What is Data Visualization and Why It's Important?

What is Data Visualization?

With the development of computing, extracting large volumes of data has become possible. Yet, to gain the full benefits, it has to be possible to analyze the information as well. Data visualization is the solution to that. It is the presentation of data in a visual form, accessible to less tech-oriented users. It enables presenting vast amounts of information in an easy-to-comprehend way.

The data may be visualized in various graphic forms. They usually depend on the target user’s needs and the best format for a particular kind of data. The data visualization might be shaped into:

• Graphs,
• Maps,
• Charts,
• Scatter plots,
• Timelines.

It is crucial to mention that the graphics utilized for data visualization should not be static. They are not meant for presentation but for exploration. Moreover, they should include explanations of each variable and in general be of much higher quality, than graphs used purely to show historical data.

Why is Data Visualization Important?

Data has dominated the current business landscape. The importance of data-driven decisions is underlined by almost every company. This is no longer the time to base business decisions on gut feeling. Yet, collecting data is not enough – it also needs to be presented in a comprehensible form so that it could lead to conclusions. Plus, data changes in real-time, thus organizations must have access to the most up-to-date information at all times.

Various data visualization tools enable businesses to do that. As humans, we prefer images over blocks of text or numbers. We need to be able to navigate through data quickly and to see the relationship between different pieces of information. Data visualization makes it possible, enabling organizations to make more accurate and based-on-facts decisions.

Data visualization is also crucial for the clients. Being able to see the effects a certain product, service, or technology has is the key to increased customer satisfaction. It enables the clients to evaluate the effectiveness of a particular solution, thus seeing its benefits and confirming that the product or service is indeed worth its price.

The Key Elements of Data Visualization

Like product roadmaps, being the visual representation of an organization’s goals, data visualization, being the representation of the information acquired by a business, needs to be prepared carefully and thoughtfully planned. Clarity and readability are among the most important principles of this process, yet there are many more on which you should focus.

Simplicity (and Goal)

The data should be presented in the most simple way possible. While the level of simplicity depends on the complexity of the data itself, the fewer additional distractions to the visualization, the more accessible it is. It is crucial to keep your goal in mind. What data will the users need? Are some pieces of information redundant? If yes, do not visualize them.

Accuracy

Visualizing large amounts of data is often difficult and may lead to errors occurring in certain circumstances. But, what is data visualization for if the information is inaccurate? Therefore, while preparing data visualization, it is crucial to ensure the highest possible accuracy. If mistakes still happen under certain circumstances, the conditions causing them should be found and fixed. And, if the latter is impossible, it is critical to include information about the possible imprecision, to ensure that the users will conclude only when the displayed data is valid.

Visual Design

As people, we like elements that are attractive visually. Yet, this does not mean that you should put as much effort into the design as possible. An overabundance of visual elements that do not come with any other value might be distracting. The best solution here is to ask yourself several questions:

• Does this element attract attention?
• Should this element attract attention? (Meaning: Is it connected to a particular chunk of data)

The form is also crucial. Don’t only try to make it appealing, but also remember to make it clear. For instance, imagine data on electricity usage in various European capitals. A map would look great, but does the user have enough geographic knowledge to navigate through such a map quickly? If not, perhaps a chart would be better.

Story

Think about it – why is data visualization important? Because it tells a story – it connects the data with your goal and describes the journey towards it. A story is not just the starting point and the endpoint, it’s a description of everything that happened in between. Effective data visualization should incorporate this principle, showing how the data changes over time – it might be yet another source of information and lead to additional conclusions.

Examples of Data Visualization

Quite a few examples are presented in our case study Chartipedia: from Instagram to data visualization platform. There you may learn how we incorporated this process into our clients’ applications. You may also see additional examples in yet another article on our blog: Introducing Data Visualization in D3 JavaScript library, where we describe the whole process of building a fascinating and insightful visualization, starting with charts, and ending with a map.

Data Visualization: Key Takeaways

What is data visualization, and why is it important? Data visualization is the process of transforming data into a graphic design that is easily readable to a wider audience. Due to this, fewer data scientists are required in organizations, and specialists may quickly make data-driven decisions. Proper data visualization should be simple, about its goal, accurate, with engaging visual design, and should tell a story – describe the changes to data over time or location.

References:

• Sadiku, M. N. O., Shadare, A. E., Musa, S. M., & Akujuobi, C. M. (2016). DATA VISUALIZATION. International Journal of Engineering Research And Advanced Technology(IJERAT), 2 (12), 11–16
• Chen, C.-h., Hrdle, W., & Unwin, A. (2008). Handbook of Data Visualization (Springer Handbooks of Computational Statistics) (1st ed.). Springer-Verlag TELOS.