Data visualisation for a meaningful experience.

Mandrila Biswas
6 min readFeb 26, 2024

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Data visualisation is essentially an abstraction of complex data sets that may help the consumer of the visualisation undergo an informed adaption or simply gain more knowledge. Organising data in various combinations of arrays may help in understanding its implications differently. This has been explained in an example below. There are complex data sets floating all around us and how we understand them depends on the ability to perceive them and connect them to our existing knowledge base.

Data visualisation in nature

Figure 1. How to know the age of a tree. Source: https://brockleytree.com
Figure 1. How to know the age of a tree. Source: https://brockleytree.com

Quantitative data visualisation exists in nature. The cross-section of a tree trunk reveals the age of the tree and the nutritional conditions or stress that it has gone through in the past. The growth rate would have been less in unfavourable climate and is indicated by a narrower concentric band. As seen in figure 1, wide rings may indicate a wet-season or the death of neighbouring vegetation, permitting rapid growth. By counting the dark rings one can know the age of the tree.

Figure 2. Sheep horn. Source: Beaty Biodiversity Museum, Vancouver, British Columbia, Museum Collection
Figure 2. Sheep horn. Source: Beaty Biodiversity Museum, Vancouver, British Columbia, Museum Collection

A sheep’s horns provide clues to its age. Horns are permanently attached to the frontal bones of the skull and continue to grow throughout the animals life. As shown in figure 2, like a tree a new ring forms each year and can be counted to determine the age of the sheep.

Figure 3. Abalones. Source: Beaty Biodiversity Museum, Vancouver, British Columbia, Museum Collection
Figure 3. Abalones. Source: Beaty Biodiversity Museum, Vancouver, British Columbia, Museum Collection

Abalones are flattened sea snails that add new material to the edge of the shell as they grow. In figure 3, one can see distinct layers of varied colours on the shell indicating the nature of the surrounding soil and water quality in the past.

Difference between data and information

The objective value of an entity that expresses its properties is data. Probably that’s why the character of Mr. Data of Star Trek is so objective and analytical. Information is a subjective value of an entity that renders more meaning to the data based on the context and purpose for which it is being used. For example, the data may indicate the temperature value of a heating instrument is 300C, however, a further information to this may convey whether it is hot enough to compress carbon to diamond or enough to liquify frozen coconut oil. Hence a relative representation of the data provides the necessary information for users to understand deviations from a standard goal. It helps in creating the strategy to reach the desirable goal.

Case study: Healthy 365 app (Singapore)

A data point is meaningful when compared against another

The Healthy365 government application enables users to sync their activities through trackers and earn Healthpoints that can be redeemed at merchant outlets for healthy purchases. The goal of the product is to reinforce healthy habits with healthy rewards and aiding users to initiate an active lifestyle and be consistent within their comfort zone.

Figure 4. Option A (older version) and Option B (newer version).
Figure 4. Option A (older version) and Option B (newer version).

Figure 4, shows an earlier version (Option A) of the challenge progress screen and a later version (Option B). The screen can be divided into 2 zones. The upper zone that shows the progress data collates the number of steps clocked against the reference of milestone reached. The lower zone provides progress insight for the purpose of motivating user groups to continue with their active lifestyle and earn rewards.

The new design solved the following problems that were present in the older version:

  1. Unable to understand two progressive bars under “Today” and “Total Challenge Progress” cards.
  2. Progress insight is the same for all progress stages for all users and doesn’t offer meaningful insight.
  3. Not inclusive of a wide range of user groups based on their lifestyle

What kind of comparison is more meaningful?

A usability test on Singaporeans revealed the preference for the kind of comparative progress insight:

  1. National average — may not be optimum as it depends on participation. Eg. “You have a daily average of 3500 steps. This is 50% less than the national daily average of 7000 steps.”
  2. Best performer of same age group — may work for competitive mindset. Eg. “You have a daily average of 3500 steps. The highest score in age group 30–40 years is 7000 steps in a day.”
  3. Today vs yesterday — helps to plan for tomorrow. Eg. Easily comparable progress data of Today and Yesterday. “Today — 3000 steps. Yesterday — 1750 steps.”
  4. Smaller achievable milestones — initiates the momentum for habit creation. Eg. Incentivise initial actions by giving more Healthpoints in the beginning and nudge to progress with consistency. “80 bonus Healthpoints for first 1000 steps completed in a day.”

The options 3 and 4 from above were preferred more due to their achievable and intrinsic nature.

Data summary should present its value and implications

Goal of the data summary was to provide progress insights to help users:

  • Aim for a comfortable consistency
  • Aim for consistent improvement
  • Not feel demotivated when not able to hit the aim
  • Be informed of the reward achieved
  • Be informed about achievable rewards and be motivated to act towards it
Figure 5. Motivational nudge cards.
Figure 5. Motivational nudge cards.
Figure 6. Progress insight logic.
Figure 6. Progress insight logic.

This was achieved by implementing appropriate backend logic and providing meaningful insights to users with various adoption behaviour, as demonstrated in figures 5 and 6.

For a holistic product meant to be used by the public, the user group and needs are varied. This often requires various formats of data to be provided for the geeks, reward-seekers, and trendsetters. This was made possible by different user flows with the governing goal of motivating users to pursue an active and healthy lifestyle. This can be seen in figure 7 below.

Figure 7. Progress visualisation for various user groups.

For the geeks, detailed activity history depicted by bar graphs in daily, weekly and monthly formats provide an in-depth overview. The reward-seekers can view clear reward information and motivational nudges as a comparative progress achieved in relation to yesterday’s performance. The trendsetters may view their progress in terms of bronze, silver and gold tier levels and also collect milestone badges that are sharable to influence social circle.

Ancient methods

Throughout history, civilizations have strived to capture valuable data to combat the greatest fear of extinction during their times; a meteor shower, a flood or a fire. A wonderful insight to such endeavours since pre-historic times has been documented by journalist Graham Hancock who travels the globe hunting for evidence of mysterious, lost civilizations dating back to the last Ice Age. Ancient Apocalypse, the 8-episode controversial docu-series on Netflix is a visual treat for the adventurous audience. The common thread connecting all the episodes demonstrates how ancient civilizations captured planetary data to predict apocalyptic conditions and be better prepared for it. It demonstrates how the data source and value shifts over centuries and mathematicians re-organise their direction and methods according to these natural changes to be more aligned with stars, planets, and satellites.

Figure 8. Serpent Mound, Ohio.

Figure 8 shows the Serpent Ground in Ohio that is essentially a pre-historic calendar to mark the four seasons, view the sunrise and sunset during equinoxes and solstices and that act as a compass as well. The data points hold great cultural significance to the makers.

As we have moved from Agrarian to Industrial to Service to currently an Experience Economy primarily controlled by abundance, the purpose of data visualisation still serves a similar purpose in each age: to analyse values that help resolve the most pressing concerns of the era.

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Mandrila Biswas
Mandrila Biswas

Written by Mandrila Biswas

Delving into experiences, consciousness and intelligence

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