Richard Saul Wurman has been called the father of information architecture. And properly so given that he not only coined the term, but also came up with – among many other things – the acronym of LATCH to describe some core information organization possibilities. So it’s with some trepidation I dare to suggest extending his model. And yet, with time has come our collective experience of dealing with more varied forms and volumes of information via digital channels for which the base model seems dated.
TL;DR version: The LATCH model of information organization includes the following aspects: Location, Alphabet, Time, Category and Hierarchy. It is, however, missing at least the following: Ordinal/Numeric, Distance, and Random. As well, the model lacks depth when it comes to faceted metadata and purpose-focused organization schemes. Okay. That’s it. You’re done. Unless you want to really dive in…
Beyond the LATCH Model
The LATCH framework is a great starting point. But it’s not enough. It’s no longer expressive enough across the range of information spaces that information architects and user experience designers will increasingly find themselves. It’s interesting when searching around for information architecture articles and find that a fair amount of people seem to repeat, “There’s only five ways to organize information.” I suppose you could try to stuff more abstract or esoteric methods into the idea of “Category.” But this seems lazy and would result in methods to organize subcomponents that wouldn’t be appropriate for the higher level category.
Over time I’ve come to believe there’s at least two aspects of fairly natural organization that are missing, plus some purpose-focused viewpoints; even if a couple of these may be more like variations on existing themes. The two seemingly more obvious missing elements include Ordinal/Numeric, and Distance. I’d suggest also thinking of “Range” as its own aspect as well, even if it might not strictly be a higher level organizational structure. (Because it likely can’t exist on its own. But nor does it seem ‘facet-like.’ And while strictly speaking, it’s a filter, it’s somewhat different from sets of items as range depends on a scope of values; whereas sets do not.)
Before I try to defend a few positions on some new aspects in addition to LATCH, let’s take another quick look at both Information Architecture in general, and LATCH in particular. Then I’ll advocate for why these other viewpoints belong in the framework – or at least should be added to your thinking as a Digital Product Manager, Information Architect or User Experience Designer. I’ve always thought that it’s often the case our ability to see and understand things depends on the tools with which we choose to observe. In these cases, the tools are the perspectives which we attempt to apply to information spaces. They’ll work – or not – as the cases may be, according to their fitness for task; just like anything else. But if they’re not even on our radar for consideration we risk missing what might otherwise have been the best ways for selected cohorts of users to navigate information spaces. And it’s probably useful to keep in mind that IA structures not only provide general browsing and drill down navigation, but the labels and relationships allowing for at least some disambiguation within search result sets. Whether that’s done with a full on planned out faceted navigation structure or a simpler topic-level filter isn’t the point. The point is all of these tools are potentially available to us for initial information structural design and consideration. If the final goal to provide for our users is something that can be handled by simplistic structures, then great. But if not, it’s our responsibility to know our toolsets.
Let’s Quickly Review Some Information Architecture Basics
Information Architecture (IA) is generally about organizing, structuring, and labeling content such that users, (or perhaps machines), can navigate, seek out and understand information. The structure of information spaces should make it as easy and efficient as possible for users to both find and understand information. And be effective within such searches. Efficiency can be thought of as time to seek such information. And effectiveness as finding the actual information sought, assuming it exists.
IA is critical in both physical and digital spaces. And use of IA tools is, at it’s most basic level, an attempt to help others reach their information gathering needs.
“Doing IA” generally involves the following:
- Research: Both to understand user needs, and the nature of the information sources themselves.
- Analysis: Analyzing data elements themselves to identify components, structure, concepts and relationships between and among them.
- Creating structure: In an attempt at sense making or creating an ‘information scent path’ an IA practitioner will attempt to find one or more structures that best bit the information at hand and also align with users’ search needs.
- Labeling: Labels are generally meaning-bearing, and are used both in defining high-level categories, entities themselves, as well as the smallest attributes of an information entity.
- User Testing. User Observation if possible. More User Testing. More User Observation. Repeat this a few more times at some sensible interval.
When crafting IA solutions, we look at a variety of academic fields. These include best practices in IA itself, psychology and behavioral psychology, possibly physical design, and more. The many tools and considerations for IA my seem overwhelming, but typically, it may take only a few of them to serve useful purposes for a task at hand. Often, we will use several different approaches for organization and storage. We will always need to come back to asking, “What problem are we trying to solve?” We ask this and many others to get at the core needs of our customers. As we learn about our audience, we should also be learning about the context(s) of their needs and how that might impact what we do.
And Now… the Basic LATCH Framework Re-Visited
The LATCH framework is a model for information architecture (IA) that helps us organize and structure our content in whatever media platform necessary for delivery to users. LATCH is an acronym that stands for Location, Alphabet, Time, Category, and Hierarchy.
- Location: Physical or virtual location; a web page, database record, geodetic reference point.
- Alphabetical: Simple alphabetical listings; directories, contact lists, indices.
- Time: Chronological order; calendars, project timelines, event logs.
- Category: Somewhat of a catchall category, this aspect is a study topic in itself, involving grouping and clustering; subjects, topics, related items.
- Hierarchy: Definition of hierarchical relationships; taxonomies, broader relationships, narrower relationships.
These were the original five methods as explained by Wurman.
Let’s Start Extending LATCH – First with Ordinal / Numeric Values
The basic LATCH model is fairly simple and covers an incredible range of possibilities. However, it also appears to have been purpose-built for particular types of challenges Wurman may have faced when he considered building the model. He may have never meant for it to be exhaustive for all information types.
One thing that is missing from the LATCH framework is ordinal values, which refers to the use of a specific order or ranking for organizing content. This could be a top 10 list or a rating system. One benefit of such an organizing principle is that users should be able to see the relative value of information items. (This is only true to a degree. It’s possible that placement upon a numeric continuum could be on a variety of number line systems. E.g., a simple straight number line where a one unit change means one unit, vs. a power law curve or an exponential growth chart. Numeric order has a variety of obvious benefits; perhaps the clearest of which is that we can often calculate relative value. Now, this isn’t always strictly true. If I rate a product with stars from one to five, it doesn’t necessarily mean that going from two to four means I find the product twice as good. And then there’s logarithmic scales. Nevertheless, numeric labels allow for ordinal value, where as other aspects of LATCH really don’t. (OK, possibly Alphabetical, but that’s of course limited in scope.)
Next… We’ll Add Distance to the LATCH Framework
Distance is not included in the LATCH framework for information architecture (IA) but it is an important aspect of IA that can be considered. Distance in the context of information architecture could refer to either the physical or conceptual proximity between different pieces of information. It’s true that “Location” is part of LATCH. And while physical distance may depend on location for a calculation, it’s nonetheless it’s own entity for the purposes of organizing things. (Or rather, I’m suggesting it should be.) And you could also say that once distance is determined, any ordering actually becomes part of an ordinal ranking system. This isn’t really satisfying though, as you would still need to add distance as the context for that ordering for it to have the appropriate meaning.
In a physical sense, a common use larger-scale distance might be defined by planetary geodetic reference systems. Or larger! Aviators, Mariners, Astronomers and a select few more may have found these useful for centuries. And since so many of us have put GPS enabled phones in our pockets now, the rest of us as well; even if only a scant few understand the underlying data sources. (Even those in the field still struggle with how this all works.) More close to home in a typical day-in-the-life of an IA designer means distance can refer to the layout of a website or application, and how closely related items are placed together. For example, placing related items, such as products or articles, close to each other on a web page can help users understand the relationship between them and make it easier to find related information.
So back to talking about distance in the physical sense, we could be referring to the real world actual distance between and among objects. I’m not sure if geospatial relationships between or among objects is a subset of “distance” or vice versa. That is, I think one could argue – and I suppose I am – that a geospatial reference datum is just the coordinate system upon which distance vectors would be plotted. In any case, even though the tools have been around quite awhile, this has actually shown itself to be a newer form of information architecture if you look at messaging or artwork that is now expressing itself in three-dimension forms. (For example, drone art and drone “fireworks” and increasingly in three dimensional environments). Please note I’m not suggesting that 3D design is anything new. We’ve had architecture for millennia, underground mining operations get planned out, air traffic control uses airspace, etc. And if you go into The Nether in Minecraft, you’re fairly deep down. (Well, technically another whole dimension.) The point is, these specialty areas should – my opinion – increasingly be thought of as aspects of general information architecture. Practitioners of IA should have at least general awareness of such things within their general Body of Knowledge.
In any case, we can also consider distance in a conceptual sense, distance can refer to the similarity or relevance of different items of information. For example, grouping similar items together, such as products in the same category, can help users understand the relationship between them and make it easier to find related information. (Which is of course not terribly different than when items as the supermarket are placed together. Except with limited shelf space. As opposed to – for example – a metaverse shopping experience where “shelves” could potentially branch out forever in any direction, circle back on themselves, or whatever.) So here one could argue this is a special flavor or category, however given that distance is a critical mechanism in creating the demarcations, the idea of distance is minimally a required partner to the concept of category.
What About Direction and Specifically Related to Sound Vectors?
Does Direction belong as an idea within Information Architecture? Maybe. Consider Virtual Worlds. From an aural perspective, direction of sound can matter a lot. Designers of modern speakers and sound systems put a great deal of thought into from where sound might emanate and from what direction it may be perceived. In use within VR systems this could be lots of fun to design with for games. Or deadly serious when used as part of weapons systems. What about electric vehicles that put out less sound than typical automobiles? Neither animals nor humans have the same warning upon their approach and so, perhaps somewhat ironically, sound needs to be artificially added back in. While sound may just be a form of energy, it certainly carries information; both in and of itself just existing as a sound, and then as well any specific content. That is, obviously we have voice and music and such which is information bearing, but merely that a sound exists and is coming from… where? That’s information as well. This is hardly that much of a new idea when you consider how many rather old war movies depended on sound for their submarine hunting plots. (Not to mention the more current book/movie Red October, which was awesome.) We can also see sound direction in use in places like spatial correlation of ocean ambient noise. So again, it’s not really as if some of these ideas are terribly new. I am by no means suggesting that they are. But we may be early in thinking about such things as another aspect of an overarching story of information architecture we should have on our collective checklist. So it does seem to belong on the list.
Can You Call “Random” a Means of Organization?
Organizing information randomly can be less effective for users because it does not take into account the needs and goals of the users, and it does not provide a logical or meaningful structure for the information. (Well, actually it might. It’s not really for me to place value judgements as to why someone else might choose an organization structure. Or intentionally destroy one, but there are use cases for random.) Random organization can make it difficult for users to find the information they need, and it can lead to confusion and frustration. I suppose for certain games, from scavenger hunts to treasure hunts to whatever, random is a valid organizational structure so it deserves to be in the checklist for consideration of organizational techniques. (Even if it is kind of an odd special case.)
Generally, random isn’t much appreciated. People often rely on patterns, structure and relationships to understand and make sense of our world. They expect the information to be organized in a way that reflects the relationships between different pieces of information, and that makes it easy to find what they seek. Or even if they’re not in search for anything in particular, at least to have clues and cues for how to navigate wherever they happen to be; physically or virtually. Random organization likely does not provide these cues, making it harder for users to understand the relationships between different pieces of information and to move about towards their goals.
There are some other cases where random organization might be useful, such as when the goal is to surprise the user or to present information in a new and unexpected way. Also, we have the common examples of lotteries and other games of chance. These aren’t so much organized randomly as they are designed to have random output. For that matter, cryptographic wallet seed phrases are structured to be random in a particular kind of way. And then we have research of various sorts from medical to marketing. Here we might want random samples of populations for study. Or, perhaps a known population, but to receive random treatments, (such as questions in different orders), in an effort to avoid bias. Hey… maybe random has enough critical organizational use cases after all and should take its rightful place in the list.
It’s important to keep in mind that organizing information randomly can be seen as a poor user experience and it may result in users leaving a website or application quickly. It’s likely better to have a clear and logical structure that is easy to navigate and understand.
Other Means of Organization
Getting beyond the basics, we have some more in depth ways of considering how information is packaged, presented and consumed. These include both tools and techniques for dealing with information entities themselves, but also consideration of purposes and context for use of information.
Faceted Meta Data and Drill Down including Ranges
Faceted metadata is a method of organizing and structuring information that is used in information architecture (IA). It is based on the idea of breaking down information into small, discrete pieces, or facets, that can be combined in different ways to create different views of the information.
In IA, faceted metadata is often used to create filters or facets that users can use to narrow down their search results or find the information they need more quickly. For example, a website that sells books could use facets such as author, genre, publisher, and release date to help users find books that match their specific interests.
Faceted metadata can also be used to organize and structure content in a way that makes it more discoverable and accessible. For example, a website that provides information about different countries could use facets such as geography, culture, history, and economy to help users find information about a specific country or region.
Another way to use faceted metadata is to create a faceted navigation system that allows users to browse the information by different attributes. This can be especially helpful when the information is vast and diverse, such as in a library, a museum, or a scientific research database.
Faceted metadata can be a powerful tool for organizing and structuring information, but it’s important to note that it’s not a one-size-fits-all solution. The facets and attributes that are used will depend on the specific content and the goals of the website or application. Additionally, it’s important to provide clear labels and definitions for each facet to help users understand the relationships between the different pieces of information and how to use the facets to find what they need.
More About Ranges
Organizing things in information architecture based on ranges is another way to structure information and it can be useful in different scenarios. It can be downright annoying to not have something like Range available in certain scenarios. On a shopping site, for example, there’s usually going to be s Sort option to order by low or highest price, but with a large result set, it’s worth the effort to just cut out everything except that within your target range. One could perhaps argue that Range is just a special instance of Category. Or perhaps merely an attribute of any of the higher level organization models from Location to Hierarchy. But until either I or someone else sorts that out in a sensible way, I’m going to leave it as it’s own thing for now.
One example of range we often see is products or services filtered by price. This helps shoppers quickly identify products or services within their target price range, making it easier for them to find options that fit their budget. Depending on the product or category we have many other traits from size to weight to length to… the list goes on.
Another obvious range-based approach is used to organize information by time. For example, grouping events by their date ranges, making it easy for users to find events that are happening in a specific time frame or to see upcoming events. And certainly just about any decent analytics product is going to allow such filtering.
In general, organizing things based on ranges, (or rather, providing a range filter), is a flexible and effective way to structure information, especially when the information has a specific value, characteristic or attribute that can be measured. It can help users quickly identify and access the information that is most relevant to them.
More Ways to Organize
The LATCH framework and faceted metadata are two examples of ways to organize information, but there are many other ways to organize information as well. Some other ways to organize information are perhaps a bit more abstract. But they may be useful.
- Relationship-based: This approach organizes information based on the relationships between different pieces of information, (or objects), such as connection and possibly connection type, cause-and-effect, similarity, or contrast. This method may be especially useful for situations where there are complex relationships between different pieces of information; such as networks of people. The technique of using mind mapping would fall within this aspect, as would concept maps.
- Process-based: Here we see information laid out based on steps involved in a process, such as a recipe, a workflow, or a set of instructions.
- Geospatial: Here we organize information based on geographic location, which might be highly localized such as in a building, more general such as mining or pipeline maps, geographic or political boundaries or as expansive as the universe. We may also use wholly customized reference datum as well. For example, in virtual worlds. Or perhaps create a center reference point for a drone-based light show.
- Storytelling: This could arguably be similar to timeline. In this case, information is organized in narrative form, according to some sort of plot.
It’s worth noting the obvious: different websites, apps, information spaces may require different ways of organizing information, and often more than one. There is no one-size-fits-all solution. Designers and developers will consider the specific needs of their users and their own goals to choose methods fit for purpose. Or at least they should. If they know the options. When they don’t is when we get hard to use and in some cases even potentially dangerous products and services.
And Back to the Challenge of the Contextual User
Organizing information can be a complex task, and there are many different ways to approach it. It’s always critical to consider the specific context in which the information will be used and by whom. Another challenge is when the same type of information will be used in different ways by different users. (Weather information will be used in different ways by pilots in flight vs. pilots during flight planning, and of course different still for event planners, roofers, and so on. Emergency dispatch information will be used differently by units responding to an emergency vs. those studying public health issues. Doctors treating patients will use diagnostic information differently than insurance companies. The list goes on.)
It’s always important to conduct user research and testing to understand their needs and preferences and under what conditions these needs may change.
Ideally these considerations of extensions to Wurman’s original model can be useful to those who’ve bothered to read this post. Or at the very least, thought provoking. I’ve intentionally not gone down a path of being exhaustively proscriptive as to how or where to use such methods. That would probably be a book length effort. My goal was just to get some of these thoughts out of my head and into the world such as they are for others’ consumption and perhaps use. Hopefully Richard won’t mind me hitchhiking on his early work.
To conclude then, part of the point, as somewhat mentioned earlier, is that these other ‘beyond LATCH’ information spaces do seem to fall under the general concept of Information Architecture. And if one were to create a larger scale Body of Knowledge for the field, these areas – and likely more – should probably be represented. Each of these areas had to by necessity grow into their own methods, languages, representations and so on. But have the practitioners been able to benefit from the cross pollination of ideas that might come from more general theory and practice? It’s just a conjecture on my part, but I believe so. Explicitly considering things like ordinal values, distance (both physical and conceptual), ranges, geospatial relationships and so on, can potentially add value to work being done by information architects. Whenever a practitioner is faced with a problem space, they typically need the same sorts of things. Where am I? Where do I want to go? How can I get there? Having more conceptual frameworks within an IA toolbox can perhaps help sort these things out.
Suggested Additional Reading
Information Foraging Theory: Adaptive Interaction with Information
Information Architecture for the World Wide Web: Designing Large-scale Web Sites
Designing the Search Experience: The Information Architecture of Discovery
The Accidental Taxonomist
Just for Fun – About the Imagery
When I finally get around to writing something, usually it’s heavy on text and doesn’t really need a lot of imagery. But since the topics I’m usually interested in are dry enough, I try to at least break things up with some graphics. For this post, just for fun, I decided to see what might pop out if – instead of seeking out specifically contextual imagery – I just put some keywords or phrases into some AI image generators. To keep the style somewhat consistent, all the graphics for this post came from Craiyon AI Image Generator, except for the bio pic on the right, which uses a filter from nightcafe.studio. (Just because I’ve always been a fan of the Starry Night series and thought I’d try it out.)