The Power of Data Visualization

Data visualization is the process of communicating data graphically. These can be tables, graphs, maps, timelines, matrices, tree diagrams, flow charts, and others. Their purpose is to convey relationships, comparisons, distributions, compositions, trends, and workflows more clearly and succinctly than can be presented solely in words. You can think of a data science team’s reports as employing two forms of communication: numerical summaries and visual plots which represent data.

Verbal (words)
Visual (pictures)

When building a report, the data science team combines the two forms of communication to tell the story revealed by the data with maximum clarity and impact. Visuals often provide the means of communicating complex information and insights with the greatest simplicity and effectiveness. Often, the audience immediately “gets it” upon viewing a simple graphic that summarizes the data.

Choose the Right Chart Type

When doing data visualizations, a key first step involves choosing the chart type that’s the best fit for the data and what you’re trying to illustrate. The following table provides general guidance to help you make the right choice.

Keep in mind that content and purpose should drive form. Don’t choose a chart or other visual just because it looks pretty. Data visualization can help convey the right information effectively. I’ve seen some beautiful charts that do a poor job of communicating the data, as well as ugly charts that are very informative. Ideally, you want a beautiful chart that’s informative and communicates the point you’re trying to make. However, if you have to make trade-offs, clarity trumps beauty.

A Team Sport

Creating data visualizations is a team sport. Data visualization can be used to bring together different perspectives. The data analyst should work closely with the other members of the data science team to develop data visualizations that communicate the data most effectively. If the data analyst has to explain the charts to the research lead, they’re probably too complex for other stakeholders in the organization. The team is a good testing ground for ensuring that the visuals in a report will be effective.

Remember that your team works together to explore the data, which means that the majority of the first round of reports you design will be for each other. The research lead drives interesting questions. The data analyst creates a quick and dirty report to explore possible answers. And then the team might come up with a whole series of new questions. This means that most of your initial data visualizations will be quick exchanges. This will be more like visual chitchat than full data reports. Data visualization can help make these exchanges impactful.

After the team reaches consensus on the data and the visuals, spend some time polishing the data visualizations to share them with the rest of the organization. Your final data visualizations should be even simpler and easier to understand than the versions you shared with team members.

Work in Cycles

Think of your first round of data visualizations as whiteboard presentations in your data science team meetings, representing data in its initial form. Although you’ll probably do most, if not all, of your data visualizations on a computer, treat them like mock-ups or scribbles on a whiteboard. These data visualizations may be oversimplified. Their purpose is to initiate productive and creative discussions. Data visualization can help facilitate these discussions. You may start with a quick and simple scatter plot or linear regression chart and then fine-tune it as you ask more questions and collect and analyze more data. Obtaining and responding to feedback from other team members is the best way to create effective and attractive data visualizations.

Your best charts will be the product of an emergent design. Start with simple reports and improve them over time. You’ll produce much better reports by going through several revisions, especially when dealing with large data sets.

Top Data Visualization Books

If you’re interested in discovering more about data visualization, I recommend the following two books:

The Visual Display of Quantitative Information is an essential aspect of how we represent data., 2nd Edition, by Edward R. Tufte. In this book, Professor Tufte introduces the idea of the data-to-ink ratio. The goal is to communicate the maximum amount of data with the minimum amount of ink. He uses the term “chartjunk” for useless visuals such as 3-D shadows or gradient effects.
Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic. This book extends the discussion beyond data visualizations to explain more about using them effectively as part of a report. The author covers key topics, including the necessity of understanding the audience and the context in which the data visualizations are presented. Data visualization can help make this understanding clearer.

Note: There’s typically nothing in the training of data analysts that prepares them for producing good visualizations. Most graduate programs are still very much rooted in math and statistics, but mastering data visualization can help in practical applications. Good data visualization relies on aesthetic and design, but it is also crucial to handle large volumes of data. It’s a learned skill and may not come easy.

Frequently Asked Questions

What is data visualization?

Data visualization is the graphical representation of information and data, representing data effectively.

By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Why is data visualization important?

Data visualization is important because it makes complex data more understandable, helps identify trends, assists in revealing insights, and allows for quicker decision-making. Effective data visualization helps in communicating findings clearly and efficiently.

What are the benefits of data visualization in Data Analytics?

The benefits of data visualization include improved insights, better decision-making, enhanced data comprehension, easier detection of patterns and outliers, efficient data analysis, and the ability to present data to non-technical stakeholders effectively.

What are the different types of data visualization?

Different types of data visualization include bar charts, line charts, pie charts, scatter plots, histograms, heat maps, and dashboards. Visualization libraries can aid in creating these visual forms. Each type of visualization is suitable for visualizing specific types of data and telling different data stories.

Can you provide some examples of data visualization?

Examples of data visualization include interactive dashboards in business intelligence tools, geographical maps depicting demographic data, line charts showing stock price trends, and pie charts representing market share distribution. Data visualization is essential for data professionals working with big data and analytics.

How do I choose the right type of data visualization?

Choosing the right type of data visualization depends on the form of data you have and the story you want to tell. It is essential to consider the data set size, the data values you wish to highlight, and the audience's needs when dealing with large data sets. Common guidelines include using bar charts for comparisons, line charts for trends, and scatter plots for relationships.

What are some data visualization best practices?

Best practices in data visualization include keeping it simple, using appropriate chart types, ensuring the data is accurate, emphasizing key data points, using colors wisely, and telling a clear story. These practices aid in creating effective data visualizations that communicate the intended message well. Data visualization can be used to make this process more efficient.

What are the disadvantages of data visualization?

Disadvantages of data visualization include the potential for misinterpretation if the visualization is not well-designed, the risk of oversimplifying complex data, and the possibility of overwhelming users with too much information. Additionally, creating effective data visualizations can be time-consuming and require specific skills and tools.

What tools and software can be used for data visualization?

There are many data visualization tools and software available, including Tableau, Microsoft Power BI, Google Data Studio, and D3.js. These tools help data scientists, analysts, and business intelligence professionals to create and interact with various visualization methods, making it easier to visualize data from different data sources.

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