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Product uptake is suffering in Africa because of extremely slow pace of translation. There are several African languages that have not been documented yet, moreover there are many modern terms, a lot of which are related with technology that do not have equivalent terms in African languages. Some of these terms are mouse, malware and phishing among others. This slow pace of translation has been further aggravated by lack of agreement on language terms, as well as the lack of technical expertise.
Due to all this, the development of software in African languages has been seriously hampered. An example is the fact that Microsoft has still not released the Swahili translation of Vista when it is already testing its upcoming operating system Windows 7.
Another problem comes from the fact that even languages are changing fast, so just like the Windows Vista would probably become obsolete when it is released in Swahili, so will many terms that were used in it and have been replaced by new and often better substitutes.
Big brands like Google, Microsoft and Facebook among others have been trying hard to get their products translated into as many languages as possible, but they require investing more to get results.
Microsoft whose products are available in eight African languages has to face hurdles because there is a serious lack of localization experience for these languages. Because of which the company has to train people for the required languages, localization methods and tools. All this takes time resulting in delays like the aforementioned Swahili translation of Vista.
The other problem that these companies face is in deciding the standard form of languages and from whom to get translation services. In the former case, companies face a dilemma about whether to get translation from commercial translation companies or from local universities/communities. Commercial providers are more efficient and on the other hand working with local communities strengthens relationships and ensures local relevance of the brand. In the latter case let’s say a product needs to be translated into Swahili. If the translation is done in Kenya it might not be accepted in Tanzania where people speak a purer form of the language and might find a difference in terminology.
Google has tried to get around the problem by relying on machine translation. It has served them, but then machine translation is seriously limited compared to human translation.
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