Navigating the Complexities of Information Architecture vs Data Architecture

"Dear Optimal Workshop

How do you make a small link attractive to people (icon vs. text)?"

— Cassie

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Dear Cassie,

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I'm going to dive straight into this interesting question with a good old game of Pros and Cons, and then offer a resolution of sorts, with a meandering thought or two along the way. Let's kick things off with Team Icon.

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The good side of icons: A picture is worth a 1000 words

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When shopping online, the number above the little shopping trolley icon tells me how badly behaved I’ve been, and if I click on it, I know I’ll get to gleefully review all the shoes I've selected so far. There’s a whole heap of icons out there like this that people have absorbed and can use without thinking twice. Marli Mesibov wrote a fantastic article on the use of icons for UX Booth on the use of icons that is well worth a look. Marli discusses how they work well on small screens, which is a definite bonus when you’re on the go! Young children who aren’t yet literate can easily figure out how to open and play Angry Birds on their parent’s smartphones thanks to icons. And icons also have a great capacity for bridging language barriers.

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The not so good side of icons: We’re too old for guessing games

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On the flipside, there are some issues that may huff and puff and blow that cute little home icon down. Starting with there being no consistent standard for them. Sure, there are a handful that are universal like home and print, but beyond that it seems to be a free-for-all. Icons are very much in the hands of the designer and this leaves a lot of room for confusion to grow like bacteria in a badly maintained office refrigerator. Difficult to understand icons can also seriously hinder a user’s ability to learn how to use your website or application. When icons don't communicate what they intend, well, you can guess what happens. In a great piece advocating for text over icons, Joshua Porter writes about an experience he had:

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"I have used this UI now for a week and I still have do a double-take each time I want to navigate. I’m not learning what the icons mean. The folder icon represents 'Projects', which I can usually remember (but I think I remember it because it’s simply the first and default option). The second icon, a factory, is actually a link to the 'Manage' screen, where you manage people and projects. This trips me up every time."

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If people can't pick up the meaning of your icons quickly and intuitively, they may just stop trying altogether. And now, over to Team Label.

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The good side of text: What you see is what you get

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Sometimes language really is the fastest vehicle you've got for delivering a message. If you choose the right words to label your links, you'll leave the user with very little doubt as to what lies beneath. It’s that simple. Carefully-considered and well-written labels can cut through the noise and leave minimal ambiguity in their wake. Quoting Joshua Porter again: "Nothing says 'manage' like 'manage'. In other words, in the battle of clarity between icons and labels, labels always win."

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The not so good side of text: Your flat shoe is my ballet pump

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Text labels can get messy and be just as confusing as unfamiliar icons! Words and phrases sometimes don’t mean the same thing to different people. One person’s flat enclosed shoe may be another person’s ballet pump, and the next person may be left scratching their head because they thought pumps were heels and all they wanted was a ballet flat! Text only labels can also become problematic if there isn’t a clear hierarchy of information, and if you have multiple links on one page or screen.  Bombarding people with a page of short text links may make it difficult for them to find a starting point. And text may also hold back people who speak other languages.

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The compromise: Pair icons up with text labels

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Because things are always better when we work together! Capitalise on the combined force of text and icons to solve the dilemma. And I don’t mean you should rely on hovers — make both text and icon visible at all times. Two great examples are Google Apps (because nothing says storage like a weird geometric shape...) and the iPhone App store (because the compass and magnifying glass would pose an interesting challenge without text...):

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So what comes next? (You can probably guess what I'm going to say)

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Whatever you decide to run with, test it. Use whatever techniques you have on hand to test all three possibilities — icons only, text only, and icons and text — on real people. No Pros and Cons list, however wonderful, can beat that. And you know, the results will probably surprise you. I ran a quick study recently using Chalkmark to find out where people on the ASOS women's shoes page would click to get to the homepage (and yes, I can alway find ways to make shoe shopping an integral part of my job). 28 people responded, and...

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...a whopping 89% of them clicked the logo, just 7% clicked the home icon, and just one person (the remaining 4%) clicked the label 'Home'. Enough said. Thanks for your question Cassie. To finish, here's some on-topic (and well-earned) comic relief (via @TechnicallyRon)

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The Ministry for Primary Industry’s (MPI) customers have some of the most varied information needs — possibly the most varied in New Zealand. MPI provides information on how to follow fishing rules, what the requirements are to sell dairy products at the market, and how to go about exporting honey to Asia. Their website mpi.govt.nz has all the information.

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However the previous website was dense and complicated, and MPI’s customers were struggling to find the information they needed, often calling the contact center instead — one of several indicators that people were lost and confused on the website.

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Ruth Hendry, Head of Strategic Growth at Springload, recently spoke at UX New Zealand, the leading UX and IA conference in New Zealand hosted by Optimal Workshop, about how new IA helped MPI’s broad range of customers find the information they needed.

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In her talk, Ruth takes us through the tips and techniques used to create an IA that met a wide variety of user needs. She covers the challenges they faced, what went well, what didn’t go so well, and what her team would do differently next time.

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Background on Ruth Hendry 💃🏻

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Ruth was Springload’s Content Director; now she’s Head of Strategic Growth. She has broad experience in content, UX, and customer-led design. A data nerd at heart, she uses analytics, research and testing to drive decision-making, resulting in digital experiences that put the customer at the forefront.

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At Springload Ruth has worked on large-scale content and information architecture projects for organisations including Massey University, Vodafone and Air New Zealand. She got into the world of websites in her native UK, working on Wildscreen's ARKive project. After she arrived in Aotearoa, she spent four years looking after Te Papa's digital content, including the live broadcast of the colossal squid dissection. She's Springload's resident cephalopod expert.

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She finds joy in a beautiful information architecture, but her desk is as messy as her websites are tidy.

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Contact Details:

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Email address: ruthbhendry@gmail.com

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LinkedIn URL: https://www.linkedin.com/in/ruth-hendry-658a0455/

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Food recalls, fishing rules, and forestry: creating an IA strategy for diverse audience needs 🎣

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Ruth begins her talk by defining IA. She says, “If IA is the way information is organized, structured, and labeled, then an IA strategy is the plan for how you achieve an effective, sustainable, people-focused IA.”

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Considering this, applying an IA strategy to the Ministry of Primary Industries (MPI) website was a challenge due to its diverse user groups. MPI is responsible for a range of things, such as publishing food recalls, looking after New Zealand’s biosecurity, outlining how much fish can be caught, how to export products, and even how to move pets between countries. Needless to say, the scope of this IA project was huge.

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The current state of the website was challenging to navigate. In fact, one customer said, “It’s hard to find what you need and hard to understand”. MPI Contact Center staff often found themselves simply guiding customers to the right information online over the phone. 

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So, in solving such a massive problem, does having an IA strategy work? Ruth says yes! And it can have a huge impact. She backs up her strategy with the results of this project before broadly outlining how she and her team achieved the following improvements.

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The project achieved:

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• 37% decrease in time spent on the home page and landing pages
  • Customers found where they needed to go, faster, using the new IA and navigation elements
• 21% decrease in on-page searches
  • People could find the content they need more easily
• 53% reduction in callers to MPI saying that they couldn’t find what they needed on the website
  • Users could more easily get information online

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Developing an IA strategy 🗺️

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Ruth attempts to summarize 14 weeks' worth of work that she and her team delivered in this project.

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Step one: Understanding the business

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During this step, Ruth and her team looked at finding out exactly what MPI wanted to achieve, what its current state is, what its digital maturity is, what its current IA was like (and the governance of it), how the site got to be in the way that it was, and what their hopes and aspirations were for their digital channels. They conducted:

• Stakeholder interviews and focus groups
• Reviewing many, many documents
• Domain and analogous search
• Website review

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Step two: Understand the customers

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In this step, the team looked at what people want to achieve on the site, their mental models (how they group and label information), their main challenges, and whether or not they understood what MPI does. They conducted:

• A review on website analytics and user needs
• In-person interviews and prototype testing
• Card sorts
• Intercepts
• Users surveys
• Treejack testing

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Step three: Create the strategy

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This talk doesn’t cover strategy development in depth, but Ruth shares some of the most interesting things she learned (outlined below) throughout this project that she’ll take into other IA strategy projects.

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Why it matters 🔥

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Throughout the project, Ruth felt that there were eight fundamental things that she would advise other teams to do when creating an IA strategy for large organizations with massively diverse customer needs. 

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1. Understand the business first: Their current IA is a window into their soul. It tells us what they value, what’s important to them, and also the stories that they want to tell their customers. By understanding the business, Ruth and her team were able to pinpoint what it was about the current IA that wasn’t working.
2. Create a customer matrix: Find the sweet spot of efficient and in-depth research. When an organization has a vast array of users and audience needs, it can often seem overwhelming. A customer matrix really helps to nail down who needs what information.
3. Card sort, then card sort again: They are the best way to understand how people’s mental model works. They are critical to understanding how information should be organized and labeled. They are particularly useful when dealing with large and diverse audiences! In the case of the MPI project, card sorts revealed a clear difference between business needs and personal needs, helping to inform the IA.
4. Involve designers: The earlier the better! User Interface (UI) decisions hugely influence the successful implementation of new IA and the overall user journey. Cross-discipline collaboration is the key to success!
5. Understand the tech: Your IA choice impacts design and tech decisions (and vice versa). IA and tech choices are becoming increasingly interrelated. Ruth stresses the importance of understanding the tech platforms involved before making IA recommendations and working with developers to ensure your recommendations are feasible.
6. Stakeholders can be your biggest and best advocates: Build trust with stakeholders early. They really see IA as a reflection of their organization and they care a lot about how it is presented.
7. IA change drives business change: You can change the story a business tells about itself. Projects like this, which are user-centric and champion audience thinking, can have a positive effect throughout the business, not just the customer. Sometimes internal business stakeholders' thinking needs to change before the final product can change.
8. IA is more than a menu: And your IA strategy should reflect that. IA captures design choices, content strategy, how technical systems can display content, etc.

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Your IA strategy needs to consider

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• Content strategy: How is content produced, governed, and maintained sustainably going forward?
• Content design: How is content designed and does it support a customer-focused IA?
• UI and visual design: Does UI and visual design support a customer-focused IA?
• Technical and functional requirements: Are they technically feasible in the CMS? And what do we need to support the changes, now and into the future?
• Business process change: How will business processes adapt to maintain IA changes sustainably in the long term?
• Change management and comms plan: How can we support the dissemination of key changes throughout the business, to key stakeholders, and to customers?

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Finally, Ruth reemphasizes that AI is more than just designing a new menu! There’s a lot more to consider when delivering a successful IA strategy that meets the needs of the customer - approach the project in a way that reflects this.

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As information professionals, we work with the “stuff” of information in our everyday work. We search for information, we spend time analyzing and synthesizing it, and we carefully create and structure it. Whether you elicit information from users and stakeholders, explore large data sets, design ‘journeys’ or interfaces, or create information architectures, understanding the information you are using and creating as information can help you do your work better.

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Kat King, Business Intelligence Analyst at the University of Michigan Library, recently spoke at UX New Zealand, the leading UX and IA conference in New Zealand hosted by Optimal Workshop, about understanding exactly what information is, and where it is, in our work.

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In her talk, Kat uses simple examples to teach you to “see” the information around you and understand what makes something “information” in the context of working as a human to accomplish something.

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Kat King bio 🎤

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Kat King is an Information Architect interested in language, meaning, and the things we make. She currently works as a Business Intelligence Analyst for the University of Michigan Library.

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Contact Details:

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Email: Katalogofchaos@gmail.com

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Where is the information? 📍🗺️

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Information theory can be dense and jargon-filled, and discussions in academic texts can feel divorced from the practice of actually working with information. We’re all told that information architecture is much more than website navigation. So, what is it? IA has a reputation for being difficult to understand, and in her talk, Kat attempts to help us understand what it is, where the information is, and what is it that we’re doing when we use IA methods.

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Kat defines IA as “the practice of ensuring ontological alignment”. ‘Ontological’ relates to concepts, categories, properties, and relationships. ‘Alignment’ means arrangements into appropriate relative positions. Therefore, information architecture is “the practice of ensuring concepts, categories and their properties and relationships are arranged into appropriate relative positions.”

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To align information then, you need to begin by sorting it into concepts and categories, which is difficult because information can sometimes be “slippery and abstract”. Kat argues this is the real reason that IA is sometimes hard to wrap our heads around. So, getting to the heart of the question, what is information? 

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Kat defines information as “a patterned relationship between differences that reduces uncertainty”. The key word here is ‘differences’. The trick to understanding and taming information is to identify what is different about sets of information. The next trick is to identify consistencies between these differences.

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This can be a little confusing, so Kat uses the example of picking fruit. We tend to use color (the difference) to identify when fruit is ripe and sweet. We know for a fact that, at some point, the fruit will be at its sweetest and, while there is a scientific way of identifying this point, we have to use the information we have at our disposal instead i.e. the colour of the fruit. The skin of the fruit in this example is like an interface - allowing a flow of information from the fruit’s ripening process to our eyes.

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Information categories 🧺🧺🧺

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The relationship between the information described in the fruit example can be split into two categories. “Information 1” is a factual, objective description of when the fruit is ripe (i.e. the science of why the fruit is the color that it is right now), whereas our subjective observation, based on color, is “Information 2”.

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• Information 1: Matter and energy, and their properties and interactions i.e. the laws of physics and universal truth or rules

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Information 1 poses challenges for us because we have a narrow range of perception, attention, and aggregation, which means we, as humans, can’t possibly understand the laws of nature just by observing. We have evolved to be simple, efficient observers of what is important to us. In other words, we don’t need to understand everything in order to get things right. We see patterns and generalize. Going back to the fruit example – we only need to know the color of ripe fruit, not the exact chemistry of why it is ripe.

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• Information 2: This is Information 1 that is given meaning by humans. This is done via processing semantic information, or “differences and structures that create meaning for people”.

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We use semantic information by processing concepts, patterns, categories, mental models, and even language as inputs to form our understanding. As social animals, we tend to reinforce general ‘truths’ about things because we’re constantly cooperating using shared information. General ‘truths’ are good enough.

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Kat uses the following interaction to demonstrate the interplay of different information.

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• Person 1: If the raspberries look good, can you get some for me?
• Person 2: How can tell is they’re good?
• Person 1: Get the ones that are the most red.

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In this interaction, the different pieces of information can be broken down by category:

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• Semantic information = Words and concepts
• Information 1 = Meaningful signs
• Information 2 = Perceptible differences
• Real life information = Raspberries

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Using our ability to communicate and understand concepts (words “red”, “good”, and “raspberries”) helps us to understand Information 2 (processing the words and concepts to understand that a red berry is good”), which aligns with Information 1 (the evolutionary science and ongoing consistency of red/ripe berries being sweet) that helps us decide when processing all of this information.

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So, now that we understand a little more about information, how does this influence our roles as designers?

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Why it matters 👀

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Thanks to our individual lived experiences, people have many different inputs/concepts about things. However, Kat points out that we’re pretty good at navigating these different concepts/inputs.

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Take conversations, for example. Conversations are our way of getting a “live” alignment of information. If we’re not on the same page we can ask each other questions to ensure we’re communicating semantic information accurately. 

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When we start to think about technology and digital products, the interfaces that we design and code become the information that is being transmitted, rather than words in a conversation. The design and presentation become semantic information structures, helping someone to understand the information we’re putting forward. This highlights the importance of aligning the interface (structure and semantic information) and the users' ontology (concepts and categories). For the interface to work, IA practitioners and designers need to know what most people understand to be true when they interact with information, concepts, and categories. 

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We need to find some sort of stability that means that most users can understand what they need to do to achieve a goal or make a decision. To do this, we need to find common ground between the semantic information (that might vary between users) so that users can have successful Information 2 style interactions (i.e. absorbing and understanding the concepts presented by the interface).

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To wrap up, let’s remind ourselves that information architecture is “the practice of ensuring concepts, categories and their properties and relations are arranged into appropriate and relevant positions”. As IA practitioners and designers, it’s our job to ensure that concepts and categories are arranged in structures that can be understood by the nuance of shared human understanding and semantic information – not just in some physical diagram.

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We need to present stable, local structures that help to reduce uncertainty at the moment of interaction. If we don’t, the information flow breaks and we aren’t reducing uncertainty; instead, we create confusion and disappointing user interactions with our digital products. Making sure we present information correctly is important (and difficult!) for the success of our products – and for better or worse, it’s the work of information architecture! 

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