What is Data Visualisation?
Data visualisation is the graphical representation of information and data. By using visual elements like charts, graphs nd maps, heatmaps and many more. Data visualisation tools provide us an accessible way to see and understand trends, outliers, patterns and nature of data.
Why do we need data visualization?
We need data visualization because a visual summary of information makes it easier to identify patterns and trends than looking through thousands of rows on a spreadsheet. It’s the way the human brain works. Since the purpose of data analysis is to gain insights, data is much more valuable when it is visualized. Even if a data analyst can pull insights from data without visualization, it will be more difficult to communicate the meaning without visualization. Charts and graphs make communicating data findings easier even if you can identify the patterns without them.
In undergraduate business schools, students are often taught the importance of presenting data findings with visualization. Without a visual representation of the insights, it can be hard for the audience to grasp the true meaning of the findings.
How is data visualization used?
Data visualization has many uses. Each type of data visualization can be used in different ways. We’ll get into the different types in a moment, but for now, here are some of the most common ways data visualization is used.
Different Data Visualization Techniques.
Now that we understand how data visualization can be used, let’s apply the different types of data visualization to their uses. There are numerous tools available to help create data visualizations. Some are more manual and some are automated, but either way they should allow you to make any of the following types of visualizations.
Line chart
A line chart illustrates changes over time. The x-axis is usually a period of time, while the y-axis is quantity. So, this could illustrate a company’s sales for the year broken down by month or how many units a factory produced each day for the past week.
Area chart
An area chart is an adaptation of a line chart where the area under the line is filled in to emphasize its significance. The color fill for the area under each line should be somewhat transparent so that overlapping areas can be discerned.
Bar chart
A bar chart also illustrates changes over time. But if there is more than one variable, a bar chart can make it easier to compare the data for each variable at each moment in time. For example, a bar chart could compare the company’s sales from this year to last year.
Histogram
A histogram looks like a bar chart, but measures frequency rather than trends over time. The x-axis of a histogram lists the “bins” or intervals of the variable, and the y-axis is frequency, so each bar represents the frequency of that bin. For example, you could measure the frequencies of each answer to a survey question. The bins would be the answer: “unsatisfactory,” “neutral,” and “satisfactory.” This would tell you how many people gave each answer.
Scatter plot
Scatter plots are used to find correlations. Each point on a scatter plot means “when x = this, then y equals this.” That way, if the points trend a certain way (upward to the left, downward to the right, etc.) there is a relationship between them. If the plot is truly scattered with no trend at all, then the variables do not affect each other at all.
Bubble chart
A bubble chart is an adaptation of a scatter plot, where each point is illustrated as a bubble whose area has meaning in addition to its placement on the axes. A pain point associated with bubble charts is the limitations on sizes of bubbles due to the limited space within the axes. So, not all data will fit effectively in this type of visualization.
Pie chart
A pie chart is the best option for illustrating percentages, because it shows each element as part of a whole. So, if your data explains a breakdown in percentages, a pie chart will clearly present the pieces in the proper proportions.
Gauge
A gauge can be used to illustrate the distance between intervals. This can be presented as a round clock-like gauge or as a tube type gauge resembling a liquid thermometer. Multiple gauges can be shown next to each other to illustrate the difference between multiple intervals.
Map
Much of the data dealt with in businesses has a location element, which makes it easy to illustrate on a map. An example of a map visualization is mapping the number of purchases customers made in each state in the U.S. In this example, each state would be shaded in and states with less purchases would be a lighter shade, while states with more purchases would be darker shades. Location information can also be very valuable for business leadership to understand, making this an important data visualization to use.
Heat map
A heat map is basically a color-coded matrix. A formula is used to color each cell of the matrix is shaded to represent the relative value or risk of that cell. Usually heat map colors range from green to red, with green being a better result and red being worse. This type of visualization is helpful because colors are quicker to interpret than numbers.
Conclusion
Effective data visualization is the crucial final step of data analysis. Without it, important insights and messages can be lost. Import.io understands the importance of data visualization, which is why it’s included in our Web Data Integration solution. Not only does Web Data Integration extract the data your organization needs from anywhere on the web, it takes that data all the way through the data analysis process of preparation, integration, and consumption, giving you easily consumable charts and graphs to gain insights from.
If your organization is ready to get the most out of web data, contact one of our data experts to see how Web Data Integration can help.