the conversational user experience resonates with the target audience. Do today’s translators need to have performing arts backgrounds or be comedians to enhance that local conversational interaction? I think storytelling skills are about to become hot property in every language.

Do today's translators need to have performing arts backgrounds or be comedians to enhance that local conversational interaction? Click To Tweet

Your punchline?

You may have other examples of humor and localization challenges from the world of technology. If so, share them in the comments!

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Ultan Ó Broin (@localization), is an independent UX consultant. With three decades of UX and L10n experience and outreach, he specializes in helping people ensure their global digital transformation makes sense culturally and also reflects how users behave locally.

Any views expressed are his own. Especially the ones you agree with.

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Haven’t an Iota About Fintech Localization? Try Cryptocurrencies

Localization, Localization Technology

Money, Money, Money Meets Its Waterloo

Apologies to ABBA fans about the cheesy introduction. But, mamma mia we need to talk about cryptocurrencies!

Lattés with your Litecoin? Crypto Café in Dublin, accepts cryptocurrencies and hard cash. (Image: Ultan Ó Broin)

Lattés with your Litecoin? Crypto Café in Dublin, Ireland accepts cryptocurrencies and hard cash. (Image: Ultan Ó Broin)

The Chips Are Down For Fintech

I enjoyed a must-read Medium article from Graham Rigby of Iota Localisation Services about the challenges of Fintech localization. Graham talks about how Fintech localization is different from ERP financial or vertical banking localization. He also tells us how a changing business environment means localization providers need to be agile, collaborative, and flexible:

“The way financial products are sold, communicated, and presented in the current market mean that linguists who have spent 20 years translating mortgage terms might not be best equipped to deal with the style and nuance of the text in a money transfer app.”

Indeed, the very notion of a “bank” itself has changed: Deutsche Bank in Berlin is now into Kaffee und Kuchen for the hip and happening people of the Hauptstadt.  ImaginBank from Spain is aimed at snombies, sorry, I mean the mobile generation.

And now, cryptocurrency localization is upon us, and that requires linguistic domain expertise too. Ironically, there is even a cryptocurrency called … Iota (designed for use with the Internet of Things [IOT]).

Oh No, It’s ONO!

I’ve changed career in the last few months, now offering digital transformation consultancy to established and startup ventures seeking to design the right digital thing the right way and to be ready to go global. I’ve been diving into the cryptocurrency space and grappling with the new ideas, concepts, and a new strange language that comes with it.

Cryptocurrency word cloud: Has language itself been disrupted by innovation? (Wordle by Ultan Ó Broin)

Cryptocurrency word cloud: Has language itself been disrupted by innovation? (Wordle by Ultan Ó Broin)

This is about much more than the Bitcoin and blockchain buzzwords du jour that people throw about without actually having an iota what these mean or indeed possible uses (blockchain, for example is behind the Chinese social media platform, ONO).

Mental “Block” About Cryptocurrencies?

If you want to explore this decentralised space further, there’s a blog series worth reading from Genson C. Glier on blockchain, Bitcoin, Ethereum, and cryptocurrency. I also recommend  this podcast from Tim Ferriss that covers all you were afraid to ask about, although some of terms and concepts will make your head spin (cheat list: jump to the “Show Notes” on the podcast). Try understanding these terms: Miner, Smart Contract, Daap, Truffle, Ganache, Hashcash, “Wet” Code, “Dry” Code, ICO, Metamask, and Gas.

Advertisement for eToro cyrptocurrency platform on Dublin public transport. Interest in cryptocurrencies has increased greatly in Ireland.

Advertisement for the eToro cryptocurrency platform on Dublin public transport. Interest in cryptocurrencies has increased greatly in Ireland. (Image: Ultan Ó Broin)

Although many people and institutions are rightly cautious about cryptocurrencies, they are a “thing” now and attitudes are shifting from suspicion to curiosity Providing localization of the conversation around cryptocurrencies and non-developer facing terms would be a great starting point to increase familiarity and adoption

Providing localization of the conversation around cryptocurrencies and non-developer facing terms would be a great starting point to increase familiarity and adoption Click To Tweet.

Read the small print. Consumer warning about cryptocurrencies lack regulation and protection on an eToro advertisement in Dublin, Ireland. (Image: Ultan Ó Broin)

Read the fine print. Consumer warning about cryptocurrency lack of regulation and protection on an eToro advertisement in Dublin, Ireland. (Image: Ultan Ó Broin)

Cryptocurrency Localization Needed

Generally, cryptocurrencies are for most adopters a form of value storage. However, cryptocurrencies are rapidly becoming a medium of value exchange, too (“digital money”). Bitcoin ATMs are appearing globally, for example. In Ireland, about 120,000 people in Ireland now own a cryptocurrency, a 300 per cent increase in the last four years. And yet, that basic usability heuristic of using plain language to communicate a concept even to experts to enable ease of use and adoption has already gone out the window.

The list of Bitcoin-friendly countries contains some surprises (Estonia is number one), and includes locations where English is very often not a mother tongue (although development tools and coding platforms are in English). We cannot be dismissive of the significant regulatory and security aspects of cryptocurrencies for now. But localization challenges are worth planning for now if cryptocurrencies are to move to the mainstream beyond those Silicon Valley types and their friends.

It’s likely, of course, that we will also see traditional finance, banking, Fintech, and cryptocurrencies all interact with more solidity in the future, adding to the need for more localization creativity.

Cryptocurrency Disruption Includes Language

At times, it’s hard to accept that the localization maxim English Is Just Another Language could apply in a cryptocurrency space that seems to have disrupted the notion of the English language itself. James Joyce might be proud of this kind of word invention, and of course it’s all a matter of context. But I remain gobsmacked by some of the terms I come across. It’s clear that lack of localization is a serious barrier to cryptocurrency adoption when even someone who has  worked in digital tech for three decades is struggling.

I need to learn that lingo though, as Dublin seems to be place it’s all happening for those cryptocurrency and blockchain ambitions.

Ah, the irony of that word, block, when it comes to getting your head around cryptocurrencies.

More About Cryptocurrencies?


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Ultan Ó Broin (@localization), is an independent UX consultant. With three decades of UX and L10n experience and outreach, he specializes in helping people ensure their global digital transformation makes sense culturally and also reflects how users behave locally.

Any views expressed are his own. Especially the ones you agree with.

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