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.
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!
According to Abby Covert, author, teacher and community leader in the field of information architecture (IA) : ‘IA is the way we arrange pieces of content to make sense when experienced as a whole. By this definition: there is information architecture in everything. Mindblown?’
😮 (That’s a resounding yes from an IA rookie like me.)
In this issue of CRUX we go in search of information architecture and have some surprising encounters in the worlds of emoji, elevators, walking tracks, games and more. We meet UX designers, developers, researchers and even a Senior Park Ranger. It seems you can have your ‘IA hat’ on without even realising it. That’s the power of information architecture.
Some highlights from this issue:
UX research and strategy specialist, Q Walker explores the world of emoji from an information architecture perspective and sparks curiosity along the way. Have you ever wondered why clock emojis are organized under travel and places? 🤔
We talk to Senior Park Ranger David Rogers about the thinking behind creating and building New Zealand’s national walking track network and reveal how information architecture can also live ‘in the wild’.
UX developer Ben Chapman delves into the classification systems of libraries over time and ponders the pros and cons for users of moving information online.
We talk to Sam Cope, Lead UX Designer at Wētā Workshop’s Interactive division about what it takes to drive design decisions with the end user in mind - something crucial for any video game’s success, whether it’s a blockbuster or something more niche.
And much more about information architecture in unexpected places….
The stories and people in this issue of CRUX certainly captured our imagination and attention - we hope they inspire and even surprise you too.
Do you have a burning idea to share or a conversation you’re dying to kickstart that’s of interest to the world of UX? Now’s your chance. We’re already on the lookout for contributors for our next edition of CRUX for 2022. To find out more please drop us a line.
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.
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!
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.
