Below are in-depth discusssions of three visualizations that I find immensely valuable: maps, sankey diagrams, and bullet charts. Each of these visualizations has its own unique use case, but when used properly, they can be extremely powerful. 

Maps

Maps help users understand the who, what, and where of their data. Mapping a dataset is one of the easiest ways to bring together qualitative and quantitative data and can be used to answer important questions like: "Where are my users? Where should we test out a new product offering? How did a roadshow fare across different markets? What region is accounting for the greatest share of my product sales?" 

Sankey Diagram

Sankey diagrams are a great choice when you want to highlight relationships and visually describe the flow of information within a dataset. This visualization makes it easy to identify source-target relationships. In a Sankey diagram, information flows visually from left to right, and the magnitude is measured by the width of an individual line. Within a diagram, a source can have multiple targets, and a target can have multiple sources—increasing the flexibility of the visualization.

For example, this visualization can show the process through which a banking customer transfers money, by measuring the cash flow per transaction. Sankey diagrams are useful any time you want to show information flow across distinct steps in a process. 

Bullet Chart

The bullet chart combines the features of a bar graph and gauge into a single visualization by displaying a featured metric alongside other measurements. These charts are especially useful when your facing space constraints in your dashboard.

This visualization showcases the relationship between projected performance, organizational goals, and realized achievements. For example, support departments depend on these to figure out whether call volume in a given month has met or exceeded its target, and system engineers use bullet charts to measure real-world server performance against goals. These charts are useful any time you want to compare actual performance to projections. 

Data visualization has the power to easily communicate complex ideas and help us better understand data, but not just any visualization will do. Picking the wrong type of visualization for your data can result in confusion or misinterpretation. When building visualizations, a data analyst needs to consider the data they have, the business problem they are trying to solve, and the person who will ultimately interact with the dashboard, in order to select the best possible visualization.