Class Exercise – Difference between Infographic and Data Visualization

I was thinking about the NM3229 course and realized that the course primarily taught us how to design data visualizations and infographics. Before this course, I was under the impression that these 2 things were one and the same. However, through this module, I have realized that there is a fundamental difference between these 2 concepts. I suddenly thought of the class exercise where we had to discuss the differences between these 2 concepts and thought that I should blog about it since one of my primary takeaways from the course has been to learn the difference between infographic design and data visualization design.

Data visualizations, as the name suggests, are simply visual representations of data. They includes graphs, charts, maps, pictures etc. When someone looks at a data visualization, he/she is left to draw the relevant conclusions. The visualization simply depicts the data graphically and in an understandable fashion. The task of deriving relevant information and conclusions from the visualization is left to the person looking at the visualization.

Infographics, on the other hand, contain multiple visualizations, text etc to convey specific information to a reader.  Infographics tell a story to a reader. By looking at an infographic, a reader can draw very clear conclusions about the data. Infographics usually consist of multiple data visualizations to clearly depict information.



Assignment 3 – Comparison of Data Visualization Tools

The final individual assignment of NM3229 was to get familiar with data visualization tools available and do a comparison among these tools. To explore various visualization tools effectively, data from US foods nutrient database (available at US Nutrient Database) was provided. We were told to come up with a motivating question about the data that could be answered with our visualization made in various data visualization tools. The data provided was HUGE. It was a challenging task to come up with an effective and relevant motivating question pertaining to the provided data and then to devise a visualization that could easily answer the motivating question. From all the 3 assignments, it is evident that the ideation stage is the most challenging stage of the design process. I spent some time thinking of relevant motivating questions about the provided data and came up with the following ideas:

  • Which energy drink provides the most energy to the drinker?
  • Which food category has the highest nutritional value?
  • What is the sugar content of popular desserts?
  • Which type of cheese is the most healthy ?
  • How do different fast food chains compare in terms of nutritional content of food?

I felt that all these questions had interesting answers and could be described using relevant visualizations. I ultimately narrowed down on the following question as it required a more specific data set and was a unique idea that I personally was interested in:

Which type of cheese is the most healthy (comparison based on sugar and fat content)?

Now that my motivating question was fixed, I had to narrow down on 3 data visualization tools that I would use to find answers to my motivating question. The 3 tools I did a comparison of were:

  1. Google Fusion Tables
  2. Tableau Public
  3. Microsoft Excel

I chose these tools for the following reasons:

  • Google Fusion Tables – Google is truly one of the greatest technology companies and produces some of the most innovative and useful products. As most Google products are top class, I wanted to use Google’s data visualization software and see how it fared against competitors.
  • Tableau Public – I had heard about Tableau from a friend who had used the tool before. It sounded like a useful and easy to use tool. Hence, I wanted to try it out and explore it for myself.
  • Microsoft Excel – I have used Excel extensively for school projects and internships. I am comfortable with using it and hence wanted to see how it performed as a data visualization tool.

After deciding my motivating question and choosing my data visualization tools, I began my comparison of the tools. The upcoming part of this blog post covers some of my findings and thoughts about the 3 data visualization tools.

Google Fusion Tables

Google fusion tables is a web based data visualization application aimed to gather and visualize large amounts of data. Though it is currently an experimental app run by Google, it had several features to make cool visualizations.

Fusion tables has a nice user interface. However, the data sources that you can connect to is limited. Common data sources such as excel worksheets and text files can be connected to easily. It is also possible to import data from a Google spreadsheet.

Since my data was in an excel worksheet, I was easily able to import it into the fusion tables.  After choosing attributes such as the table name and date of creation, the imported data is arranged as rows / cards. Like a Google document, fusion tables gives you the flexibility to filter your data based on various fields. For example, I could filter based my data based on type of cheese or fat content / sugar content.

Fusion tables also lets you summarize data on various parameters such as number of occurrences of a particular value of a particular field. You could also summarize the data by choosing to display the average, maximum and minimum values of the data.
To depict data graphically, there are several types of charts which can be utilized. I created the following visualization for my data:


I could pick which data value I wanted represented in the chart (fat / sugar). I could also pick the maximum number of items I would like to display on the chart. The appearance of the chart could also be changed easily using the ‘change appearance’ tab. Using this feature, fonts, font sizes, axis titles and chart legends could be easily edited.

Tableau Public

Tableau public is a very useful visualization tool. It has an easy to use user interface and has some very useful features. I particularly liked the following points about the Tableau:

  • It is very easy to import data from external sources.
  • After importing, the data appears as ‘dimensions’ and ‘measures’. In Tableau,’ dimensions’ refers to non number values while ‘measures’ refers to actual values which can be measured.
  • You can set ‘dimensions’ or ‘measures’ as the rows / columns of the visualization you would like to generate by dragging and dropping.
  • You can choose the chart you would like from a variety of charts.
  • You can sort data in the chart created in ascending or descending order which makes trends more prominent.
  • You can create and use calculated fields (fields calculated from other fields).

Some visualizations that I created around my data and motivating question:




Microsoft Excel

Excel is one of the most commonly used data analysis tools and a tool that I have used before. In all other visualization tools, I was importing data from excel. In this case I used excel itself to create the visualizations I wanted on the data.
Excel, like the other tools, has features to filter, sort and select data. The Insert tab in excel helps you insert charts based on selected data. A good thing about excel is that it has many different designs for different kinds of charts. For example, it has 2D and 3D bar graphs. There are several design adjustments you can make by changing the fonts, colours and styles of your chart.

A visualization I created around my data and motivating question:



Comparison of tools

I enjoyed using all the 3 visualization tools. They have clean and easy to use user interfaces. Playing around with each tool for an hour is sufficient to get a basic understanding of the main features of the tool.
The tool which I believe has most utility is Tableau. This is because:

  • You can import data from a wide variety of sources.
  • There is huge variety in the number of charts and visuals you can create.
  • It is easier to customize the created chart.
  • Tableau is well equipped to deal with huge volumes of data.

Fusion tables is a bit more simplistic as it does not have as many data sources and options for charts.
Excel has many of the features of Tableau. However, it cannot connect to many data sources. Also, a map feature is not available in excel.

Conclusions about data

The tools helped me discover that Riccotta cheese is the best for consumption due to its relatively low sugar and fat content. This is a surprising revelation as it is considered a creamier cheese and thought to be more unhealthy.
American cheese has the highest sugar content and Cream cheese has the highest fat content and hence these 2 cheeses are less suitable in high consumption.

Primary takeaways from Assignment 3

My primary takeaways from assignment 3 are as follows:

  1. Familiarity with useful data visualization tools such as Tableau, Google Fusion Tables and Microsoft Excel.
  2. Insight into how to deal with huge volumes of data.
  3. How to create effective visualizations based on massive amounts of data.

Overall, assignment 3 was a great insight into data visualization with large amounts of data. It also improved my knowledge of data visualization tools.