Instead of telling them a story, show them.
You have the data, you have the insight, now what? The next step is to communicate your findings in the best possible way with people who need to take the necessary actions. To do so, unleash the storyteller in you, simplify things and make sure that the real essence of your analysis is not lost. Get started with data visualization.
The question might be bothering you, ‘Is data visualization easy?’
Data visualization is not as hard as you might think. Just maintaining the right balance between all of the visual elements to create an impact is all you have to do. However, there goes in a lot of effort to balance it by not doing too little or too much.
As Ben Schneiderman rightly puts it:
“Visualization gives you answers to questions you didn’t know you had.”
Before getting into the tips to make data visualization easy, let’s make sure we all understand what it is and why it is important.
What is data visualization?
Data visualization can be penned down as the representation of any data or information in form of a chart, a graph, or any other visual format, with the use of which you can communicate your data. Finance, marketing, design, or technology, in every field there is a need to visualize data. In simpler terms, visualizing data brings the story behind your numbers to life.
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Tips to make data visualization easy
Before initiating, many people are concerned that is data visualization easy or not. But, once you are into it, you will be able to tell stories through your visualizations.
There is no doubt that data visualization is important today, but we can see some poorly designed visualizations. This is why I have pulled together some best practices to make data visualization easy by avoiding some common mistakes.
1. Do not display too much data
To not overwhelm the person, you are representing your data to, avoid displaying too much data. Think before you choose how to visualize your data. Focus on whether the table is telling your story or not. Give a thought to the data analysis tools, if they are offering a better-suited visualization to communicate your data, use them. Choose wisely.
Say, for example, you decided to move forward with a table, these will be the best practices to help the audience to make sense of what they are consuming:
- Filter your data to top or bottom rows. Filtering the data will help the audience to scan the important data relevant for decision-making.
- Make sure to add detailed data from your sources to back up the numbers representing your data.
- Think critically if you need to use data labels in your table. Adding too many of them can spoil your visualization by distracting the audience.
2. Make your data easy to read
Increase the readability of your data by using commas or by skipping on the unnecessary decimal points in big numbers. You can also shorten the values by reciprocating or adding symbols such as K, M, or B for thousands, millions, and billions to make data short and easy to read.
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3. Wisely use formatting
If you are using any simple kind of visualization, you can always play with the formatting types. Such as the trend lines help you to make the data pattern more obvious and clear to the user. These will also help you to predict and detect trends and outcomes.
4. Make responsive images
Make sure that your user understands your data irrespective of what device they are using. Your visualizations must be equally easy to access for both desktop and mobile users. To ensure that your data is properly understood, your visualization should be responsive. It means that the layout and image should automatically adapt to the target screen. There are a variety of online tutorials offering to train you on how to adjust your responsiveness. Or you can do it automatically by using optimization tools.
5. Compare sets of Data
Including comparisons while representing data in visual forms can be useful. You can compare two variables to provide an insight and guide the user about the message that is hidden in your visualization. Such as, you can compare the sales of your company with the previous year by plotting a comparison between both.
6. Choose the right color scheme
Meaningfully assign a color to data. You can also match series colors on multiple charts so that it looks easier to view data with different angles at the same time. Do not use a legend with so many colors. It will make it hard for the user to differentiate the data properly. Keep the number of colors low.
7. Use alternative texts or captions
To make your data accessible write captions underneath the data visualization. A short description of an image will help to make sure that the people unable to understand the visualization understand it through the caption.
Following these tips will help you to visualize your data in a more organized form.
What makes the visualization of data effective?
When done right, data visualization is effective. To be highly effective, the visualizations should serve their purpose, and to do that, you need to design the right visualizations for your team members to interpret and make decisions based on them.
Types of data visualization categories
We can divide data visualization into the following categories based upon your data.
- Temporal data visualization
Your visualizations are temporal if:
- They are linear
- They are one-dimensional
This type of visualizations mainly features lines that may stand alone or overlap each other with a starting and ending time. They include:
- Line graphs
- Scatter plots
- Time series sequence
- Polar area diagrams
- Hierarchical data visualization
The visualizations that fall in the hierarchical category are those in which the entities are not dependent and they all are related to each other. Simply putting, they are groups within larger groups.
The downside of the hierarchical tree graphs is more difficult to read. Otherwise, it is the simplest graph to follow due to its linear nature.
Hierarchical graphs include:
- Ring charts
- Tree diagrams
- Sunburst diagrams
- Network data visualization
Also known as network graphs, network visualization is used to visualize complex relations between a huge amount of elements. It displays directed and undirected graph structures. They connect datasets with datasets by illumination relationships between entities.
Network data visualizations include:
- Word clouds
- Node link diagrams
- Matrix charts
- Alluvial diagrams
- Multi-dimensional data visualization
As clear from the name, multidimensional data visualizations represent one dimension as one-dimensional, two dimensions as two-dimensional, three dimensions as three-dimensional graphs, and so on. This is the most eye-catching type of data visualization.
Multi-dimensional data visualization includes:
- Pie charts
- Venn’s diagram
- Scatter plots
- Stacked bar graphs
- Geospatial data visualization
It is one of the earliest forms of data visualizations. It is used to relate to overlaying familiar maps or locations with different data points. It is also commonly used as display sales of market penetrations in multidimensional corporations.
Geospatial data visualization includes:
- Flow maps
- Heat maps
- Density maps
Pros and cons of using data visualization:
|Data Visualization||Data Visualization|
|Easily summarized large datasets||Requires additional explanation either verbal or written|
|Provide better clarification of trends||Can be falsely manipulated by amateurs|
Since now you have the know-how of data visualization, here are some tips to make it easier and better:
“Data is the new oil? No, data is the new soil.”
– David McCandless
Data makes the world go round. Therefore, we cannot deny the importance of data visualization. Effectively visualizing the data will help you to explain the story behind your data.
What is the main objective of data visualization?
The main goal of data visualization is to understand the importance of data and to communicate it through visualization clearly and effectively. By analyzing and reasoning the data through visualizations we can make the complex data easier to use and understand.
What tools are used for data visualization?
Data visualization tools offer a wide range of styles and ease to use. The best data visualization tools include the following:
- Google charts