Table Of Contents
  • Using data representation and visualization to make sense of information
  • Laying the foundation for data representation and visualization
  • Examples of data representation and visualization methods

Using data representation and visualization to make sense of information

Irrespective of the size of the industry, all businesses are now using data representation and visualization to learn and understand their data. They are using different tools and techniques to:

Comprehend data quickly:

By representing business information graphically, the management and shareholders can see large and complex data more clearly and cohesively, enabling them to draw meaningful conclusions. And since analyzing information in a graphical format is much easier and faster than using spreadsheets, businesses can answer questions and address problems more promptly.

Recognize patterns and relationships:

When data is represented graphically, even the most complicated parts start to make sense and this can help researchers to identify highly correlated variables. While some relationships can be obvious to find, there are others where one needs to dig a little deeper to identify them. Displaying information graphically helps businesses recognize complex relationships quickly, which enables them to make informed decisions much faster. Using data visualization to pinpoint trends can give organizations an edge over their competitors, which would ultimately affect the bottom line. With graphical representations, it is easier to identify outliers that affect the quality of a product or customer churn and fix problems before they get out of hand.

Communicate effectively:

When a business uncovers new trends from visual representations, the next step is sharing that information with others. Using graphs, charts, and other data representation tools make the information being communicated engaging and the message much quicker to put across.

Laying the foundation for data representation and visualization

Before using any tool for visual analytics, there a few things that one needs to do. For instance, one needs to have a good comprehension of the data that needs to be represented, the goals that need to be achieved, and the needs of the audience. Once this is out of the way, one must choose a visual that communicates the information in the best and simplest form. Also, one needs to remember that large data can pose a lot of challenges, hence when dealing with such data, some elements like data velocity and varieties must be put into consideration. Plus, in most cases, data is generated faster than it is analyzed and managed; this must also be taken into account. To represent data effectively, one should also think about the cardinality of columns that are going to be visualized. If there is high cardinality, it means that the percentage of unique values (like bank account numbers, for instance) is also high. Low cardinality means that the data column contains a high percentage of repeat values (like the gender column where ‘male’ or ‘female’ can be repeated multiple times).

Examples of data representation and visualization methods

When visual analytics were first introduced, the most popular method of representing data was using MS Excel spreadsheets to convert data into a table, pie chart, or a bar graph. While this method is still popular today, more advanced techniques have been introduced to help visualize large data. Among these include:

Infographics:

An infographic is a group of charts, imagery, and text used to display complex data in an easy-to-understand format.

Heat maps:

These are two-dimensional graphical representations of data in which the values are displayed in colors.

Fever charts:

A fever chart is used to represent changes in a variable over time

Time-series charts:

A time-series graph represents a set of data points collected and observed over a given reporting time.

Other popular methods of representing and visualizing data include bubble clouds, bullet graphs, area charts, line charts, treemaps, scatter plots and population pyramids,