How to Make Technical Data Simple and Beautiful

How to Make Technical Data Simple and Beautiful

Written by:
Amanda Wilks

Nov 18, 2016
how to communicate technical information

Data is the supportive brick for any presentation or report out there. It offers bits of reality that together form an accurate image. 

Without data, marketers would create their campaigns on assumptions and suppositions, instead of knowing exactly what their clients want. Not to mention engineers would construct only hazardous buildings from the beginning. Even consumers need data to know how to use their new products accordingly.

Despite its ubiquitous nature, technical data is still seen as cold and flat. Here are a few tips on how to communicate technical information simply and beautifully through content and design.

RELATED: Expert Tips on How to Grab Attention With Your Data Visualizations


Technical Data Needs a Visual Upgrade


There are four types of learning styles, which are visual, auditory, read-write, and kinesthetic. This means that we all understand information in different ways. People usually have one predominant style, but they need the other three too to assimilate a complex image of the new content.

Visual data can combine almost all learning styles to obtain a comprehensive scheme. A 1998 study discovered that people have the capacity to remember images better than words. That is because people should read one line at a time. On the other hand, images can be comprehended with just a glance. Writers need entire pages to describe a scene. However, one image can contain all details at the same time.

humans process information 60,000 times faster than text

Visual data is not all about images. It is about finding a way to connect two elements that in a text would appear unrelated. Once the information is clearly categorized, the visual elements can come in to represent pieces of content. Once each of them receives a form of its own, it is easy to work with them.

Data interpreters can create scenarios true to life and use geographical orientation to find a connection between two seemingly foreign elements. Geographical orientation will enable you to find a link between two points exactly like people do in geometry. Texts cannot give you a spatial interpretation, and data can remain hidden behind context.

For example, architects use data visualization to assign each real element with a visual interpretation. Instead of the word “wall,” they draw a line. Instead of using the word “window,” they draw a second line against a wall. Each element receives its own interpretation in the world of design and becomes a symbol.

By reducing complex images to simple lines and dots, architects can easily discover certain connections. So, the job of an architect does not only require design skills, but also extensive problem solving capabilities, and we all know how much architects make.


How to Communicate Technical Information

convert technical information into visual data

People learn through assimilation. We take a completely unknown notion and place it in its rightful location in this world. We need to understand its meaning and its relationship with the things and notions we already know. For example, when we learn a new language, we assimilate each word with an object or phenomenon from reality.

The more concise and clear our lessons are, the more quickly we learn them. Our human nature demands us to find a logic or reason behind the unknown. Therefore, the prehistoric era found the cause of strange natural phenomena like thunder or rain as the power of gods. We all know that behind these effects is science and we learned its connection to weather.

So, to create an easily digestible technical data representation, we need structure. The new information must follow a logical course. Without this organization, there are going to be just some random numbers and phrases.

how to organize technical information

To create simplicity, your technical data needs organization. People need a logical beginning, middle, and end to understand and grasp new notions. Thus, any clear visual data should respect one of the five logical structures:

  • Location;
  • Alphabet;
  • Chronology;
  • Category;
  • Hierarchy.

Without these structures, there would be a compact mass of infinite data. The world functions on complex rules and equations, and one lifetime is not enough to master them all.

But people don’t need to know everything. By structuring a precise topic in a logical structure, you deliver meaning to their subjects of interest.


How to Turn Simplicity into Effectiveness

Simplicity and structure are not enough to support effective visual data by themselves. They need the help of a common goal. What’s the purpose of the report? To what problem does the data provide a solution? Thus, any technical content should work towards one or several of the following functions:

  • Display data in a visual structure;
  • Use graphic design only as a means to an end, and that of understanding the substance of data;
graphic design as a means to communicate data simply
  • Avoid misinterpretations that can appear after an unfortunate combination of phrases in plain texts; when you use data instead of text, you evade the contextual nature of words that can lead to multiple interpretations;
  • Provide a multitude of important numbers that are about the subject of the graphic;
  • Provide clarity to large data sets;
  • Enable users to conduct comparisons between graphical or numerical elements;
  • Activate several levels of overviews, from broad descriptions to in-depth segments of a field;
  • Respect one of the following concise purposes:
    • Decoration (structuring data in a pleasant design to make it easier to digest);
    • Tabulation (classifying data into a tabular form);
    • Description (outlines the context and source of data) or exploration (the act of forming analysis based on gathered data;
  • Integrate statistics and verbal descriptions into graphics.
integrate statistics verbal descriptions into graphics


Types of Data Visualizations

Now that we've covered the importance of simplicity in communicating technical information, it is time to integrate data visualization into beautifully designed graphics. There are many ways to bestow data with visual elements and meaning. The following is a complex list of visualization types that can structure data in logical and accurate graphics.


1. 1D/ Linear

types of data visualizations linear

This single dimensional structure can be defined by what we know of lists of items. While this is the most used type of visual, it certainly lacks depth. The viewers are unable to perform logical correlations, and the connections between its items remain hidden behind the absence of multidimensional agents.


2. 2D / Planar

This is a comprehensive type of graphics that relies on geographical interpretations. Its main aim is to synthesize the data that describe a real area such as a country, city, or neighborhood. There are several ways to unlock the potential of planar visualization that can even lead to interactive maps.

  • Choropleth: this is a map that has its areas of interest categorized in colors or patterns. Its goal is to offer an overall view of how a certain feature like population density is different from area to area.
  • Cartogram: this type of map substitutes land area or distance for certain variables. For example, the size of districts can be altered to show how many citizens there are in contrast with other districts.
  • Dot Distribution Map: this map representation uses dots to signal the presence of certain factors within a certain area. One dot represents the location of the phenomenon that data recorded. Thus, it is convenient to observe the impact of a certain agent in an entire country.
  • Proportional Symbol Map: as with the cartogram style, the proportional symbols have the mission to display the right representation of a phenomenon at its precise location on the map. The symbols can change their sizes according to their quantity.
  • Contour Map: this is a map that uses curves with different functions to represent data.


3. 3D/ Volumetric

This type of visualization offers extensive methods to represent the data recorded in three dimensions, which are length, width, and height.

This is the perfect visualization style that enables viewers to understand connections in space. It is easy to find unique angles and combinations of data elements that would have remained otherwise unseen in 2D or 1D representations.


4. Temporal

  • Timeline: the timeline respects the chronological laws. The logical development of data respects the line of time, which enables viewers to understand the evolution of data.
  • Time Series: this is a visual list where data elements receive their own dot in the dimension of time or space. By connecting the dots between them in chronological order, you obtain a graphic that depicts the ups and downs of a certain field. Based on time series, experts have enough data to forecast the future of the field in an accurate manner.
  • Gantt Chart: it is actually a bar chart that focuses on the project schedule. There is one predetermined period of time, where the work activities represented by graphics appear at the precise moment they took place. Gantt Chart is used to record the project management effectiveness among other useful functions.
  • Streamgraph: the streamgraph has a central axis that is usually the time line. Across this axis flows some variables recorded in a certain field. One of the most popular streamgraphs is the one created by New York Times which served the purpose of comparing movie box office revenues.
  • Sankey Diagram: the data is represented by arrows that change their size according to their impact level.
  • Alluvial Diagram: this visualization technique uses the timeline as an axis and horizontal lines that change their trajectory according to the development history of a system.


5. Multidimensional

The most complex data visualization offers the most comprehensive solutions. It is difficult to remain relevant in the realm of multi-dimensions. However, the end result will turn complicated structures into simple graphics.

From here on, it is quite simple to relate the elements between them and understand how they work together as a whole.

  • Pie Chart: this is a circular shape with multiple slices. Each segment represents a variable. This graphic simplifies the quantitative proportions between several numerical factors.
  • Histogram: describes the estimation of the probability distributions. There are two axes that usually represent time and quantity.
  • Tag cloud: this is used to highlight keyword metadata. These units are displayed randomly. The bigger their size is, the more important they are.
  • Bubble Cloud: resembles the tag cloud. However, each term has its own bubble in this case. But their importance is still measured by the size of the bubbles.
  • Tree Map: follows the hierarchical rules. Each element receives its own rectangle that can have its own smaller rectangles as sub-branches. In the tree map, the creators can use colors to mark a certain resemblance between several branches. The size of the rectangles symbolizes its dimensions or success in relation with the others.
  • Scatter Plot: uses Cartesian coordinates to place certain variables in their rightful location according to their values. The end result looks like a collection of points that represent the graphical representation of data.
  • Area Chart: represents a true to reality graphic of quantitative data. With this type, you can compare several line charts to measure the differences between them. Business people use them to record the most popular trends in a field.
  • Heat Maps: this is a matrix where the variables receive a certain color. The color coding is precise. For example, red means high activity, while yellow represents a lower record.
  • Parallel Coordinates: this visualization technique employs high-dimensional geometry to represent data through parallel lines that depend on several vertical lines. People use them for air traffic control, data mining, computer vision, and optimization.

All in all, the effectiveness of technical data depends on simplicity and visual representation. Simplicity means structuring data in a certain manner so that it serves one single goal.

Beauty and visual representations are vital to encourage viewers to understand the system described by numbers and other records. Once the users are educated on a certain topic, the data would have served its purpose entirely.


Your Turn

If you're wondering whether it takes advanced expertise--and even a data science degree--to create these kinds of data visualizations, there are also plenty of easy-to-use information design tools for beginners that can help you create interactive charts, graphs and infographics within minutes.

One option you can try is Visme, an all-in-one visual content creation tool. Click here to try it for free.

And if you have any information design tips and advice of your own on how to communicate technical information to non-technical audiences, don't hesitate to drop us a line in the comments section below.

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    About the Author

    Amanda Wilks is a Boston University graduate and a Digital Marketing Strategist. She has a great interest in everything related to content marketing, online marketing and corporate and personal branding.

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