Strategies for Creating Informative Graphic Representations of Data
In the realm of data analysis, information visualization plays a crucial role in unveiling insights hidden within complex datasets. Here's a step-by-step guide to designing effective visualizations, as outlined by Riccardo Mazza in his book "Introduction to Information Visualization."
- Define the Decision: Clarify the exact decision or question the visualization is intended to answer. This ensures the visualization is focused and purposeful rather than merely aesthetic.
- Identify Relevant Metrics: Select the specific data points that are relevant to the decision. Filter out extraneous data and ensure that the necessary metrics are available and reliable.
- Develop the Story: Plan the narrative or message the visualization should convey to guide how the data is presented.
- Choose the Appropriate Visual: Select the most suitable chart or graphical form (like bar chart, line graph, etc.) that best illustrates the data and supports the storytelling.
- Add Relevant Elements: Incorporate labels, legends, and other visual cues that aid interpretation without creating clutter.
- Label and Review the Visual: Ensure all components are clearly marked, and review the visualization thoroughly for accuracy and clarity.
- Seek Nonexpert Feedback: Have someone without expertise in the data or domain review it to confirm the visualization is understandable to a broad audience.
User research is essential in defining the problem for information visualization work. It involves understanding the user's needs, their level of education, and their ability with data handling, among other factors unique to the user base.
Transformable models enable users to transform or modify data, allowing them to vary parameters for analysis or choose a different form of visual mapping for the data set. Manipulable models give the user control over the generation of views, allowing them to zoom in or zoom out on a model or to rotate 3-dimensional models for viewing from other angles.
Information visualization can involve quantitative data, ordinal data, or categorical data. Spatial relationships (or geographical relationships) involve data that relates to the real world, such as map data or an office floor plan. Temporal relationships refer to data that changes over the passage of time.
The number of dimensions or attributes in a data set can impact the complexity of the information visualization. Univariate analysis studies a single variable, bivariate analysis studies two variables, trivariate analysis studies three dependent variables, and multivariate analysis studies more than one variable. Hierarchical relationships involve data that relates to positions in a defined hierarchy, from an office management structure to a simple flowchart.
Networked relationships involve data that relates to other entities within the same data set. Linear relationships in data can be shown in linear formats such as tables or vectors. Static models are presented "as is" and cannot be modified by the user.
Combining transformable and manipulable models can create the highest level of interaction in information visualization. An example of a hierarchical network model is shown in the image above, which depicts the organization of the now-defunct Enron group's communications.
In conclusion, the purpose of information visualization is insight, not pictures, as stated by Ben Shneiderman. By following these steps, we can design visualizations that effectively communicate complex data, leading to informed decisions and valuable insights.
UI design plays a crucial role in enhancing the user experience, as it influences how users interact with information visualizations. User research should be conducted to understand the user's needs, preferences, and limitations, which can guide the design of a user-friendly interface for the visualizations.
Data-and-cloud-computing can be utilized to store and manage large datasets, ensuring the data's availability for information visualizations. Technology advancements can provide powerful tools for the creation, interpretation, and sharing of visualizations, facilitating the discovery of insights hidden within complex datasets.
