A costly drain on productivity, information overload can stem from not only vast amounts of information but also a failure to filter information as presented. Information architects (IAs) are in a position to address the challenge, starting by distinguishing between macro information overload and micro information overload. At the macro level, physical storage limits and processing capacities present an obstacle to information access. Information overload at the micro level is a failure to filter information, interfering with a user’s cognitive processing by presenting too many images, concepts, messages and other elements to sort through. If systems architects are skilled at recognizing signs of macro information overload, IAs should be able to identify signals of overload at the micro level, drawing on user experience design methods, and should develop ways to spot when information presented on a web page threatens to exceed a typical user’s threshold for effective cognitive intake. The solution presents fertile ground for research and potential time and cost savings. 

information overload
information filtering
mental processes
user experience
information architecture

Bulletin, June/July 2011


Information Overload, Reloaded

by Nathaniel Davis

Contemporary information architecture can help reframe how we approach this thing called information overload.

Look around you. You’re probably in a room with at least 50 recognizable objects: lights, carpet on the floor – or maybe hardwood or tile; a table, a chair ¬– windows along a wall; a computer monitor, keyboard and a mouse. There’s possibly a tablet computer, paper or pen close by. Your shoes, pants and shirt can be included – even your arms and legs that are in view. With each object, you are able to discern texture – dimples on the flat-coated wall created by the impression of a paint roller; the veining along the molding around a door frame, created by a paint brush; the woven pattern on the chair across the room. Shapes…color…depth. 

If we refer to known science and remind ourselves that our sense of sight is aided by the interaction with photons that reflect off the surface of every object within the room, we realize our eyes and brain are transacting millions of photons in a fraction of a second. 

Within this brief moment, our sense and orientation within and of the room is given dimension by a cognitive symphony that is orchestrated by a rhythm played out by the remainder of our human senses – sound, touch, smell and taste.

This process continues with each recurring fraction of time – second-by-second, minute-by-minute, hour-by-hour. Now, imagine stepping outside to absorb a panoramic dose of Mother Nature! Information is approaching you from all around – from the aromas of the bakery down the block to light from the distant sun. 

While we appear to easily take on Mother Nature – who packs the “information heat” of the universe – in the digital domain we somehow manage to create a backlog of inefficient and unprocessed information and the stress to go with it. This phenomenon is typically referred to as information overload. As we create new virtual realities within the domain of information technology, we are failing to do it as efficiently as Mother Nature – and we are doing it at a cost to our collective user experience and corporate bottom line.

According to Basex, a knowledge economy research firm and IORG member, information overload cost the U.S. economy at least $997 billion per year in reduced productivity and innovation as of 2010. 

An alarming reality that relates to this figure is that we are struggling to understand information overload. So, the economic and social impact of information overload will continue to rise as society’s voracious appetite for information proceeds. This article will briefly explore alternative thinking of information overload and what the field of information architecture can do to participate in finding solutions to this phenomenon.

Signatures of Information Overload
Discussions around information overload in context to information technology vary. Popular cognitive science notions of information overload (IO) describe it as a human experience, meaning that it’s something we experience internally as an overload of the senses that prevents us from focusing on a task at hand. 

Futurist Alvin Toffler, who popularized the term information overload, describes IO “as the difficulty a person can have understanding an issue and making decisions that can be caused by the presence of too much information.”[1] His definition came prior to the Internet, but can be used to describe what many experience at times.

Lastly, the Information Overload Research Group (IORG) – chartered by companies such as Xerox, IBM and Google – acknowledges information overload as “a problem that diminishes the productivity and quality of life of knowledge workers.” [2]

Cognitive science and Toffler agree that too much information can be a problem when trying to perform a task, and the IORG agrees as well – with a particular focus on how it leads to lost productivity of knowledge workers. 

The challenge with today’s popular perspectives of information overload, in most cases, is that information overload is inadvertently defined as the cause and the effect. In essence, we experience information overload because we have too much information. Or, we have too much information because we have information overload. So, what is the real problem? 

In a 2008 conference keynote, Clay Shirky argued that we’ve had an abundance of information since the advent of the printing press and that the problem for lost productivity is not information abundance, but the failure to filter information as it is either published or consumed [3]. His often-quoted argument was, “it’s not information overload. It’s filter failure.”

Any trending report you find will show how the creation of information is increasing at an exponential rate. And we can safely assume this trend will continue until humankind exhausts every avenue to channel and manipulate it, and with every technological resource available. My conceptualization of the Quartet Compression [4] – which suggests a co-dependence between man and technology – predicts this as well. So even if we are able to produce the filters suggested by Shirky, we will still need to address how to mitigate a domain that would eventually swell with an abundance of filtered information. 

While Shirky’s controversial attempt to reposition information overload as a non-issue fails to remove our concern for information abundance, filter failure adds further complexity around the subject matter: that we are battling an abundance of too much unfiltered information. 

As a result, filter failure does not replace information overload; it is an aspect of it. Along with information abundance, filter failure can be viewed as one of two identifiable signatures of information overload. 

Figure 1
Figure 1. Signatures of information overload: Information abundance and filter failure

What does information overload have to do with information architecture?
When we position information overload as a consequence of information abundance and filter failure, it becomes apparent that we are referring to a subject that should be of particular interest to the field of information architecture. Since the dawn of the public Internet, a particular function of information architecture has been to simplify how people navigate and use information that connects to the web [5], using a range of methods – inherently meant to manage information abundance and gaps in filtering – that might include domain modeling to more granular approaches like metadata and tagging. 

However, there is no contemporary information architecture (IA) perspective for information overload. While some may argue that Richard Saul Wurman explained IO on behalf of the field of information architecture in his book, Information Anxiety 2 [6], his perspective is much to do about the anxiety caused by abundant non-information that fails to produce understanding. 

A clear distinction between Wurman information architecture and a contemporary view of information architecture is that Wurman IA is about presenting information as a message in a clear and understandable manner. A contemporary perspective of information architecture is one of specialization and is concerned with the structure of “information spaces” and the use of information for the “shaping of context and connections” [7] for “facilitating effective communication” [8] in the domain of information technology. 

So allow me to demonstrate a contemporary information architecture perspective of information overload.

Rethinking Information Overload
To start, it will be useful to view information overload as an event where the abundance of information within a domain – such as a computer screen or workplace environment – triggers a failure of an underlying intention. The intention can be imparted to a person or an autonomous agent. It doesn’t matter.

You may be wondering why intention is central to this idea. Intention must be recognized because it contributes to the contextual conditions for any user goal that an information architecture strategy must accommodate and will consequently contribute to the definition of requirements for any set of related interactions.

Now, let’s refer back to the two signatures of information overload that were suggested previously.

Information abundance – the first signature of IO – can suggest that at some point information abundance meets a threshold that inhibits a systematic function. This type of information behavior within a domain reflects a quantitative relationship that fits well with physical correlations – such as a container, information storage system or some form of infrastructure. This is called macro information overload. As an event, we can graph macro information overload as a conditional point on a continuum (See Figure 2).

Macro IO Condition (of Technology) – Where the abundance of information becomes a quantitative obstruction to an underlying intention of a system 

An example of a macro IO condition is where information within a computing system meets the physical storage limit or processing capacity of said system. When this happens and the system is no longer able to satisfy the intention for which it was designed, information overload is realized. 

If the function of the hard drive component within a computer is to store information, but is no longer able to do so because it has no more physical space, a macro IO condition is realized. A common method for fixing a macro IO condition such as this is by adding another drive or replacing the existing drive with one that has a greater storage capacity.


Figure 2
Figure 2. Mapping example of macro information overload condition

Filter failure – the second signature of IO – can suggest that, at some point, information abundance will relate to a contextual threshold whereby an intention of a person or autonomous agent is obstructed. This is called micro information overload. A micro IO condition may not be the same for all users for a given set of information. As a result, it must be viewed as a range (See Figure 3).
 

Micro IO Condition (of UX) – Where the abundance of information becomes an obstruction to an underlying intention of an agent interacting with a system

An example of a micro IO condition is where abundant information within an interface obstructs a user’s ability to complete a transaction as intended. In this case, overload is more about a qualitative pre-condition than a quantitative one.

Micro IO conditions can also extend to offline events as well. This is why corporations are interested in information overload. For example, while an email application might let a user save unlimited messages and create unlimited custom folders, accommodating this approach may create enough macro load that a user’s offline intention can become obstructed. For example, not being able to find a message and/or in a timely manner, to act against a job request, can trigger a micro IO condition. 

Figure 3
Figure 3. Mapping example of micro information overload condition

What triggers a micro IO condition can be broad and complex. For instance, is there too much text? Are there too many graphics? Are we expressing too many ideas? Are there too many sounds? These questions can arise in the instance of a single screen scenario. 

However, what if we tried to investigate micro IO of an entire application – like email? We can ask whether there are too many messages. Or, are there too many features? Asking these questions instead of adding more infrastructure to accommodate abundance – which offers a macro IO solution – can potentially lead to reducing the experience of information overload by knowledge workers.

It’s worth noting the research approach typical of user experience design (UXD) is ideal for investigating root causes for micro IO. When done effectively user experience research will expose the underlying intentions that must be accommodated across the widest range of touch points. 

Integrating IO Concepts into Practice and Research
Historically, the measurement and observation of information overload have been its effects. We quantify how much it costs in money, time and lost effort. Or, we indirectly measure its effects through qualitative user research by identifying its byproducts – such as failed tasks, information anxiety, paradox of choice and quality of life. 

To mitigate information overload and its effects, we can attempt to directly quantify it through two co-dependent poles – of macro and micro conditional states – and by recognizing signatures that are precursors to an overload condition. 

If systems architects can follow the trend of the load on their platforms to predict when enhancements are necessary to avoid a macro IO condition, is it possible to benchmark and follow the trend of information in a way to avoid micro IO conditions? With a wealth of existing and future projects to glean from, this question is one the field of information architecture is primed to answer.

For those who have an interest in information architecture, you have an opportunity to explore information overload from a new angle. 

Future insights in modeling information overload may also move us closer to explaining with greater detail what it means to say, “There is too much information on the page.”

Finally, from a wider angle, I’m convinced that information overload is fundamentally propagated by people, which makes it mostly a social condition. This means that it’s likely that technology and information science won’t fix everything. But let’s say that only 20% of the estimated cost of information overload can be directly addressed through the efforts of information science professionals. If so, we would be helping businesses gain $200 billion in productivity.

I’m not sure how much that’s worth to governments and businesses, but my guess is that it’s a major incentive to keep practitioners of contemporary information architecture and other information science professionals close by as society explores new frontiers and extends its dependence on information technology. 

Resources Mentioned in the Article
[1] Information overload. (n.d.). Wikipedia. Retrieved April 2, 2011, from http://en.wikipedia.org/wiki/Information_overload.

[2] Information Overload Research Group (IORG). (n.d.). About IORG. iorgforum.org. Retrieved April 2, 2011, from http://iorgforum.org/about-iorg/.

[3] Shirkey, C. (2008). It's not information overload. It's filter failure [video]. Web 2.0 Expo NY. Sebastopol, CA: O'Reilly Media. Retrieved April 2, 2011, from www.youtube.com/watch?v=LabqeJEOQyI.

[4] Davis, N. (August/September 2010). Information architecture, black holes and discipline: On developing a framework for a practice of information architecture. Bulletin of the American Society for Information Science and Technology, 36(6), 25-29. Retrieved April 5, 2011, from www.asis.org/Bulletin/Aug-10/AugSep10_Davis.html.

[5] Davis, N. (April 2010). The function of information architecture. DSIA Portal of Information Architecture. Retrieved April 5, 2011, from www.methodbrain.com/dsia/the-basics-of-ia/The-Basic-Function-of-IA.cfm.

[6] Wurman, S. (2001). Information anxiety 2. Indianapolis: QUE.

[7] Hinton, A. (2009). The machineries of context. Journal of Information Architecture 1(1), 37-47.

[8] Garrett, J. J. (2002). ia/recon. jjg.net. Retrieved April 5, 2011, from www.jjg.net/ia/recon/.

© 2011 Nathaniel Davis


Nathaniel Davis is a practitioner and theorist in the field of information architecture. In April 2010 he launched the DSIA Research Initiative and DSIA Portal of Information Architecture in an effort to begin defining and communicating a distinct discipline of information architecture that is centered around theory, research and practice. He can be reached at natedavis.ia<at>methodbrain.com.