Information grade — Potentially useful concept

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In my newish job working with structured information with the Scottish Government, I am deepening my understanding of concepts around information architecture, and how to apply ideas from the discipline to our context.

I have worked on information architecture projects for 15 years, with some great successes. But never has my role specialised solely in information architecture.

Moreover, the Scottish Government’s context is so complex, and scope so vast, that I knew coming into this role that I would have to do some hard thinking to do this work justice.

I am also the only person in the organisation leading on information architecture, at least for public-facing information delivered over the web.

As such, part of my role is to build the organisation’s practice in information architecture, and teach my colleagues about it.

It has also involved defining terms that I have an intuitive feel for, but need careful definition for the sake of alignment and clarity across colleagues, not all of whom are content experts. Things like: What is the difference between a content type and content format?

This is all very useful for me given my own need to learn more to meet the challenge. There is no better way to learn about a topic than to have to teach someone else. I am studying widely, synthesising those lessons, and relaying them to my colleagues. This is bringing information architecture to life for me like never before.

Defining information

I have quickly noticed is that information architecture itself is poorly defined. Some basic concepts have variable definitions, even across the handful of canonical resources that have emerged over the past few decades since the emergence of the web made information architecture such a prominent problem.

For example, the latest edition of Information Architecture for the Web and Beyond — “the polar bear book”, which is essentially the book on digital information architecture — offers up four separate definitions of information architecture. Moreover, none of them seem exactly right to me — which I think is the point the book is trying to make.

The definition of information itself is surprisingly hard to pin down. There is a dazzling array of varying definitions, applying different lenses to the concept for different purposes.

One big question is: What is the difference between information, data, content and knowledge?

Many of us will carry intuitive definitions of these terms in our heads. It is common to hear people say things like: “Data is raw facts”. This seems sensible enough at first glance.

Content is a bit harder. One view might be: “Content has a specific purpose for a specific audience.” But what might make something content but not data? And how does it become knowledge? And where does information fit into all this?

Across my wide reading, I came to see how a specific understanding of these concepts may be useful in the context of our project. I am sharing this idea in case someone either finds it useful, or can offer critique.

Information is on a spectrum

While there are many differing definitions of the word information, I found it useful to consider its origins and etymology. Early uses of the word described teaching. It derived from the Latin verb informare, which literally meant “give shape to” or “give form to”.

Information is literally in formation. It is something that someone has taken and shaped in order to teach others.

I think of this act of shaping information as processing it. Information is subject to varying degrees of processing. It exists somewhere on a spectrum.

At one end of the spectrum, information is more data-like — less heavily processed.

At the other end, it is more content-like — very heavily processed.

The process of processing

Note that data is not entirely unprocessed. There is no such thing as raw data. By the time data exists, someone has already decided what to collect, what not to collect, how to collect it, how to record it, how to store it, and how to represent it. As such, it has already been subject to a large amount of messy, human decisions — processing.

But often there is cause to carry out further processing to make that data more useful. For example, significant or interesting pieces of data can be plucked out of the dataset, and presented in a different way to give it a certain context. Even taking one data point from dataset and presenting it differently puts a spotlight on it.

A further degree of processing might involve writing a short paragraph describing the data, what it means, and why it is important.

From there, it might be subject to further processing. Someone might write an article that takes the interesting data, and compares it to another piece of information — providing yet more context. Another example would be to produce an infographic to present it in a visual way.

Eventually, this information might be used as just one part of a large amount of research that would go into producing increasingly rich and informative pieces of content. This way of producing information is what we now call content design.

By this stage, many people would recognise this as content rather than information. It is certainly no longer data.

Someone in my team used the word “information grades” to describe different these differing levels of processing.

From information to knowledge creation

Where does knowledge fit in?

As with the related terms I’ve described here, interpretations of the meaning of knowledge vary wildly depending on the context and purpose.

To me it makes sense to describe knowledge as what happens when a piece of information connects to a person’s existing understanding of something. The connection between new information and existing understanding creates some new insight within a person. That is the creation of knowledge.

We can think about what our users bring to the table. It’s not always necessary or desirable to simplify things as if our users are not experts.

An example from usability would be the design of an air traffic control system for trained air traffic controllers, or a medical device for use by trained medical professionals. You wouldn’t want to design these systems so that they were usable to a general audience, like a cash machine.

These systems still of course need to be usable. But expert users still need to access specialist functions. They can’t afford for them to be removed for the sake of simplicity.

“Don’t make me think” is a wise mantra. But we don’t always consider what our users can bring to the table. If our goal is to create knowledge, we must do this.

My point in saying this is that to the right person, more data-like or technical information is just what they need to complete their goal. This is because their existing understanding is what creates the context required to create knowledge.


Are these concepts useful or not? Let me know what you think in the comments.

Duncan Stephen

Photo of Duncan Stephen

I lead teams and organisations to make human-centred decisions. I am a lead content designer and information architect at the Scottish Government.

Email — contact@duncanstephen.net

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