tekom-Jahrestagung 2012 - ActiveDoc

Sprachtechnologie / Language Technology
LT 12
Presentation
Business Intelligence in cloudbased
Translation Environments
Stephan Böhmig, Wordbee, Luxembourg
Dave van den Akker, TVcN Tolk- en Vertaalcentrum Nederland, The Netherlands
Introduction
The success of a translation department highly depends on how good
teams are at optimizing communication, quality, cost and turnaround
times. Decisions that should lead to an optimal result often rely on the
experience and implicit knowledge of individuals rather than factual
historical data. This is not surprising, since the business focus on daily
projects and clients, combined with the use of various technical platforms
simultaneously makes it hard to gather, let alone combine and
analyze data needed.
In Wordbee’s cloud-based TMS/CAT environment collecting data becomes
specifically powerful, because all individuals involved work “online”
in the same environment and each individual action can be tracked
and timestamped. This gave TVcN the unique possibility to gather and
combine data from its clients, internal and external translators and project
managers. In the Wordbee business analytics tool, indicators are depicted
graphically and values can be viewed as they fluctuate, improve
or worsen, over time. We can now see what is really happening in the
translation process. How efficiently are we communicating? How correlated
are delivery times and quality really? What do our clients really
think of the work we do? Which teams work optimally together? How
successful are we at leveraging our TM? We answer these questions
based on actual data from the cloud and use findings as decision-support
to improve our performance. This helps us in optimizing processes
or at least to more objectively base our decisions.
This lecture intends to be hands on. It shows how an LSP or corporation
can make use of data to optimize business processes.
Business Analytics in the Cloud
Business analytics (BA) or intelligence aims at exploring past business
performance to gain insight into processes and use these insights to
drive business planning. In a translation environment, we might expect
a system to automatically create workflows, assign teams and deadlines
under a set of defined constraints and goals. A simple goal might be to
accept a maximum 5% likelihood of failure to deliver after the deadline.
Automation requires the system to gather a maximum of parameters
that potentially influence a business decision in question.
The “classic indicators”, volume per day, deadline and response times
etc. are not sufficient to predict likely client satisfaction (a qualitative
property) and cost efficiency given a specific translator/reviser couple, a
TM, a document and a deadline. Additional data needs to be aggregated
such as ratings, post-edits or revision cycles.
Gathering such data may proof challenging or impossible. This is where
cloud based TMS/CAT systems clearly have some advantages: All the
users from the client to the vendors and the inhouse staff login to a
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