What Is Information Architecture & Why It's Important?

What Is Information Architecture?

Coined in 1975, information architecture (IA) was the term describing the need to transform data into information that people can access and use. Nowadays, the definition evolved, although there is no fixed, unified understanding of this term. Rosenfield and Morville (2002, via Dillon & Turnbull, 2005) present four possibilities:

1. The combination of organization, labeling, and navigation schemes within an information system.
2. The structural design of an information space to facilitate task completion and intuitive access to content.
3. The art and science of structuring and classifying websites and intranets to help people find and manage information.
4. An emerging discipline and community of practice focusing on bringing principles of design and architecture to the digital landscape.

An effective information architecture should consider three elements:

1. User – Who will access the information? What are the user’s expectations? How will they use it?
2. Context – Where is the user looking for the data? How, when, and why will the user access the content?
3. Content – What kind of data does the information architecture contain? How much information is there? What format is the data in?

Why Is Information Architecture Important?

With the rapid expansion of technology, namely computing, vast amounts of data became accessible. However, when collected in a rough state, the information was unreadable for an average user, rendering them useless – unless for experts in the field: data scientists. Information architecture is the means to eliminate this issue.

As a part of product development, information architecture is designed to make the information accessible for the interested parties: the users and the owners.

Without information architecture, consumers would be lost in the vast amounts of data available on the web. They desire to find information quickly, efficiently, and effectively, so raw information is not an option.

The owners of a product also need to acquire information, though of a different type. They need insight into the financial performance of the product or the data about the users themselves to prepare better marketing strategies. These would not be available (or would take too many resources to comprehend) without an established architecture.

The Core Concepts of Information Architecture And the Challenges to Them

An information architecture is a complex creation. To understand it better, we will discuss the main concepts related to it – the aspects that are introduced in every information architecture, along with the potential difficulties related to them.

Information Structuring

The first element is connected with the accessibility and efficiency of an information architecture. The data has to be organized and categorized consistently. However, there are some potential challenges to this process:

• Ambiguity – The first challenge is the possible ambiguity of the terms used. The information is structured and labeled with the use of language, which is frequently vague and may sometimes mislead the users.
• Heterogeneity – Another element that poses a challenge to data structuring is heterogeneity. The data provided from one source often contains information unrelated to each other, thus making it much more difficult to prepare a reasonable structure.
• Different perspectives – Have you ever entered two similar websites and seen some information categorized differently? It is the result of different perspectives. Whoever structures the data, they do it from their perspective, and a different person with the same guidelines might create a different structure. Yet, from the user’s point of view, this makes it much more difficult to navigate through the vast amounts of information – especially if the user is already familiar with one structure and then stumbles upon a different one.

Navigation

Secondly, there is navigation. To create the perfect information architecture, you need to use the best UX design patterns designed for this purpose. Common issues with this aspect include:

• User’s location – The main challenge here is related to the knowledge location of the user within an information architecture. Often, this aspect is not clear to the user, thus making it more difficult for them to head in the right direction.
• Invisible navigation options – The user needs to be able to navigate through the architecture quickly, not spend time searching for ways to move through it. This, however, might be easily solved by ensuring that visible navigation elements are written into developers’ guidelines.
• Overabundance of navigation options – The more isn’t always the better, and such is the case with navigation techniques used in an information architecture. Too many options will simply make the app or website messy, actually increasing the difficulty of moving between different information.

Searching Systems

The whole point of information architecture is to search for data. Therefore, the search systems are of utmost importance. The main issue that may occur here is the lack of optimization in two areas:

• External – The external searches are mainly connected to SEO and organic traffic. Many users might look for information but without a certain source in mind – they will likely enter your website through search results. In this case, you need to ensure that the right keywords will direct the users to a relevant location within your data architecture.
• Internal – The search feature in your app, system, or website is also crucial. Yet, in many cases, this aspect does not perform well. For instance, you may often see search engines within a website that only find exact matches, often omitting the truly relevant information. To ensure that your information architecture is effective, you need to ensure that the search feature is indeed helpful.

Labeling Systems

Labels are crucial in a structured database, yet they shouldn’t be attached at random. Instead, a labeling system should be introduced to ensure that the information architecture is scalable – a set of rules on how the labels are assigned to particular pieces of data. This concept is also prone to some problems:

• Irrelevant labels – The labels have to be created with the target user in mind so that the people accessing the data can navigate through them and understand the information better. Yet, often, due to the discrepancy between the level of knowledge of the architecture’s creators and the final users, the labels are irrelevant, making the usage of the system harder.
• Consistency – This is why you require a system, rather than just creating certain labels and assigning them. Often, it is possible to use different terms to describe a phenomenon. The users of the architecture are likely to utilize them interchangeably. Yet, the whole architecture needs to be consistent since, otherwise, it could hinder navigation.

Scalable Information Architecture and AI

With the current AI boom and the so-called Industrial Revolution 4.0, it should not be surprising that artificial intelligence is also used to build effective information architectures. What can it help with?

• Content categorization – With the proper rules set up, an AI-based system is capable of automatically categorizing information to create the labeling system quickly and effortlessly.
• Content mapping – AI can also be used to connect related pieces of information and offer the users quick navigation options between them.

There are numerous examples of AI being used in information architecture. E-commerce (but also banks, and other sellers) leverage it to automatically generate recommendations for the customers; news websites exercise the content categorization features to tag news automatically; even Chat GPT may be used to structure data on known objects. Artificial intelligence accelerates the data architecture building process significantly, thus it is a direction worth exploring.

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Information Architecture: The Takeaway

Information architecture is the act of structuring, categorizing, and making data accessible. It aims to make information available for more users than purely data scientists. The process may be further accelerated with the use of AI, which is capable of labeling and structuring data automatically and making the navigation process easier for the users.

References:

• Dillon, A., & Turnbull, D. (2005). Information Architecture. In Encyclopedia of Library and Information Science. New York: Marcel Dekker. Retrieved from http://hdl.handle.net/10150/105971.
• Rosenfeld, L., & Morville, P. (1998). Information architecture for the world wide web. O’Reilly Media.