While reorganizing my library a few weeks ago, I came across a handout from a 2003 workshop by my friend Lou Rosenfeld titled Enterprise Information Architecture: Because Users Don’t Care About Your Org Chart.

Lots of ideas quickly become obsolete in tech. But after 18 years, the idea that users don’t care about your org chart is still relevant. Teams still ship systems that reflect their internal structures. IA is still crucial to addressing the issue.

Few teams set out to design inwardly-focused systems. Instead, they inadvertently arrive at solutions that feel “natural” — i.e., that mirror their structures. Subtly, the systems they design come to reflect distinctions inherent in their orgs.

In 1968, computer programmer Melvin Conway wrote that “organizations which design systems… are constrained to produce designs which are copies of the communication structures of these organizations.” This idea is now referred to as Conway’s Law.

Conway suggested teams should structure design efforts around their communication needs. Organizations should be “lean and flexible” so teams can shift concepts as needed. This is easier said than done — even with today’s leaner organizations and more efficient communications. Incentive structures play as important a role today as they did in the late 1960s, and significant reorgs lie outside design’s remit.

Still, design can help. Producing effective user-centered systems requires that stakeholders see beyond internal needs and drivers. The process of designing an IA is especially well-suited to the task, since it entails

  1. Understanding users’ domain-specific mental models
  2. Synthesizing research-driven insights
  3. Reflecting insights to stakeholders as conceptual structures that are more broadly relatable

User-centered models may feel wrong at first. They may seem unsophisticated or naive. Design can build confidence in the models by producing tangible artifacts, which can be used to iterate and validate new structural directions with users.

Ultimately, working on a particular subject within a particular structure leads to deep — yet inward-looking — domain expertise. Creating relatable systems requires that we step out of our comfort zones to understand others’ perspectives. IA helps.

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