March 6, 2023
4 min

Navigating the Complexities of Information Architecture vs Data Architecture

Thanks to an ever-growing digital world, businesses are spoiled for information and data. The more complex the business, the more information there is and the more complicated the business requirements are. But where there are challenges, there is opportunity. That’s where information architecture and data architecture come in.

Information and data architecture both seek to make sense of the plethora of information a business handles. However, the two have different roles to play in the way businesses use, move, maintain, and present data - both to internal and external stakeholders. So what are they and why should businesses take note?

Defining Information Architecture 🗺️

Information architecture is the structure used to organize and label content on websites, mobile applications and other digital environments. Its primary purpose is to enhance user experience by ensuring information is structured in an accessible, usable and relevant way.

Information architecture seeks to understand user needs and goals by analyzing both existing and required information, then building an information framework in a logical and user-friendly way. It deals with three main components:

  • Labels: How information is represented
  • Navigation: How users make their way through the information
  • Search: How users look for information

Whilst this information sits in the background, it’s the layer upon which you build the design of your digital products.

Information architects bring data from file systems and databases to life by building meaningful narratives and stories. Outputs can include site-mapping, information architecture diagrams and content inventories. These outputs are supported by user research techniques such as card sorting, tree testing, user surveys and first-click testing.

Defining Data Architecture 💻

Data architecture bridges the gap between business needs, goals, and system requirements related to data handling. It sets out a framework for managing data assets, the flow of data and the maintenance of data systems. As such, it has a slightly more macro view than information architecture and concerns itself with emerging technologies such as artificial intelligence, machine learning, and blockchain.

Where information architecture centers around the end-user interaction, data architecture centers around practical handling and operation of data processes i.e. collection through to transformation, distribution, and consumption. Because of this, data architecture must take into account the businesses ability to scale operations, integrate with third party systems, support real-time data processes and the reduction of operating costs. Modern data architecture may point to artificial intelligence to tackle some of these challenges.

The Importance of Enterprise Architects in Information and Data Architecture 🏗

Enterprise architects are big-picture people. Data architecture and information architecture both fall within their remit, and they often oversee other data management job specialities within an IT department.

As a leader (and often, visionary) within a business, enterprise architects shoulder the responsibility of ‘mission critical’ projects. As a result, they tend to have several years experience with IT systems, backed by a bachelor’s degree in computer science, IT management, data science or similar. Many will hold a master’s degree and specialty certifications.

The role involves collaborating with senior business leaders, solution-delivery teams and external stakeholders, and requires creative problem solving and excellent communication skills. Therefore, enterprise architects very much steer the ship when it comes to information and data architecture. Combining high-level business strategy with knowledge of ‘the nuts and bolts’ of IT data systems and processes, they command an annual salary in New Zealand between $150,000 and $200,000 per annum..

Continuous improvement within any business that has substantial IT infrastructure calls for serious investment in enterprise architecture.

Designing and Implementing an Effective Information and Data Architecture 𝞹📈🧠📚

Once overarching business goals are aligned with the scope of data and system requirements, information and data architecture design (or redesign) can begin.

Crucial to the design and implementation process is developing an architecture framework. This is a set of guidelines that lays out principles, practices, tools and approaches required to complete the design. It supports system design decisions, assigns key tasks and provides project guidance throughout the design process. The framework essentially aims to unite disparate teams and maintain business and IT alignment.

The choice of architecture design is also critical. It should consider scalability, performance, maintainability and adaptability to emerging technology. Which is why cloud platforms feature so heavily in modern data architecture. Cloud architects will navigate the architecture design and technical requirements of cloud-based delivery models, which offer the solution to those scalability and adaptability challenges. They are responsible for bridging the gaps between complex business problems and solutions in the cloud. Modern data architecture tends to involve some form of cloud delivery component.

Throughout implementation, data and information architects will work closely with designers and engineers until testable architecture is ready. User research and testing will be carried out, and a feedback loop will commence until requirements are met. Users, as always, should be at the center of your digital product.

Summing Up the Complexities of Information and Data Architecture 🧮

Whilst the difference between information and data architecture can appear nuanced on the surface, they hold unique roles when delivering a cohesive, user-friendly digital product.

Think of a sliding scale where business operations sit at one end, and users sit at the other. Data architecture addresses challenges closer to the business: aligning business requirements and goals with how data flows through the system. On the other hand, information architecture addresses the challenges related to how this data is organized and interpreted for the end user.

At the end of the day, both information and data architecture need to work in harmony to satisfy the user and the business.

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Kat King: Where is the Information?

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.

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.

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.

Kat King bio 🎤

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.

Contact Details:

Email: Katalogofchaos@gmail.com

Where is the information? 📍🗺️

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.

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.”

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? 

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.

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.

Information categories 🧺🧺🧺

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”.

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

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.

  • 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”.

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.

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

  • 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.

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

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

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.

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

Why it matters 👀

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.

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. 

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. 

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).

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.

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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Why information architecture is important for designers

Sitting inside any beautifully crafted and designed digital product, there must be a fully functional and considered information architecture.

As much as information architecture shouldn’t be developed in a vacuum. Neither should the design and look of digital products. In fact, a large proportion of the function of digital designers is devoted to supporting users locating content they need and driving them towards content that the product owners want them to find.

Incorporating visual markers to make sure that certain content is distinct from the rest or creating layers that demonstrate the diverse content on a product.

If you do not have quality content, it is impossible to design a quality digital product. It all comes back to creating a user experience that makes sense and is designed to make task completion simple. And this relates back to designing the product with the content planned for it in mind.

8 Principles of information architecture, according to Dan Brown 🏗️

As a designer, the more you know about information architecture, the better the products you design will meet your user requirements and deliver what they need. If you work with an information architect, even better. If you’re still learning about information architecture the 8 Principles according to Dan Brown is a great place to begin.

If you haven’t come across Dan Brown yet, you have more than likely come across his 8 principles. Dan Brown is one of the UX world's most prolific experts with a career that spans most areas of UX designs. He’s written 3 books on the subject and experience across a multitude of high profile projects. Aiding large organizations to make the most of their user experience.

  1. The principle of objects: Content should be treated as a living, breathing thing. It has lifecycles, behaviors, and attributes.
  2. The principle of choices: Less is more. Keep the number of choices to a minimum.
  3. The principle of disclosure: Show a preview of information that will help users understand what kind of information is hidden if they dig deeper.
  4. The principle of examples: Show examples of content when describing the content of the categories.
  5. The principle of front doors: Assume that at least 50% of users will use a different entry point than the home page.
  6. The principle of multiple classifications: Offer users several different classification schemes to browse the site’s content.
  7. The principle of focused navigation: Keep navigation simple and never mix different things.
  8. The principle of growth: Assume that the content on the website will grow. Make sure the website is scalable.

It’s highly likely that you’ve already used some, or all, of these IA principles in your designs. Don’t be shy about mastering them, or at the very least be familiar. They can only help you become a better user experience designer.

Wrap up 🌯

Mastering the 8 principles, according to IA expert Dan Brown will see you mastering the complex tasks of information architecture. Understanding IA is key to creating digital designs with a content structure that is functional, logical and just what your users need to navigate your product. Design without good IA doesn’t work as well, just as a content structure without a well designed interface will not engage users.

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How to develop a taxonomy for your information architecture

When I first heard the word ‘taxonomy’, I had no idea what it meant. I remember enthusiastically nodding my head at my boss about how awesome it is while frantically Googling it under the table. We’ve all been there early on in our careers. Although, what I found left me feeling even more confused — something about classifying animals? Whether you’re as confused as I was all those years ago or just in need of a refresher, this quick guide to all things taxonomy will sort you out.

What is a taxonomy in information architecture?

In information architecture, taxonomy refers to how information is grouped, classified and labeled within a shared information environment. The overarching structure of that shared information environment is the information architecture (IA) and we find our way around it using the navigation. Think of an IA as a house. The taxonomy determines which pieces of furniture belong in each room and we navigate around the house via doorways and hallways. It all fits together to create one shared environment.

For a website architecture example, think of an online shoe store. The shoes might be organized and labelled by color, size, style, season or collection — that’s the taxonomy. The overall picture of where those groups of shoes live is the IA and in our pursuit of new shoes, we might navigate that structure via a navigation bar at the top of the page. In the amazing Grand Taxonomy of Rap Names visualization below, we can see how the information is categorized, connected and labeled through the lines and the colors. There’s no structure or hierarchy to it yet; that would be the next step in the process to build the IA.

A taxonomy visualization of rapper names
Source:http://hiphopmakers.com/grand-taxonomy-of-rap-names

Creating a taxonomy

There are so many different ways to carve information up into a taxonomy and the key drivers for determining that are your content and, of course, your users. Your taxonomy needs to make sense to your users.You may be starting from scratch with a new website or you may have inherited a taxonomy that for whatever reason just isn’t fit for purpose. The first step when creating an initial taxonomy is to do a comprehensive audit of your content. Ask yourself, is your content relevant? Is it up-to-date? Is it all necessary? Are there opportunities to delete or condense content? Once you have your content sorted, you’re ready to move on to the next step of running a card sort with users.Running a card sort early in your taxonomy creation process will allow you to build it up from an evidence based foundation. There’s no point guessing then testing and potentially going back to square one, when you can co-create with your users and then test that informed approach to validate and further evolve your thinking.When you’re designing your card sort, you’ll need to decide if you’re going to do an open, closed or hybrid sort. Here’s a very high level look at what each type involves:

  • Open: participants sort cards into groups and name their own categories
  • Closed: participants sort cards into categories determined by you
  • Hybrid: participants sort cards into categories determined by you AND they can also make up their own.

This early in the taxonomy creation process, it’s best to start out with an open card sort. Not only will this tell you how your users expect your content to be grouped, but will also provide insight into the language and labels that they would expect that content to be associated with. You never know, an open card sort may even surface something you hadn’t considered. At this stage of the process, it’s important to be open to ideas and new possibilities and an open card sort will do just that.Once you’ve settled on the type of card sort you’ll be running, you’ll need to test which can be done through a tool such as Optimal Workshop’s OptimalSort. OptimalSort enables you to run unmoderated card sorts remotely (or print out cards for a moderated/in-person card sort!). After your participants have completed your card sort, you can access the benefits of OptimalSort’s powerful result analysis functions.

Learn more about running a card sort and more through our 101 guide.

After you’ve run your initial open card sort with users, you should have everything you need to create the first iteration of your taxonomy. Consider everything you learned during the card sort and cross reference that with your business goals and any tech constraints you might be facing. Don’t stress too much about nailing it this time around — remember this is the first iteration and as you test more and learn more, you can make changes. Build out your taxonomy in Post-it notes with a team and then whack it into a spreadsheet to make future testing and iteration activities easier.

How to test a taxonomy

Now that you have the first iteration of your taxonomy, it’s time to have a go at structuring those groups into an IA and running a tree test. A tree test works like a card sort but in reverse — it allows you to test your thinking by working backwards. Optimal Workshop’s Treejack is an online tree testing tool that helps you assess the findability of your content without any visual design elements. All you need are clear objectives for what you’d like to learn more about and a spreadsheet version of your draft IA (told you it would come in handy! ).

Learn more about Treejack and tree testing through our equally handy 101 guide.

Another way to test your taxonomy thinking is to run another card sort. However this time, a hybrid or a closed card sort might be more suitable. A closed card sort would be useful if you’ve got evidence to suggest that your group labels are making sense to users but you’re not 100% sure what belongs in each group. A hybrid sort will let you go one step further and tell you if your content does in fact fit within those labels and if not you’ll also pick up some new ideas to iterate your taxonomy further.

Developing a taxonomy is like any other design process. Bring users into your process as early as you can and never stop iterating, improving and learning.Oh, and about those animals — I wasn’t entirely wrong. The way we classify animals (e.g., vertebrates and invertebrates) is a taxonomy. There are taxonomies everywhere and they’re not all digital. From libraries to supermarkets, we are immersed in taxonomies. It's the role of information architects to determine how these taxonomies are presented to us and how we navigate through them — the possibilities are truly endless!

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