5 Hat Racks Principle: Organize & Present Your Research Insights
Richard Saul Wurman was an American architect and designer that coined the 5 Hat Racks Principle, or L.A.T.C.H., in his book, ‘Information Anxiety’ (1989). The book provides guidance on how to organize your information to make it more digestible for you or your users. If you haven’t read the book, I suggest you do. It’s relevant even in our personal lives for curing the uneasiness of daily information we see online; overwhelming us with facts and data and pretending to be useful information.
“Information may be infinite, however…The organization of information is finite as it can only be organized by LATCH: Location, Alphabet, Time, Category, or Hierarchy.” — Richard Saul Wurman, (1996)
Wurman concluded that all organizational schemas can be reduced down to just five. The acronym L.A.T.C.H. helps to remember the 5 hat racks representing the five schemas. While these 5 schemas are useful in organizing your information to users, it can also guide us in the appropriate methods for presenting our research insights to our stakeholders. Let’s take a look at each of the 5 areas and the most common ways to present research insights.
Location
Heat Maps
I am sure we have all bookmarked heat maps similar to the one above within the last year to see were COVID-19 outbreaks or unemployment rates across the country. Heat maps are always a common way to show data comparison between locations such as states or cities, but can also compare location of content on a site.
Scatter Plot
A scatter plot is a set of data points using x,y coordinates along a regression line to help identify patterns that may occur. When it comes to location, a scatter plot shows location of data on a regression line rather than data between physical locations. Scatter plots can be used to help identify any outliers within your research. Outliers are a set of data furthest away from the regression line. The example below provides a great visual into seeing the outliers (Africa) within the data set. While most of the data follows a linear correlation between the higher the GDP, the higher the life expectancy. However, the life expectancy of Africa caps around 60 years old even when GDP increases. Thus, creating outliers (blue dots around 10k).
Alphabetical
Tables
A long list of information within table can get overwhelming very quickly when presenting to stakeholders. To make your information tables easier for scanning, try ordering your primary index or unique identifier alphabetically. In the example below, the name of the country is unique identifier. This table is alphabetized by country to help scan for a specific country more efficiently.
Charts
In addition to a list within a table, a list within charts can be organized for easier chart legibility. The same countries from the example above are used within the chart below to visualize the data within the PS.2 column in a different way. Again, the alphabetical order helps when scanning for insights on a specific country.
Time
Timelines
Similar to the way data can be alphabetized within a bar graph, timelines are commonly used to show data within a specific time period. If you have ever used a data program, such as, Google Analytics, Tableau, or PowerBI then you will often see dashboards filled with timelines showing data within a specific time period. A timeline commonly captures numerical values of events that occurred, ordered by time.
Timeframes
Timeframes are similar to timelines in that they both show a linear view of events that occurred, ordered by time. The difference with a timeframe is that it shows insights of a specific period within a timeline. Timeframes also tend to only provide the most relevant insights to your research. You can see from the example below the timeframes of repeat orders per person within a specific timeline.
Bonus: notice the list of users names are in alphabetical order as well ;).
Categorical
Affinity Diagram/S.W.O.T. Analysis
Commonly used among showing qualitative insights, an affinity diagram and S.W.O.T. Analysis is used to present insights by grouping together similar quotes, actions, or themes. It is a high level approach to understanding patterns between themes or within a specific theme.
A S.W.O.T. analysis is a type of affinity diagram commonly used within market research or competitive research. It is used to show high-level insight of how other (competitor) brands/products compare to your brand product. Unlike an affinity diagram, the themes of a S.W.O.T. analysis do not change. The insights you gather must be organized into 4 different themes; Strengths, Weaknesses, Opportunities, and Threats. Themes within an affinity diagram can change.
Stacked Columns
This type of chart is often useful when showing numerical volume of categories within categories. Unlike affinity diagrams, stacked columns can provide more quantifiable insight between themes and volume of themes within themes.
Card Sorting
I did want to call out that card sorting is a great method for organizing information by category. The reason I am not goin into detail is because you typically do not present a card sort as is. Card sorting is a method to gather insights and then you typically present your findings using another method (like a stacked columns chart).
Hierarchal
Customer Journey Map
If you are thinking, ‘wait a minute…doesn’t a customer journey map belong more as a categorical schema rather than hierarchal?’ — then you may want to keep reading.
While a customer journey map does group a sequence of events within specific stages (categories) of the journey map, the purpose of the map is not to show the stages, but the sequence of events. What differentiates a customer journey map is the linear (step-by-step) process that does not exist in categorical schemas.
‘So…could a customer journey map also be a timeline?’
No. The process of a customer journey could occur at anytime, which means that we cannot order insights within a customer journey map by time.
The reason for using a customer journey map as a hierarchal schema to organize and present data is because one event must follow the other. A customer cannot get to the “buy stage” without having to go through some sort of “attention stage” or “consideration stage” first. This is the same whether you are presenting insights from a customer’s journey or a user’s journey (ex: a user has to login before doing anything else).
Tree Test/Site Mapping
Again, like the category schema, tree tests use categories to help organize information. Unlike card sorting, tree tests or sitemaps have a system of hierarchy that must be followed. An example may be if a user is shopping for a lounge chair for their living room, they would most likely have to filter through a hierarchy like, Furniture >Living room>Chairs>Lounge Chairs.
Tree tests tend to be used when there is already an existing hierarchy, such as updating an existing website menu. A card sort is used more at the discovery phase of your research. Also unlike card sorting, you do typically present your final insights as either a sitemap or hierarchal tree.
I want to hear from you. What are some other common methods you use for presenting research insights using the 5 organizational schemas?
