IA Strategy: Addressing the Signatures of Information Overload

Finding Our Way

Navigating the practice of Information Architecture

A column by Nathaniel Davis
February 6, 2012

The one thing we know about information overload on the Web is that we don’t know enough. The rapid rate at which people and organizations create and propagate information complicates our getting a grip on information overload in the domain of information technology. Our information includes things like our Honey-Do lists, gigabytes of digital documents, and the deluge of email messages that pile up in our email inboxes. The amount of information we consume and manage is growing in both its volume and volatility. Probably worse than the self-inflicted burden of information glut that we’ve invented for ourselves is the fact that the less we know about information overload, the less we can know about the relevance of our collective stockpiles of information.

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Signatures of Information Overload

In this month’s column, I’d like to broaden the scope of our IA strategy lens by raising awareness of six information signatures that many of us may have observed in practice, but never related collectively to the information overload problem:

  • Feedback
  • The Utility Gap
  • Filter Failure
  • Information Abundance
  • Volatility
  • The Literacy Gap

Taking into account the pervasive digital epidemic of information overload and bringing greater attention to these six information signatures on your next project can help you to optimize the resilience of your IA recommendations.

Building a vocabulary around the topic of information overload should be a goal of serious IA practitioners. Why? Because it seems like everyone else is having a discussion about it. Type the phrase information overload into Google News, and you’ll see what I mean.

We commonly associate information overload on the Web with the voluminous numbers of email messages, tweets, feeds, and other social interactions that inevitably put a drain on our productivity. According to Basex, a knowledge economy research firm, as of 2010, the productivity drain that information overload causes costs the U.S. economy at least 997 billion dollars per year in reduced productivity and innovation. Armed with this estimate—give or take a few billion dollars—we can make the case that mitigating information overload is a worthy cause for UX professionals.


As an information architect or UX designer, how often have you had a professional conversation about information overload with your clients or peers? For many of us, I’m guessing the answer is seldom to never. Yet, information overload is the elephant in the room that acts against the success of every project we take on.

Maybe it’s because we have a hard time recognizing the signatures of information overload through the work that we typically do. Plus, the tools information architects and UX designers might use to assess information overload—like quantitative and qualitative user research—while useful, have one disadvantage in this case. They expose information overload only through indirect means—similar to the way physicists detect the behaviors of subatomic particles: by measuring the effects they have on other objects around them. Looking back in time; never at the actual event.

For example, it’s commonly only after a proactive Web site analysis or a client’s calling our attention to a major change in consumer behavior on a site that we discover information overload might be contributing to the diminished usability of both the site and its underlying information architecture. I like to refer to the observed affects of information overload that we glean from quantitative and qualitative research as feedback, which I’ve depicted in Figure 1. Common types of feedback that many of us might recognize are anxiety, the affects of the paradox of choice, and people’s general inefficiency in performing a task.

Figure 1—Information overload signature: Feedback
Information overload signature: Feedback

The Utility Gap

With their reduced sense of awareness about the information that they create and consume, users and entire business organizations have slowly become curators of information domains that are becoming less intelligible over time. Why? Because the gap between the information that we create and own and the information that we use and find usable is widening at an exponential rate. I call this growing chasm the utility gap, shown in Figure 2.

Figure 2—Information overload signature: The Utility Gap
Information overload signature: The Utility Gap

Search engines are great tools for diving into the abyss of the content that we own. It is promising that more intelligent search platforms probe for deeper content, or data, relationships to encourage greater discretion in information use—what Oracle|Endeca calls information visibility. However, these appliances are primarily for mid-size and large enterprise businesses within niche markets. As a result, handling utility gaps for individuals and smaller organizations will still require much effort.

Filter Failure, Information Abundance, and Volatility

In 2008, Clay Shirky introduced the provocative notion that information overload has been around since the invention of the printing press and, thus, that information overload is not the problem. Rather, filter failure, depicted in Figure 3, is the problem that we must address. Shirky argued that, on the Web, the practice of “filtering for quality” is no longer prevalent, so we now struggle with filtering the quality of both the inbound/consumption and outbound/distribution flows of information.

Figure 3—Information overload signature: Filter Failure
Information overload signature: Filter Failure

In summary, Shirky calls for the design of better filters. Although Shirky never defines what a digital filter is, if we assume that he was referring to the basic concept of filtering, it’s possible that he meant the removal of unwanted information in our consumption, creation, and distribution of information. Shirky argues that better filters help mitigate information overload. But, if filtering were the only capability we used to overcome information overload, the perpetual behavior of most users in consuming new information would eventually cause information abundance to rear its ugly head, as shown in Figure 4. In other words, we would eventually face an abundance of filtered information that would still contribute to the condition of information overload.

Figure 4—Information overload signature: Abundance
Information overload signature: Abundance

Twitter provides a good example of filtered information in abundance. We see instances where people have made hundreds to thousands of tweets, while following tens to hundreds of others who are also tweeting. Filtering for quality is a common motivation for the people who craft these messages of 140 characters or less that they send to the folks who are following them.

But there is no way to control the volatility of tweets coming from the people you follow, depicted in Figure 5. For example, people who decide to give you a play-by-play account of a professional conference they’re attending. While some may find these frequent updates valuable, others might view receiving 10 to 20 tweets from a single individual within a span of 45 minutes slightly cumbersome—especially when they’re following several others who are at the same conference! Consequently, having to scroll through all of these tweets can become an information-loaded experience that produces both anxiety and inefficiency.

Figure 5—Information overload signature: Volatility
Information overload signature: Volatility

Yes, while Twitter is most engaging when tweets are firing away, it is also a poster child for propagating information overload.

Got Digital Literacy? The Literacy Gap

In his article, “Information Overload, Information Architecture, and Digital Literacy,” Tibor Koltay responded to an article I had written, in which I had introduced filter failure and information abundance as signatures of information overload. Koltay observed that digital “literacies have their place in treating information overload.” As the term would apply to information overload and information architecture, digital literacy is users’ ability to responsibly curate their own user-generated knowledge when we give them the tools to do so.

Koltay—and likely most of you who are reading this column—have observed how Web 2.0 and the use of folksonomies have created conditions that result in information overload. When we provide applications that let users manage information, and those users have limited to no awareness of knowledge organization for the Web, the information architectures that evolve for users and the entire system may be less than optimal.

Since most users are not equipped to produce sound classification schemes or efficient top-down taxonomies on their own, their impact on any system creates what I call a literacy gap, depicted in Figure 6. Depending on the other signatures of information overload that play out in users’ interaction with a system, the consequences of their literacy gap can lead to information overload. Koltay’s article makes this claim, and I agree.

Figure 6—Information overload signature: The Literacy Gap
Information overload signature: The Literacy Gap

Recap: Assessing Your Strategic Readiness for Information Overload

Depending on the complexity of the domain for which you must provide an information architecture, part of your IA strategy should take into account the threat of information overload.

As I’ve just discussed, there are several signatures of information overload that practitioners of information architecture can isolate when planning an IA strategy, allowing the achievement of greater resilience against information overload. Addressing the six signatures that Table 1 outlines is not a silver bullet that completely mitigates information overload. But doing so should get you moving in the right direction, and you can apply this approach to an object, a page, or an entire Web site.

Table 1—Signatures of information overload
Information Overload Signature Summary IA Strategic Assessment
Feedback Undesirable human performance or behavioral responses as a consequence of information overload Have you tested the effectiveness of your recommendations? Have you recommended analytics and future review cycles for spotting signs of information overload?
The Utility Gap The amount of unusable information that is stored within a domain What efforts have you made to improve the usefulness of the information that people use often versus the content they’ll use rarely?
Filter Failure Ineffective controls for determining content quality and relevance Have you seeded your information architecture with sound information organization and techniques for enabling associative relationships between content? Did your information architecture consider the impact of a user-managed architecture? (See Literacy Gap.)
Abundance Excessive amounts of information, or content Have you estimated the content growth and its future impact within a domain?
Volatility Increased rate of information flow Does your information architecture consider the frequency with which people or organizations create, consume, and distribute information?
The Literacy Gap The degree of education that a user needs to effectively use and contribute to a knowledge system and information architecture What affordances can you provide to promote digital literacy for information architecture? To what degree can you automate the creation of new navigation, information organization, and content relationships on behalf of your users?


The signatures of information overload can surface anywhere, and there’s still much to learn about handling their effects. Over past decades, we have encountered information overload in visual design, text, and interaction design. Systems can even become so physically overloaded with information that users experience long delays in page loads or limitations in their ability to store more data. For information architecture, the threat of information overload is equally real. As a result, information architecture plays an important role in helping people and organizations combat information overload.

To ensure the thinking behind your next IA recommendations is sound, ask yourself these strategic questions: Does my solution consider the risks of information overload? Do my assumptions project far enough into the future to enable a sustainable information architecture and user experience?

Lastly, how much responsibility for managing information are we pushing onto users versus what we do for them automatically? The more social in nature our systems become, the more we should keep an eye on their evolution over time. Think twice when building in self-service features for users. For example, ask yourself, What is the likelihood that information abundance and user-managed content might lead to a gap in utility and filter failure?

In this article, I have reviewed six traceable signatures of impending conditions of information overload. If, as an IA practitioner, you hold yourself to high standards, I challenge you to test the use of these signatures. I also encourage you to discover new ones. Then, apply and share your insights, because almost 1 trillion dollars of lost productivity are on the table for organizations of all sizes, who could benefit from reducing these costs through a sound information architecture strategy. 


Davis, Nathaniel. “Information Overload, Reloaded.” Bulletin of the American Society for Information Science and Technology, 2011. Retrieved January 31, 2012.

Koltay, Tibor. “Information Overload, Information Architecture, and Digital Literacy.” Bulletin of the American Society for Information Science and Technology, 2011. January 31, 2012.

Shirky, Clay. “It’s Not Information Overload. It’s Filter Failure.” Web 2.0 Expo NY. Sebastopol, CA: O’Reilly Media. 2008. January 31, 2012.

Founder at Methodbrain

Franklin Park, New Jersey, USA

Nathaniel DavisNathaniel has over 25 years of experience in human-computer interaction and is a leading advocate for the advancement of information architecture as an area of research and practice. He began practicing information architecture in the late ’90s, then focused on information architecture as his primary area of interest in 2006. He has made a study of information-architecture theory and how that theory translates into science, workable software, and methods that improve human interaction in complex information environments. Nathaniel was formerly director of information architecture at Prudential. His information-architecture consultancy Methodbrain specializes in UI structural engineering.  Read More

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