Delving Deep in Data Visualization
Did you know that people tend to classify themselves as either visual or verbal thinkers?
Think back to grade 5. After your teacher taught you the basics of photosynthesis, the process through which plants make their food, did you need to refer to your textbook for the diagram, or were you simply able to understand it through the lecture the teacher delivered?
Research suggests that such a distinction between being a visual or verbal thinker may not exist in the first place. A combination of both is most likely the answer for many out there: if an explanation of photosynthesis is accompanied by a diagram that details the process with just as much information and clarity, the concept will be clearly understood.
Big data and its ever-growing reach have become the defining factor today. Whether it be your fitness app tracking the number of steps you’ve walked in a month, or the estimated pit stops displayed on your screens for a particularly enthralling Formula 1 race, big data has infiltrated our digital existence.
Considering how much data is collected in such a process, what matters eventually is how you make sense of it. That bridge between these two ends of a data analysis process is constituted by Data Visualization.
What is Data Visualization?
Scott Berinato refers to visualization as our shared language, which transcends text, spoken languages, and cultures to help people understand one another and connect. Data visualization, as a process, is the graphical representation of information and data using tools that transform complex data insights into compact and easily understandable bytes.
A good dataviz will tell a story while removing noise from data and highlighting useful information. It helps capture attention more effectively, mainly because the human brain can process pictures faster than words. Telling a story through graphics, thus, creates a greater impact by engaging the audience deeper.
Different Types of Data Visualization:
People are familiarized with simple bar graphs or pie charts from a very early age. However, while these may be a starting point for many, the right visualization must be paired with the right information set. Following are some of the common types of data visualization:
- Charts
- Tables
- Graphs
- Maps
- Infographics
- Dashboards
If we delve deeper, the following are some specific examples of visualizing data:
- Area chart
- Dot Distribution
- Circle View
- Gantt Chart
- Highlight Table
- Matrix (Not the Keanu Reeves Movie)
- Scatter plot (2D or 3D) and so many more
Considerations Before Visualizing Data:
Before deciding which kind of dataviz to create for your purpose, you must consider the following questions:
- Who will see this?
- What do they want?
- What do they need?
- What idea do you want to convey?
- What should you show it?
- And finally, how will you show it?
Once you have decided on such considerations and have a dataviz ready, you should qualitatively answer the following questions:
- Is it effective?
- Are you helping people see and learn?
- Are you making your case?
- Is the data telling a story?
- Is it communicating the context?
It is only when you have affirmative answers to such questions that your dataviz fulfills the purpose for which it’s created: to tell a story. The growing importance of creating compelling narratives to drive data-driven decision-making has made data visualization central to learning spaces. Sambodhi’s Education Nest also offers courses such as Tableau, one of the most popular data visualization tools, to help development professionals enhance the quality of decision-making through their insights and bring the impact the world desperately needs now.
Aishwarya Bhatia, Sambodhi
