Localization

Focus
convert raw data into meaningful
and useful information for business
analysis purposes. As long as there
is no transparency or agreement in
the measurements, results may be
subpar and lead to frustration and
misunderstandings.
In order for data to be useful, it
needs to be annotated using commonly
acknowledged metrics. I have
intentionally avoided the term standards,
because metrics don’t have to
reach that status to be useful. A wide
agreement about them is enough. One
example of such metrics is the harmonized
DQF-MQM error-typology
intended for error review. The typology
has been developed in the European
Union subsidy project QT21 and is
being implemented in the TAUS Quality
Dashboard along with other metrics.
The goal is to seamlessly integrate quality
and productivity measurement in a
number of computer-assisted translation
tools and enable reviewers to go a
step further. Rather than just correcting
or counting errors, assign error labels
from a two-level hierarchy and specify
severities on a preselected sample.
Startups are in a prime position to
try and test new metrics and apply
the best fit for their purpose, driving
industry adoption. Of course,
error typology might not be the
best solution for their content, just
as we have seen in the examples for
nonconventional content. But there
are so many evaluation methods out
there, including usability, readability,
adequacy, fluency, rank comparison,
productivity — the sky's the limit.
Localization is a data-rich industry.
How can we leverage this rich
seam of data to help make better
decisions in our localization projects?
First of all, data should offer real
solutions focusing on how to target
customers, provide real benefits and
drive a better service.
Secondly, there is plenty of data
available in our industry, but not all
data is good or relevant to everyone.
Before sharing business intelligence
data, it is also important to verify what
the impact will be when sharing this
information: negative data doesn’t
always bring us further. Positive data
can be a good thing if it is shared
because it can have a virtuous influence
on the enterprise.
A new market segment
TAUS predicts a growing market
for translation and consulting services
targeting startups. These ventures
are desperate for advice when jumping
on the localization bandwagon.
This is simply because their growth
potential is huge, but they are starting
from scratch and they need a
knowledgeable companion to avoid
mistakes down the road. There are
many technologies to choose from,
many approaches to localization and
many tools to measure return on
investment. One of the key elements
for growth is localization readiness
at an early stage. Appealing content
should be created fast with a multilingual
purpose in mind. Ideally, the
localization should be outsourced
to a vendor or multiple vendors that
provide scalable solutions and different
levels of quality.
Startups need a partner that can
help in getting the word out there
and support them in their early
endeavors. I’m sure you can have it
all in one: a consultancy with access
to business intelligence tools, that
can set up agile localization, speak
the lingo of vendors and is familiar
with the technology out there. With
a mighty companion like that, achieving
efficiency for startups should be a
piece of cake. [M]
A thousand different workflows. One Solution.
The Business Management Solution for the Translation Industry
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April/May 2016 49