reality by presenting the data in different ways. When using line or bar charts, use
caution not to distort the data by truncating the bottom of the line or bar chart where
differences between the data points appear larger. Also, use caution with scales, such as
different size bubbles to ensure they are at the correct scale for comparisons.

Data Quality

Data quality is important for a good visualization. Good data include data that are
complete, clean, not questionable or conflicting, and valid. Quality data can lead to better
decisions and better visualizations. There are different dimensions of data quality to be
considered including the following:

• Accuracy—correct values


• Completeness—no missing values


• Consistency—same unit of measures or time format


• Integrity—data that are reliable


• Timeliness—data relevant to the time period


• Uniqueness—removal of duplicates


• Validity—data that are valid and not made up


• Accessibility—data that are accessible with permissible use

Data can be collected from many different places. Before designing a visualization, it's
important to understand the data that will be used. Data can be structured, such as a
customer name and location, or unstructured, such as a customer comment or phone call
transcribed to text. When collecting the data, it's important to understand how different
data sets are related. For example, if structured customer data and unstructured customer
comments are going to be used, then how are they related? What will be communicated
through a visualization and what kind of story will be told? By understanding these
questions, then the right type of visualization can be used.

Step 3: Design

The concept of using a visualization to represent data has been around for hundreds of
years. Today, with the advancements in technology and business intelligence (BI)
technology capabilities, there are many tools available to help create a visualization.
Technology has made it possible to process high amounts of data quickly. Technology
may continue to advance capabilities to create a visualization—perhaps through audio
describing what a user wants to see or through machine learning. No matter where we are
going with the creation of a visualization, there are fundamentals that are important to

Chapter 18.1: An Introduction to Data Visualizations