Top 15 Data Visualization Techniques to Enhance Your Analytics Credibility

The world around us is inundated with tons of data. As per recent research, about 2.5 quintillion bytes of data are produced daily. The evolution of new-age technologies like the internet of things and artificial intelligence can be attributed to this staggering figure. As a matter of fact, with the new age information explosion, the last two years have alone accounted for 90 percent generation in the world’s data.

However, this overwhelming amount of generated information is of little use unless it is well organized into analyzable data with actionable insights. Identifying trends and patterns within existing data is easy when viewing data in a visual format rather than in a plain tabular format. Visualizing your data provides the much-needed punch to shape your analytics strategy.

The concept of data visualization can be dated as long back as the 17th century when the first maps and graphs were used. In fact, one of the most prominent statistical graphs was designed to map Napoleon’s invasion of Russia.

Defining Data Visualization and its Importance

As stated in my previous articles, data visualization refers to providing a visualized context to the given information or data. It is the representation of data using visual formats such as graphs, charts, maps, etc. These graphical representations are vital for enterprises to comprehend logical relationships between various data and make more progressive business decisions.

Importance of data visualization can be attributed to the fact that human brain can easily process complex information in form of visual formats. A good data visualization technique has the potential to simplify complex datasets, thus conveying a clear and concise message.

Facts and figures given below are indeed proof of the importance of data visualization in this data driven world:

  • Half of our brain is active in visual processing with a speed of about 60K times more than text processing.
  • We can process an image in merely 13 milliseconds.
  • You can read high quality infographics with 30 times more clarity than plain text.
  • A simple graph with a scientific claim will be believed to accurate and authentic by about 97% of viewers.
  • Managers relying on visual data recovery tools are 28% more successful in getting timely information compared to those using managed reports and dashboards.
  • Data visualization helps 48 percent of managers to find data without being dependent on IT staff.

What are the different widely used data visualization techniques?

Before we figure out different possible ways to visualize data, let us understand data visualization through an analogy with architecture. Like architecture, visualization requires a basic understanding of the function or the valuable information to be extracted from the data. The next requirement is to understand the object or way a user would interact with the data. The last step is to render a clean and appealing structure to the available data.

In other words, it is imperative to have a good knowledge of the information to be presented before finalizing the type of data visualization. This article lists out 15 best ways to visualize your data based on your requirement.

1.  Column Chart

A column chart is the most common example of data visualization and simplest way to show side by side comparisons of various datasets. A column chart can also be used to monitor changes in data over time. A column chart represents datasets using vertical columns and hence it is easier to add values to each column without overlapping the chart.

Figure below shows a column chart comparing response time for each IT service team at different risk levels:

Figure 1: Example of Column Chart

2.  Bar Chart

A bar chart is best suited when you are working with lengthier data categories and negative data values. A bar chart or graph displays datasets through horizontal columns wherein the measurements are listed along the X-axis. You can also use a bar graph to view the growth of individual data sets within a single category.  

Figure below shows a bar chart showing number of incidents for different malware types. Each malware type or category is further divided into different security levels.

Figure 2: Example of Bar Chart

3.  Line Chart

A line chart is one of the most recognizable standard data visualization tools, demonstrating an overall trend in data. It is used to reveal progress or changes in data over a period. A line chart is best suited for continuous data set rather than discrete form.

Figure below shows a line chart with projected sales for each close date in different quarters.

Figure 3: Example of Line Chart

4.  Area Chart

An area chart is best suitable to understand overlapping of one set of data series with another. It depicts overall volume of data and the proportion taken by each category within that volume. An area chart emphasizes the space between a data series line and the axis with color to represent the trend information.

Figure below shows a stacked area chart depicting number of incidents against security ratings for last quarters:

Figure 4: Example of Area Chart

5. Pie Chart

A pie chart is used to compare values of each data set through the arc. It is also one of the most widely used data visualization tools to demonstrate the proportion of different data categories within a total volume. A pie chart can only be used with a single data series, and with only 6 or lesser categories, to avoid cluttering.

Figure 5: Example of a Pie Chart

6.  Scatter Plot

A scatter plot is vital to understand correlation between two data variables for different categories. The independent variable is represented on X-axis. Value of the dependent variable (for each data category) relative to the independent variable is represented using data points on the rectangular coordinate system.

7.  Bubble Chart

Like a scatter plot, a bubble chart also depicts values of each data category in terms of its circular circumference size. However, it only shows the value of a single measurement for different categories. A bubble chart is a multi-variate chart plotted using the three-axis – usual X-axis and Y-axis along with the Z-axis representing the area of each bubble.  

Figure below is a bubble chart showing average sales size for different salespersons depicted by circles with different areas.

Figure 6: Example of a Bubble Chart

8.  Radar Chart

A radar chart is a multi-variate chart used to compare values of different variables for various data sets. Each variable in a radar chart has its own axis. It is suitable to depict outliers or similarities within the variables and to check which variable is scoring higher or lower within a dataset. A radar chart is best suitable to compare two different brands of the same variety or to monitor job performance.

9.  Tree Diagram

A tree map is a complex data visualization tool used to interpret hierarchical data. It consists of different data categories represented as large size rectangles or branches of a tree. Each rectangle is further sub-divided into different smaller rectangles whose area or size represents values of different variable within the category. A tree map is best suitable to monitor sales data, to perceive underperforming or overperforming items for each category.

10. Funnel Chart

A funnel chart is like a pie chart showing the proportion of each data variable within a dataset or category. It demonstrates the progressive reduction of data with each passing stage. A funnel chart is best suitable to depict sales conversion data or the number of customers in each stage of the sales process.

Figure below is a funnel chart showing different stages of a sales cycle starting from the initial production stage to the final closed stage.

Figure 7: Example of a funnel chart

11.  Area Map

An area or scatter map is used to monitor business performance in different geographical locations. Data is plotted by circles of different colors, with the size of each circle representing the data value.

12.  Heat Map

A heat map utilizes color coding to represent variations in data. It is generally plotted as a tabular format or on a geographical map. A heatmap represents the value of data by the density of the color shade. The density of the color shade is directly proportional to the value of the data variable. Each heatmap is available along with a legend depicting data values for each given color code.

Figure below is a heatmap showing week wise patient count with different color code depicting different range of values.

Figure 8: Example of a Heatmap

13.  Gantt Chart

A Gantt chart demonstrates different stages of a project lifecycle with respect to a time frame. Each activity is represented by a bar whose length represents the duration of the activity and position defines the start and end date of the activity. A Gantt chart is usually preferred by managers to monitor the progress of a project.

14.  Word Cloud Chart

A word cloud chart is used to demonstrate textual data using visual representation. It depicts frequency of words within a body of text wherein size of each word denotes its occurrence. A word cloud is mostly used on websites or blogs to display usage of different tags. However, a word cloud chart is not suitable unless there is a larger difference in data.

Figure below is a word cloud chart which shows top customers by depicting their names with word size proportional to their occurrence.

Figure 9: Example of a Word Cloud Chart

15.  Network Diagram

A network diagram is an advanced data visualization technique used to represent semi-structured or unstructured data. It consists of nodes (representing individual data sets) and ties denoting the relationship between the data sets. A network diagram represents the interconnection of various data sets. It is typically used to show social networks.

Choosing the Right Data Visualization Technique

A myth surrounding data visualization is that it makes your dashboard or storyboard visually appealing. The purpose of data visualization is far beyond providing visual attraction. You do not choose a type of data visualization just because it looks attractive. In fact, factors like target audience, type of data, the context of the data, and the final purpose of the visualization, are essential to decide upon the relevant type of data visualization.

BIRD business analytics tool is among the prominent applications available in the market providing out of the box data visualization solutions for the most complex types of data.

Satisfied with the above information? Feel free to share your views, and even details about any other types of data visualization, not covered in this article.

Explore BIRD to leverage your business data through our powerful data visualization techniques.