In God, we trust. All others must bring data."
- W. Edwards Deming, American Engineer & Statistician.
Visual Statistics was one such brief that I found challenging and interesting once we started analysing the visual data.
Brief:
We will have accumulated a range of statistical information captured in photographic form during the session. Your challenge is to analyse, present and conclude a content and/or sample of information based on the data collated. Each individual 10-minute presentation (maximum) should include your own supporting visual material without using the images captured during the session.
Process:
Accumulation of data - For this particular brief, the data which we had to study was created by us in the classroom session itself. Firstly, we were asked to make a list of categories that comes in our mind, for example, House parties/Club parties, Asian/Western, Art/Design, Visual content/Written content, etc. After making such categories, we divided all these 66 categories into certain areas of the topic. Now, taking these categories as the base of data, all the students were asked to take their stand in each category they belong to or are interested in. All these 66 categories and the answers are given to it by all the 14 students was recorded in photographic form in a class. This is how we made the data for the research of visual statistics.
Short listing the categories - It was unnecessary to study all the categories and connect them. So, I decided to take Sports/No sports as my main subject to study according to my interest concerning the following categories: Expensive/Affordable, Saver/Spender, Heels or Stilletos/Sports or sneaker shoes, Online shoppers/Offline shoppers, Physical games/Video games, Introvert/Extrovert, Leader/Follower, and Creative/Non-creative.
Why did I select these particular categories? - I am passionate about sports, so I decided to study the people interested and not interested in sports. Then, I chose the rest categories listed in point 2 based on people's habits (Personality wise) and the Business sector.
Deriving Statistics - As looking at the photographs, there were a total of 14 students, including me, who provided information for this data development. Of those 14 students, nine were interested in sports, and five were not interested in sports. At first, I studied both sectors: sports enthusiasts and non-sports enthusiasts. However, while my observational research was going on, I realised that I should only focus on the people interested in sports and study their responses in other categories as collective data.
Data Visualisation: I presented data with emotions and photography whilst developing one specific design language to make the data effectively readable and visually interesting for the reader.
Sports Enthusiast - 9/14 people - 64.28%
Not interested in sports - 5/14 people - 35.72%
Rest all the calculated data, and the conclusions are very well showcased in my Visual Statistics presentation.
Research:
Firstly, I studied and analysed the photographs; according to this observational research, I calculated the numerical data to understand the similarities and differences.
Data Flow - a book by Robert Klanten. Data Flow is an up-to-date survey providing cutting-edge aesthetics and inspirational solutions for designers, and at the same time, unlocks a new field of visual codes. By reading Data Flow, I understood the comprehensive examples and possibilities in visualising data and information on a non-linear basis. Through Data Flow, I researched the expansive scope of innovatively designed diagrams range from chart like diagrams, such as bar, plot, line diagrams and spider charts, graphed based diagrams including line matrix, process flow, and molecular diagrams to extremely complex three - dimensional diagrams.
Work of Gabrielle Merite - a scientist-turned- Information designer & Data Illustrator who creates some fantastic data visualisation. I love her style and admire her sense of social justice. https://www.gabriellemerite.com/ After the whole process of analysis, calculating statistical data and coming upon conclusions, I was not much aware of presenting my data visually. Precisely going through Gabrielle Merite's work to understand how to visualise data, I developed my design language to represent my data. Her work reflected the contemporary design approach and made the data more interesting to study and look at. I got inspiration to use the collage method with simplicity for my data representation. Researching and analysing her work, I realised the method of using data, emotion and photography to create data visualisations.
Reflective Analysis & Learnings:
Visual metaphors are a powerful aid to human thinking. From Sanskrit through hieroglyphics to the modern alphabet, we have used cyphers, objects, and illustrations to share meaning with other people, thus enabling collective and collaborative thought. As our world experience has become more complex and nuanced, the demands for our thinking aids have increased proportionally. Diagrams, Data Graphics, and Visual Confections have become the language we resort to in this abstract and complex world. They help us understand, create, and completely experience reality.
As we live in the age of information, the world is an interconnected system where everything appears to be cross-linked. With the vast quantity of complex information that we need to understand, rank and communicate, information becomes fundamental. Therefore more and more information is being visualised, extending the application of diagrams far beyond its classical field of use.
Data Visualization is essential for almost every career and brand.
Brands will be able to access information quickly, improve insights, and make faster decisions to understand the following steps to improve the results. It benefits them to recognise the new patterns and the errors in the data.
Don't try to present too much information and maintain the privacy of the people participating in the data collection. While our presentations on visual statistics, many of my peers revealed the names and other vital information while presenting their data visualisation. It is very unethical to release the names or any different kind of personal information because it may lead the data on biased notions and breach of privacy as it may hurt the sentiments of the people. I was criticised that I should have shown the journey of 9 sports enthusiast people in my data along with names instead of the method of symbolising them. I tried to explain that we should not reveal the names of the person participating in the data collection as they might not prefer giving honest answers, which will create the data with lots of errors.
One of my peer's work which I found interesting, was Amy's. The way she did the ideation of people preferably should be living together in an accommodation designed by her based on the patterns she figured while analysing the data was quirky. In addition, I gave her the feedback that these inferences carried out by her utilising this data can be implemented for some TV shows like Big Brother.
Thanks to one of my peers named Chien-Chih Huang, who gave me personal feedback through email - "Her report analyses people who love sports. One of the conclusions that fascinate me is that people who love sports consider themselves leaders. In an article I have read before, sports can cultivate children's desire for victory, an ideal way to train them to become leaders. The two are related first, which makes me quite interested, and I agree with this statement very much."
In Visual Statistics, I made sure to keep my presentation to exact 10 minutes as feedback received in International Pedagogy. I rehearsed my presentation very well to keep it to the point, which will help me in the industry.
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