Measuring Information Quality: What’s Missing?

A lot written about information quality recently. But how do we measure it?

There is a perception out there that information quality generally is very poor. But how do we know? All that digital content out there, but most of it is never read, and only a fraction of that is ever translated (and translators are often the only readers of that content). Just because a user guide isn’t read doesn’t mean the information itself within is poor quality. Perhaps, the accompanying software’s usability is spectacularly good and so it isn’t needed? Who knows?

In the GILT industry, information quality is all too often only assessed in terms of the cost, time, and effort to produce and then translate content (and usually one function is measured in isolation of the other). We have all kinds of metrics about “spend” published, time-to-market statistics analyzed, the opinions of professional linguists and terminologists debated, complicated mathematical formulae promulgated (trust me, if you reach that level you’ve clearly no real work to do), QA checklists written, certifications from standards bodies waved under our noses, and all the rest, in an attempt to define information quality. All good, though how efficient or applicable to the real world some of these things are is debatable.

However, often there’s a decider of information quality that is missing from these methodologies: the user of the information.

We need to move the key determinant of information quality to the user community: engaging users, and research, analyzing and understanding how users search for, retrieve, and use information to solve problems. For example, what search keywords and phrases do they use? Which pages do they read the most? Which parts of those pages are read and how? And so on.

The tools and opportunities for this are everywhere. Ever studied web server logs? Done any eye tracking studies (see image below) before and after an information quality project? Conducted comprehension studies on the outputs? Observed how real users consume information? Found out what terminology they actually use when helping each other, on support forums, and when they customize and extend software?


So, let’s look at costs and translatability issues, post-editing metrics, number of flagged errors in QA, and so on, sure. But let’s connect it to the user experience too, regardless of language, and give the user the final say. Make users the arbiters of information quality.

Otherwise, we’re really just talking to ourselves.

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Natural User Interface Gestures. Are They Global?

I was reading an interesting BBC article about how language changes are being driven by the internet. Examples are cited from English, Ukrainian, and more. In its own right, this is an interesting subject, reminding us that such changes are global.

How such changes can be reflected in our information quality efforts, and how applicable they are to everyday ‘plain’ use as opposed to specialized, domain-specific needs is a subject I’ll return to.

My eye was drawn to the following in the article:

The dreaded force-quit process of pressing ‘Control, Alt, Delete’ is known as Дуля (dulya).

“A dulya is an old-fashioned Ukrainian gesture using two fingers and a thumb – something similar to giving a finger in Anglo-Saxon cultures,” she said.

“And you need three fingers to press the buttons. So it’s like telling somebody (a computer in this case) to get lost.”

This made me consider a section in the 150-page Apple iPad User Guide (PDF), listing out the natural user interface, finger-based, gestures for that device. For example:

  • Two-finger flick up: Read all, from the top of the screen.
  • Three-finger flick up or down: Scroll one page at a time

I wonder if some of these might be ‘tricky’, internationally? Certainly the use of hand and finger gestures (or, indeed, of other body parts) has long been regarded as a potential international no-no when designing graphics. I’d love to hear your opinion.

For some, it would seem that some of these gestures are even positively disgusting!

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¿Socially-Enabled What?

The mystery of Galvão birds. Twitter use in Japan and Brazil. Different trending topics in the US depending on whether you are black or white. The “wisdom of the flock.” How the new media doesn’t help us. “Imaginary cosmopolitanism.” How “we think we’re getting a wide view of the globe.” Except we’re not. We’re in social media “filter bubbles.” We don’t see the wider picture.

There are lots of people who don’t get the notion of true global connectivity, replacing the essence of global relationships between real people with a ’Globish‘ Anglo-American-centric debate about digital infrastructure instead. But, this guy coming up does.

If you want to move on past that tired social media guru “we’re all connected now through Facebook and Twitter” stuff (actually the second easiest job in the industry,) then he’s for you.

So, I urge you to view the very refreshing and insightful TED talk called The Need for Cultural Translation with Community Media by Ethan Zuckerman of Global Voices. PBS tells us his talk is about exploding “the big myth that the web is bringing us closer to other cultures or countries – when we’re on the web, we’re basically in our own small islands of our social networks.”

Ethan says we need “DJs … skilled human curators” who “speak the language of the West and of other cultures at the same time.” We need more “xenophiles,” those “bridge figures” who can and do cross cultural divides, rewiring information systems across language and culture so we get that wider picture.

The talk has subtitles in English and Portuguese different languages.

We need more Ethans too.

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Far Too Easy: Knocking Machine Translation

The easiest job in the translation industry has to be the one disparaging machine translation (MT). As a former boss of mine would say when he saw such e-mails: “Those guys are not busy.”

A day doesn’t pass without some link being circulated my way, either on Twitter or e-mail, highlighting poor applications of machine translation. Some dismally poor translated output is shown (almost always the result of the use of a free online translation service) along with some subjective declaration about how using Google Translate, Bing Translator, and others, is dangerous to your brand’s reputation, will drive away your customers, and so on (translation: “I haven’t a clue about the potential or proper application of MT really, but I think my job is under threat or you must pay me to do it instead”).

Of course feeding text randomly into a free online MT engine will result in a less than perfect translation, at times by a very long shot indeed. Except, you can get equally dreadful results by giving poor quality information, with no context or glossary, to the wrong type of human translator too (and still pay for the privilege).

Such tiresome “MT be bad” examples have brought nothing new to the translation debate in two decades. And then, of course, there are times when these free online services do a very good job indeed. But those examples are rarely declared. Natch.

For those who do understand the potential and application of MT, they must counter all this stuff by the mass circulation of correct examples of MT output. Match every lazy, bad example with a better, applicable one. The role of information quality, of customization, and post-editing needs to be explained more. And, critically, the role of MT in alleviating information poverty too must be brought to the fore.

How many of the MT knockers out there are permanently offering translation services for free for high value, life-saving information in Asia or Africa?

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Information Quality, MT and UX

I’ve been working on an acrolinx IQ deployment for my employer Oracle (yes, I do have a real job). For many who go down this route, the claim that such initiatives are only being done because it will mean the instant arrival of machine translation (MT) will seem familiar.

The ‘translation process’ imperative is the wrong way to drive such initiatives. Instead, what is critical here is understand the notion of managing information quality for the user, regardless of the translation process. Because the language used in content, or information if you like, is a user experience (UX) issue.

It’s clear that a quality input (or source) gives better returns for every kind of translation. In the case of any language technology, clean source data delivers the best returns; and is becoming even more important as more and more turn to statistical machine translation.

Furthermore, the points made by Mike Dillinger at the recent Aquatic/Bay Area Machine Translation User Group meeting need re-emphasizing: MT does not require special writing; people require special writing. The rules for writing English source text for human translators, machine translators, and users in general, are the same.

Developing quality information in the first place, and then managing it, is the way to go.

acrolinxIQ flagging errors

So, forget about “controlled authoring” (manual, automated, or whatever other method of implementing it) and indeed “writing for translation” classes as the “mandatory prerequisite” for improved translatability or machine translation. Think and practise information quality as an end user deliverable in itself that has significant translation automation (and other) externalities.

I’d love to hear other perspectives on this, too.

If you’re interested in this notion of the primacy of information quality per se in the translation space, then read Kirti Vashee’s The Importance of Information Quality & Standards.

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