The 9 best data visualization methods that will add value to any report

Professional data analysts use data visualization techniques such as graphs, charts, and maps to generate reports from numerical data. These visual elements help others understand the patterns, trends, and outliers in each data set.

By knowing and applying the best data visualization techniques, you can also improve your project reports. Read on to become your own data analyst or learn more about mathematical visualization of data.

1. Choropleth map

You can use a choropleth map to visualize geographic location data. You’ll need to use a gradient to show the data values ​​decreasing or increasing as the color fades or deepens.

This data visualization technique enables you to see the change of data variables from one region to another. You can use this technique to visualize population density, employment rate, and so on. If you want to determine the nationwide consumption of your services or content, this technique is also useful.

Google Data Studio’s geo-chart element is useful for creating choropleth maps. You can add your data to Google Sheets and then import it into Google Data Studio to create different views of your choropleth map. You also get an interactive graphic that shows the underlying data when you hover over any area of ​​the map.

2. Bar graph

Bar charts are crucial for visualizing data comparisons between multiple components of the data. For example, comparing sales from financial quarters or years is the most common application.

You need to place the categories you want to compare on an axis. You have to keep the measured values ​​on the other axis. The length of the bars varies depending on the values ​​of this second axis.

You can make your bar graphs more informative by adding different colors, 3D effects, legends, interactive data views, and more. Google Data Studio offers you several forms of bar graphs. You can choose between column / stacked column, bar / stacked bar, etc.

3. 3D maps

3-D maps are essentially diagrams made up of bars or columns on a geographic map. You can use this type of chart to showcase your technical talent when presenting reports to the audience. 3D maps are ideal for product, service, or population data records that link a specific region in the world.

Microsoft Excel has a nifty feature for creating such cool pictorial data visualizations. It’s called Microsoft 3D Cards. You can find it on the insertion Excel ribbon tab.

4. Sankey diagram

This data visualization technique is useful for showing how data flows. A pure text or rectangular field represents the entities or nodes of your data record. Arcs or arrows of different widths represent the connection between the above nodes.

The width of the arrows or arcs is directly proportional to the importance of the data flow. Hence, this diagram is useful for visualizing large data sets related to the flow of resources such as energy, money, time, etc. in a project.

5. Network diagrams

You can show the relationship between multiple entities by creating a network diagram. In a network diagram, nodes or vertices represent each entity. Edges or links represent the connection between these nodes.

You can use network diagrams to find patterns, anomalies, and insights from large data sets that connect multiple entities. Alternatively, you can use a network diagram to resolve bottlenecks in project tasks and subtasks by linking all resources and statuses.

6. Timeline charts

Timeline charts help you visualize events or tasks in chronological order. It has wide uses in managing multiple tasks and projects. Those involved can easily identify project progress or bottlenecks when you visualize task-related data in timeline charts.

Timeline charts are typically linear. The most important events, tasks and subtasks are displayed on the axis. You can also make your timelines more attractive by inserting graphics, document thumbnails, linked resources, milestones, deadlines, and so on.

Gantt charts are optimized timeline charts. You can find many free Gantt chart templates in Microsft’s Excel template library. You can choose any template that suits your project and tweak it a little to create comprehensive project schedules.

Related: How to Create a Scatter Chart in Excel and Present Your Data

7. Treemap diagram

The treemap diagram comes from the renowned information visualization and calculation method treemapping. Large hierarchical data sets are visualized on nested rectangles. Your data looks highly organized in branches and sub-branches.

You need a data set with two quantitative values ​​for each product or service because the rectangles visualize two data values. A treemap diagram is ideal for visualizing a huge data set with many attributes on one screen.

The color and dimension of each rectangle are directly related to the underlying values. Therefore, when you create treemaps for large data sets, spotting anomalies or patterns becomes really easy.

8. Spiral chart

Spiral charts efficiently visualize large data sets on a single screen. You can view the underlying data as points, columns, or lines. You can also color-code the data for easy visualization.

Typically, you need to place the starting point of your data in the middle of the spiral and move outward as the volume of data increases. Spiral plots are ideal for visualizing the following data: annual student attendance, employee attendance, products sold, website traffic, etc.

9. Pyramid diagram

Pyramid charts are ideal when you need to visualize records in a hierarchy. You will see different pyramid charts with lots of visual effects. However, it’s just a triangle with different sections separated by lines.

These separate sections of the triangle usually have different heights and widths. The value of the underlying data defines the volume of the separated sections. Usually the high volume or wider sections should have a general topic.

The least volume section should have a niche topic from the general topic at the bottom. You can create pyramid charts to better understand your business model, products sold, customer segment, and so on.

Get valuable insights from your data

These are the commonly used data visualization techniques that cover a wide variety of data sets. You can easily create any of the above charts or graphs by practicing a few times. If you are a learner, these are the appropriate math data interpretation techniques to get started.

While using the above techniques in your work or school, you should know that you can create great data visualizations with just a few clicks using Google Data Studio.

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