Imagine you own a fleet of taco trucks. Awesome, right? The neon sign glows at night in the club district and units are perfectly placed for the lunch rush in downtown the next day. The menus are stacked. The orders keep coming. But behind the scenes: ingredients arrive late, new franchisees don’t create the same quality level as the original trucks, and customers don’t know if the tacos are really as ethically and ecologically sourced as the signs claim.
Now, even if sales are strong today, how sustainable is that business?
How sustainable is any business where the different stakeholders don’t have the answers to the questions they need or an operational foundation that supports them achieving their objectives? In this month’s episode of the OmnichannelX Podcast, we explore a topic that seldom gets the spotlight but quietly defines whether your digital work truly moves the needle: Information Architecture (IA).
I sat down with Dan Brown, seasoned Information Architect, and we drew on this simple taco‑truck example to reveal a core insight: if the information underneath is mis-shaped, business suffers, and the numbers can’t be trusted. Not because the data is wrong, but because the structure isn’t allowing performance reporting, personalisation, or contextual delivery to the people who need it.
IA is the unseen scaffolding of success
Most teams think of IA as menus and taxonomies. Dan calls that “the surface layer”. The real work happens below — connecting questions, data, and decisions.
Whether it’s a customer deciding to stop scrolling or move on to the next option, a taco truck owner deciding how to restock ingredients, or a fleet manager coordinating vehicles, good IA determines whether users find what they need.
When information is structured around human questions –What should I pay attention to? What’s next? – the whole business moves more smoothly and effectiveness reports can connect the dots from data to insights.
Telling stories with data
Story drives decisions. Data merely provides the foundation. Dan distinguishes sharply between database architecture, which efficiently stores information, and information architecture, which transforms information into understanding. While both have their place, only information architecture enables human comprehension of complex systems.
We often construct sophisticated databases yet neglect the narrative framework that makes them valuable. Tables organise information through rows and columns, but they cannot convey meaning.
“IA creates multiple pathways through the same information,” Dan said. “Each one tailored to different user needs.”
This approach transforms omnichannel confusion into coherent experiences. Different audiences such as customers, partners, and employees, require unique navigation through to a shared truth.
Without such frameworks, organisations fail to generate value. They simply present data when people need answers.
“The world is made of data for better or worse,” Dan notes. “There’s an increasing demand from everyday consumers to executives for access in ways that are meaningful to us.”
From gut feelings to evidence: A taxonomy in action
On a recent project, we showed how small structural choices shape the whole system.
The team had introduced a controlled vocabulary to standardise how content was tagged. Instead of writers typing their own keywords as they came to mind, they selected from a defined set of terms linked to wider business rules, analytics requirements, and customer journeys.
What looked like a simple dropdown list quietly reshaped their operations. Consistent tags fed cleaner performance data because every piece of content was described in the same way, using the same terms, with no free-text variations to distort the analytics.
In most organisations, open keyword fields create dozens of near-duplicates for the same idea – “FAQ”, “faqs”, “common questions”, “help”, plus inconsistencies and misspellings. Systems cannot reliably group or compare those variations, which fragments dashboards and hides trends. With a controlled vocabulary, the noise disappears. Every item labelled with a term is genuinely comparable, so patterns stand out and insights become trustworthy.
The same taxonomy also powered internal use cases. Product teams could curate retrospectives, analyse historic performance, and cluster content and data by topic even when nothing customer-facing displayed those tags. A shared structure became a kind of internal compass that let teams answer practical questions: What’s performing well? What’s missing? Where are we over- or under-investing?
The content effectiveness programme sat on top of this foundation. Baseline metrics, dashboards, and conversion funnels only worked because tagging was stable. Without that consistency, reporting broke down. With it, the organisation could map data to decisions: which topics needed investment, which journeys were stalling, and which content was truly earning its keep.
The lesson mirrors our taco-truck analogy. When the underlying structure is sound, insight and performance are enabled.
Telling the story of IA’s value
We discussed another category of stakeholder question: internal value reporting. Any business solution needs funding, support, engagement, and, from some participants, compliance.
This means communicating up, laterally, and down, effectively.
We looked at the types of questions that business stakeholders have and how to address them for IA, or any other change initiative, laddering every question up to a 3×3 matrix of themes and stakeholder focus areas.
Business metrics can broadly be laddered up to 3 meta-questions that define business health:
- Are we growing? Is the top line going up? Are we getting more customers? Are we expanding our reach, mindshare, marketshare, etc.?
- Are our customers happy? Are we keeping relationships healthy, or are we burning our relationships for the sake of other goals like growth or efficiency?
- Are we being efficient? Are we profitable? Scalable? Wasteful?
If any of these 3 is out of alignment, a business isn’t sustainable in the long term. For example, growth and efficiency could be going well, but if customers engage, get frustrated, and leave, growth will slow. If customers are delighted but the business can’t scale due to inefficiencies, it won’t grow. And so on.
And these questions might be asked in different ways by 3 meta-stakeholder types:
- Money-focused people
- Experience-focused people
- Tech-focused people
A money person might care about efficiency in terms of revenue and profit, but a tech person might see efficiency in terms of scalability and performance. An experience-oriented person sees customer satisfaction as a direct benefit, but a tech-focused person might see it in terms of system stability and uptime.
Having this high-level framework is part of how you communicate the benefits of an information architecture, or other strategy or initiative, especially to executives who can impact your goals.
We discussed the usefulness of mapping users’ questions to these top-level concerns, and thus making initiatives like IA less abstract, and show it as a mechanism that moves the needle.
“Connect user questions to executive concerns. Show how efficiently answering them drives those three metrics.” – Noz Urbina
AI and why this especially matters now
Without building clear pathways from experience to data to analysis to action, organisations face
- questions that miss the mark
- continued data fragmentation, and
- metrics tracking busywork instead of outcomes.
And all this structure – it’s really great for AIs, too. In the (extremely long and techie) study on Cornell University’s arXiv.org, researchers demonstrated that AIs perform better when fed structured, tagged information instead of raw text. With the right IA, that structure can get inserted automatically without content authors needing to worry about them.
What you’ll take away from this podcast
- Why IA is more than website navigation.
- How to frame use‑cases as questions users are asking (conscious and unconscious).
- The difference between database architecture (how data is stored) and information architecture (how meaning is created).
- Why target communications at three stakeholder types: the money‑focused, the experience‑focused, and the tech‑focused.
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Truth Collapse podcast: Building resilient information spaces
Dan Brown joined us for the Truth Collapse podcast episode, too, and we explored how we can redesign information spaces for trust, context, and long-term integrity — and why it matters now more than ever.
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