Disambiguation is at the heart of adopting a definition

Posts and articles by Nathaniel Davis

A formal collection of proposed and existing IA-related terms


Coming Soon.


This site contains selected research, publications, and perspectives by Nathaniel Davis. The objective of this site is to share insights and bring awareness to information architecture. Works from other practitioners and related industry content are also featured.

Eyes On the Field

Notable publications by practitioners, researchers, and information architecture advocates.

Peter Morville reconsiders his previous framing of information architecture and provokes a conversation about practicing IA beyond business and the Web to change minds through linguistic and categorical alignment.

The “structural design of shared information environments” requires engineering. This article introduces a professional path for IA practitioners with a passion for modeling.

Jason Hobbs reflects on the void left by the closing of the IA Institute. The institute may be gone but the field must continue to build discipline to achieve its true relevance to society.

Uday Gajendar argues how the professional future of design practice will require teams to go “meta.” 

Library of additional articles coming soon.

In the works:

Poor website structure costs more than you think.

Every website and digital product has a structure. Over time, digital user interfaces add “structural debt.” When structural debt goes unaddressed, it compounds and affects everything from site usability and user experience to your team’s ability to collaborate effectively.

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annual cost of structural debt per team member

IA viewpoint: Nathaniel davis
November, 2o20

The Thinking Before Design Thinking

“Information architecture is a field of study that explores how to facilitate shared understanding and alignment through conceptual clarity.” You can argue that it’s the thinking you do before design thinking (a human-centered approach for exploring solutions). Information architecture practitioners that specialize in alignment and sensemaking offer a unique lens for framing assumptions that are essential to design activity.

Despite its effectiveness, information architecture’s importance to the strategy and structure of user interfaces was lost in the widespread adoption of user experience design and agile methods over the last two decades. Today, the information architecture lens is used narrowly to solve for navigation, labeling, and basic content organization. Without proper sound sensemaking approaches, digital design teams, products, and services often find themselves in desperate need of clarity.

Since most digital teams fail to recognize their IA roots, some teams look to design thinking to fill the gap of confusion. However, design thinking requires sound framing of assumptions and constraints as well. Fortunately, this can be facilitated with skilled information architects.

If digital teams want to create better solutions and improve their ability to innovate, they should consider reintroducing the practice of information architecture into their process.

Nathaniel is the Founder of  Methodbrain and a leading advocate for the advancement of information architecture as an area of research and practice. He is a former co-chair of the IA Summit and co-founder the  IAC.  Nathaniel began his private research in information architecture in 2006 and established DSIA Research for Information Architecture in 2010 to help mature IA theory, science, and practice. He writes the information architecture column for UXmatters, is a contributor to the Bulletin for the Association of Information Science & Technology, and blogs periodically on methodbrain.com. 

Information architects help teams build clarity

IA practitioners make it their business to enrich strategy and solutions with a shared understanding and sound assumptions.


At times, our conceptual assertions and the language we use to communicate can get confusing as teams get larger or a problem space gets more complex. Leadership and solution teams can leverage information architects for an alternative lens for synthesizing insights.

Skilled information architects break down ambiguity to express the information and behavioral patterns in a problem space. 

Strategic Support

Sometimes strategy is not about what you want but the questions you ask. When you’re trying to create a solution for a project that leaves more questions than answers, your team would likely benefit from having an information architect.

Asking the right questions is not a design skill but a skill of architecture. The most experienced IA practitioners have developed this important architectural competency.

Sustainable Structures

Strategy, digital interfaces, and even internal processes have structure. Teams that can reference and maintain their structures will improve their outcomes, promote accountability, and reduce risk.

Information architects blend conceptual modeling techniques with deep knowledge in information science. IA practitioners who specialize in structural modeling are ideal for demonstrating deep relationships across language, behavior, content, and data.

When should UI & UX teams consider an IA practitioner?

IA practitioners provide leadership in conceptual clarity. Practitioners usually have competency in at least one of the following areas.


The facilitation of team alignment to assist in the identification of constraints, clarification of scope, and identification of gaps and opportunities.

This is useful for getting owners, clients, and teams on the same page.


The synthesis of research and framing of a strategy that satisfies owner and “user” objectives.

This is useful for rationalizing frameworks for effectively engaging users based on context.


The science, technical language, and approach to designing and engineering a conceptual structure for complexity and scale.

This is useful for modeling the structure of websites and apps to improve the relevant disclosure of content.

The is also useful for maintaining system-wide views of underlying conceptual relationships that drive information interaction.

Oh, yeah. People like to call this information architecture.


Systemic-level research and modeling to promote alignment, scope definition, and sustainable outcomes for a social context

This is useful for investigating problems spaces where technology is not the primary interface of communication. For example, helping a government policymaker assess a civic plan. Or, mapping the organizational dynamics inherent in a collaborative space to improve efficiency and outcomes. 

Read “What is Information Architecture?” to get a formal perspective on the variety in IA subject matter.

DSIA-based Articles

DSIA articles are based on foundational research.

Information overload is a costly drain on productivity that stems from vast amounts of information and failure to filter information as presented. Information architects (IAs) are in a position to address the challenge.

The “structural design of shared information environments” requires engineering. This article introduces an alternative path for IA practitioners with a passion for modeling.

The one thing we know about information overload on the Web is that we don’t know enough. This article reviews six IO signatures to consider on your next project.

Theory—as a synthesis of what, how, and why we do what we do—can provide the frameworks to reinforce IA discipline. This article offers an example.

An Information Architecture Maturity Model


In this World IA Day video, Nathaniel Davis discusses the general function of information architecture when creating user interfaces. It differentiates information architecture from UX design activity and introduces an IA maturity model. The presentation posits a value proposition of information architecture, four essential IA modeling activities, and six pillars of UI structure. 

Related Articles

Recent articles related to the field of information architecture. 

AI practitioners need to build partnerships with community members, stakeholders, and experts to help them better understand the world they’re interacting with and the implications of making mistakes.

This three part article provides a comparison of the strengths and limitations of Knowledge Graphs versus Property Graphs and guidance on their respective capabilities.

“Widely used image-recognition data set is teaching computer-vision software to classify images using racist and misogynistic slurs.”

A review of ontologies and knowledge graphs, describing how they’re different and how they work together to organize data and information.

“Why Computing Belongs Within the Social Sciences”


Because computing as a discipline is becoming progressively more entangled within the human and social lifeworld, computing as an academic discipline must move away from engineering-inspired curricular models and integrate the analytic lenses supplied by social science theories and methodologies.

Illustrations & Posters

Previously published or presented at industry conferences.