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:
- The Utility Gap
- Filter Failure
- Information Abundance
- 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.
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.
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.
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.
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.
Yes, while Twitter is most engaging when tweets are firing away, it is also a poster child for propagating information overload.