Best Practices for Data Visualization

Introduction

“The greatest cost of a picture is when it arms us to notice what we have never waited to see.”

Data Visualization is a method of translating raw data into visual forms like charts, graphs, and maps. It has quickly become famous for publishing information on the web. It helps in a wide variety of industries, from business intelligence to journalism for better knowledge and conveys the vision within data. Displaying data in visual form makes it easier to grasp and analyze, and counted as best practices for Data Visualization. It also helps in decision making faster and finding patterns, including new and hidden, to better understand difficult concepts. When information represents in graphic form, a human can realize more frequently and quickly even complicated things also. Yet it does not require any technical skills and easy to learn with the help of various tools like Power BITableau. Before we get started with the Data Visualization best practices; let’s get to know some history. 

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An Illustrated History of Data Visualization

The theory of Data Visualization is not new. Even it has been around for centuries. The first and most obvious examples are maps. Then the pie chart came into play in the 19th century. After a few decades, Charles Joseph and Minard used graphs for drawing map Napoleon Bonaparte’s Russian campaign of 1812 with different metrics like several armies, temperature, distance. Some more highlights in the history of data visualization include Oresme one of the best thinkers of middle ages invented bar chart in the 14th century, and Playfair is awarded first use of area chart.

Let us start with the Data Visualization Best Practices.

10 Best Practices for Data Visualization

All of us want our Business Intelligence (BI) and analytics solutions to transform data into
insights. However, the data is handy only if understand and shares it in a better way. In this section, we will be listing the best practices of Data Visualization for the same:

1. Entitle a specific audience and mark their needs

Constructing visualization and putting all of them together into a dashboard is not a way of providing data to the customers. There might be chances that one not fit for all. We have to put more effort than just sharing performance with others, monitor behavior, and measure effectiveness. We have to answer these questions also:-

For whom are we designing visualization?

While designing a dashboard, we must know our priority persona. What challenges are we doing to face? How we solve these challenges? Design dashboard in such a way that meets all the requirements of users. 

What decision do I need the user to make?

It must answer that what decision should I take. Is this decision is risky? Is this profitable or not?. Whether I buy this property or not. Sometimes a decision needs to be answered multiple times a day or even a week or month at a meeting. So visualization should be created in such a way it will give a binary response to you.

2. Choose the right visual (The Key)

Once you know your audience and the data, it is time to select the right type of visual that best expresses the information included in the data:

Line Chart

When we want to represent continuous data with small changes, we use a line chartIt works well for a high number of values with different time intervals.

Bar Chart

When we want to compare data, there can be a horizontal or vertical bar chart. Example Like time spent on a smartphone, compare which field had the highest profit or compare no of paid and unpaid apps downloaded by types of gadgets

Pie Chart

Mainly the pie chart is used for representing the proportion or percentage of data. It is useful when we have less than seven categories. It also helps in determining at what percentage that particular category holds.  

Column Chart

It is used when we want to compare various categories with subcategories. It is also used to compare various items within a particular range. Like in this example, we compare the profit of a particular product in a specific country.

Area Chart

It is good practice to use area charts when we want to show how values develop with time. It mainly used when we want to know the exact share of a particular category. It helps to represent a significant difference between values.

3. Apply text carefully and intentionally

At the top or upper left corner put all the vital point because the human eye is drawn that place first. Try to add three or four views in a single dashboard as it is one of the best practices for Data Visualization. Because if we add too many graphs, it will difficult to understand. When applying multiple filters, group them, and add a border around it to make it more transparent and attractive. 

4. Use the predictable pattern for layouts

Human eyes quickly caught indicators that help in understanding important information. You usually grab patterns, and if they are random or don’t make sense, sometimes it is tough to understand what visualization wants to communicate.

To know about human thinking, we have to represent data that make sense to viewers, either it is sequential or numeric. If you are using no graphs, make sure the chart should be properly visible, and connections between data should be clear. Don’t let your viewers get confused as it is not counted in Data Visualization best practices and users can quickly they can go from to point to another.

5. Select the right data visualization tool

The tools for visualization of data defer from developers to Data Engineers to Data Analysts to BI Engineers. Some of the Tools that are Famous among the community are –

  • Echart 
  • Highchart 
  • Tableau
  • Fine BI
  • Power BI
  • Ali DataV
  • FineReport
  • Digital Hail

6. Use attractive colors for telling data stories

Colors play an important role in depicting graphs without using words. It helps to communicate a lot about your visuals. Try to keep it simple as it is one of the best practices for Data Visualization. Using color for highlighting essential points helps to understand the dashboard more frequently and quickly. Proper Color clubbing matters a lot. The viewer can understand faster and promptly try to use natural colors. For example, in graph use different shades of particular colors to show a profit for a specific month and brightest for a month have the highest benefit. To highlight contrasts in data colors also be a help and one of the advanced Data Visualization techniques.

7. Use attractive and straightforward Dashboards

As we know, most of the time dashboard contains a number of the graph so try to add three or four charts or graphs for better understanding. Try to use different colors for different figures for better knowledge of viewers because the dashboard is the main thing that will help the viewers to get the result and make decisions accordingly. If we made a dashboard clear and attractive helps to grab the viewer’s intention.

8. Design in such a way user should be engaged

Designing the dashboard that the user should be engaged in is one of the most important Data Visualization Strategies. For compiling data into data, visualization consistency plays essential. A great visualization helps the observer to conclude faster and efficiently. They perfectly show data. Display on the hierarchy of data supports the observer for making a decision quickly. The designer can sort the data into highest to lowest to highlight the most crucial point to the viewer in the most prominent way. Even in which order we display data, the colors we used, the font we used, various other elements in charts help viewers to interpret data more efficiently.

9. Try to make visualization inclusive

The way we represent data using different colors helps people to better understand the dashboard. From the recent review, it is a critical factor in observer decision. People responded to different color combinations faster than similar colors used in charts. If we use identical colors with fewer contrasts, create difficulty to average viewers, sometimes even more difficult for them who have a vision problem. Data Visualization best practices include using the right contrast combination. To convey different types of information, try to use different patterns. For labels, elements try to use text and icons. 

Data Visualization best practices

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10. Examine business insights

In this competitive world, it is tough finding data interaction. This is one of the most important best practices for Data Visualization that emphasis you to find data correlation in business insights. Examining these insights is very important for every business so that they can set the right track for achieving their goals. It also helps in getting knowledge of the latest trends and provide a quality product to customers. It also helps in finding a problem before they arise. You can stay at the top of trends which helps you in increasing profits.

Conclusion

Data visualization helps in handling a large amount of data that will be shown in the form of meaningful charts and graphs. To do so, we need the best visual tools that are available in the market like power BI, tableau, and many more. Always try to choose the tool according to your requirement. The tool should support maximum interactivity, attractive, combining data sources, sharing dashboards with other viewers. Always try to create a neat and clean dashboard that helps viewers for a better understanding of data and also helps in decision making. Last but not least data visualization saves a lot of your business time. 



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