Definitions of Data Visualization
- Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends, and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software .
- Data visualization is a graphical representation of information and data. By using visual elements like charts, graphs and maps, data visualization tools provide an accessible way to see and understand trends, outliers and patterns in data .
- Data visualization is the graphic representation of data. It involves producing images that communicate relationships among the represented data to viewers of the images .
- Turn numbers into narratives. Data visualization tells multilayered stories to engage decision-makers in deep exploration .
- Data viz is the communication of data in a visual manner or turning raw data into insights that can be easily interpreted by your readers .
Categories and types of Data Visualization
5 Types of Big Data Visualization Categories :
- Data visualizations belong in the temporal category if they satisfy two conditions: that they are linear, and that they are one-dimensional. Temporal visualizations normally feature lines that either stand-alone or overlap with each other, with a start and finish time.
- Data visualizations that belong in the hierarchical category are those that order groups within larger groups. Hierarchical visualizations are best suited if you’re looking to display clusters of information, especially if they flow from a single origin point.
- Datasets connect deeply with other datasets. Network data visualizations show how they relate to one another within a network. In other words, demonstrating relationships between datasets without wordy explanations.
- Just like the name, multidimensional data visualizations have multiple dimensions. This means that there are always 2 or more variables in the mix to create a 3D data visualization. Because of the many concurrent layers and datasets, these types of visualizations tend to be the most vibrant or eye-catching visuals. Another plus? These visuals can break down a ton of data down to key takeaways.
- Geospatial or spatial data visualizations relate to real-life physical locations, overlaying familiar maps with different data points. These types of data visualizations are commonly used to display sales or acquisitions over time and can be most recognizable for their use in political campaigns or to display market penetration in multinational corporations.
Common Characteristic of Data Visualization
- It is visually appealing. The sophisticated visualization tools and the high quality of mobile applications have raised the bar on quality and usability. The quality requirements for visualization will only become higher with new and emerging technologies such as VR Glasses. If your visualization was developed with old technology and poor quality, probably no one will be using it.
- It is scalable. If you have great datasets, and you want more people to use them, you need to make sure your visualization is scalable. As data volume and the number of users grow, your visualization application needs to maintain the same performance. In another word, the system architecture should be scalable for future maintenance and modifications.
- It gives the audience the right information. It will be a problem you lead your audience to focus on a particular feature of your visualization but that is not the information they need. Before creating a visualization product, you need to understand your audience; you need to define exactly what they need. For example, are they the technical audience who want to drill down into the analysis or non-technical audience who want to understand the high-level information?
- It is accessible. You need to make sure people can use different devices to access your visualization, whether it is a high-resolution monitor or a mobile device. Furthermore, an accessible visualization is not only easy to use, but also easily be changed if necessary. The accessibility function is critical for user acceptance.
- It enables rapid development and deployment. After you collect some interesting data, your audience may want to see the information as soon as possible. In some cases like the traveller information applications, the audience may want to see the information in real-time to support their driving decisions. If you cannot put in a reasonable time limit, your audience will find other ways to get it
Data Visualization Consideration
- Choosing the right visualization technique and tools is important and it must be based on the objective or purpose for the visualization.
- Good data visualizations come in all shapes and sizes, but all have certain characteristics that help ensure that you produce something with important insights.
- Generally, a good data visualization piece should be :
- Meaningful – People use it on a regular basis and can make relevant decisions by a comprehensive view
- Desirable – It is not only easy to use but also pleasant to use
- Usable – People can use them to achieve their goals and easily and quickly
- Without the right visualization tools, raw data is of little use. Data visualization helps present the data in an interactive visual format. Here are the qualities to look for in a data visualization tool .
- Choosing the right type of visualization depends on what you need to show (comparison, distribution, composition, or relationship), how much detail the audience needs, and what information the audience needs in order to be successful .
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