What is Data Visualization and Why is it Important?
Humans are adept at processing visual information. In fact, the ability to respond to information conveyed by light is so important that different visual mechanisms have evolved multiple times since the origin of life. Now, as businesses become more data-driven it is no surprise that companies are realizing the importance of visualizing data, hiring analysts, and providing them with tools to better extract information from data.
Visual representations have several advantages over other forms of information sharing, such as text or tabular data. First, visualization presents large volumes of data at once. A simple pie chart, for example, presents relative proportions of a whole while a bar chart allows a viewer to compare multiple values at once. More complex visualizations, such as network diagrams and three-dimensional contour plots enable someone to grasp 3D shapes using a 2D surface.
Improved Understanding of Data
Visualizations also make large data sets comprehensible. Commonly used visualizations, such as charts, can represent hundreds or tens of thousands of data points in the same sized visualization. One of the most significant advantages of a visualization is that it facilitates rapid comparison of data sets, which is one way to facilitate the formation of insights derived from the data. This is especially useful when comparing measurements over time, such as this quarter's sales to sales from the same quarter last year. Someone could view columns of data looking for anomalies and making comparisons but it is more difficult to extract insights from low level details.
Another benefit of visualizations is that they convey information without requiring mathematical skills or calculations. A statistician may be able to derive insights by calculating properties of a data set but non-statisticians could find the same insights through visualizations. For example, the shape of a curve can be understood by calculating the median and standard deviation when one understands statistics. Viewing an image of a bell curve or a long tail curve conveys the same information to someone unfamiliar with statistics as it would to a mathematician.
All of these advantages contribute to the increasing adoption of visualization. Representing data in a visualization instead of a table can mean the difference between whether a reader understands an insight or not. The ability to see patterns in data can lead to hypothesis generation about more complex relationships. Deriving insights from visualizations also leads to higher degrees of confidence in those insights.
Three Stages of Visualization
Working with visualized data often includes three stages: overview, focus, and drill down.
In the overview stage, the goal is to develop an understanding of the global characteristics of a data set. For example, there may subgroups in a data set or outliers that indicate something anomalous about some data points. Visualizations can help find these areas of potential interest and that leads to the second stage.
During the focus stage the goal is to better understand the subsets of data that appeared interesting in the first stage. At this point you can filter out data points that are less interesting and zoom-in on areas that are relevant to the hypothesis the analyst is investigating.
Finally, in the third stage you can drill down into details of the data set. The goal is to validate the hypothesis under investigation. Now that you have narrowed your analysis to relevant subsets of data, does the data support your hypothesis? This is a crucial stage because this is the point where you validate your insight with support from the data.
As valuable as visualizing data is, it a means not an end. Visualization should be a first step in exploring and representing data in away that provides actionable insights. The next step is to move from understanding what has happened in the past to using data about the past to model the future. For more on how to leverage even more from your data, read our post on business insights vs. business analysis.
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