In today’s increasingly data-driven world, having the capacity to quickly and accurately interpret complex data sets is an invaluable skill. Fortunately, data visualization tools provide a powerful way to quickly make sense of large amounts of data. By using graphs and charts, data can be organized and presented in ways that are easier to understand and interpret.
With the right tools and techniques, data visualization can be used to unlock the power of data and gain valuable insights into a variety of topics. From exploring trends in customer behavior to uncovering correlations in financial markets, data visualization can be a powerful ally to anyone looking to gain a better understanding of their data.
Topic Overview
What is data visualization?
Data visualization is the process of converting raw data into visual representations, such as charts and graphs, that are easier for humans to understand. Visualization can be applied to all types of data, regardless of source, and has many uses, including exploratory analysis, communication of findings, and decision-making.
While visualization is often used to create graphs or charts from quantitative data, it can also be applied to qualitative data such as images, sound, and text. Visualizations are made up of two components: the data, which is what we’re visualizing, and the visualization, which is how we represent the data.
Data can be anything, such as sales figures, a sample of people’s blood pressure levels, or numbers from an online survey. The visualization, on the other hand, is the specific type of graph or chart used to represent the data.
Benefits of data visualization
- Improved decision-making – Data visualization can help your team make better decisions by providing a clear picture of your data that is easy to understand. This allows you to focus on the data rather than the complexity of the numbers. With visualization, you can quickly identify patterns, trends, and outliers that might otherwise be overlooked. This allows you to make better decisions based on the numbers rather than relying on gut instinct.
- Improved communication – Effective data visualization can help your team communicate their findings more effectively, which can help to build relationships with customers and stakeholders, as well as with your internal team members. Visualizations aren’t just for numbers — they can also be used to represent text, images, and any other data types.
- Improved accuracy – When visualizing your data, it’s important to choose the right visualization type to ensure that your data is accurately represented. This is important because visualizations can sometimes skew data. For example, bar graphs that use uniform bar heights can be misleading because they can misrepresent the data by visually equating shorter amounts with longer ones.
Types of data visualization
Graphs
Graphs are a type of visualization that is used to explore relationships between data points. There are many different types of graphs, including bar graphs, line graphs, pie graphs, and histograms.
Charts
Charts are another type of visualization that is often used alongside graphs. Charts can be used to represent a variety of data types, including numbers, text, and images.
Maps
While some of the other visualizations are only used to explore relationships between data points, maps are also used to explore geographical data.
Techniques used in data visualization
- Selecting the appropriate visualization type – Visualizations come in many different forms, and before you can start to create a chart or graph, you have to decide which type is most appropriate for your data. This is important because different visualization types will represent your data differently, and the wrong choice can lead to inaccurate and misleading results.
- Thorough data cleaning – A clean data set is the foundation for any visualization. Before you start creating charts and graphs, you need to make sure that your data is free of errors and inconsistencies and formatted in a way that can be easily used by visualization tools.
- Selecting the right chart type – Once you’ve selected the appropriate visualization type, you need to decide on the best chart type for your data. There are many different types of charts, and each is used to represent data in a different way. For example, a line graph is better suited to exploring a trend over time, while a bar graph is better suited to comparing two groups of data.
Choosing the right data visualization tools
- Strengths and weaknesses – Every visualization tool has its strengths and weaknesses, and it’s important that you choose a tool that excels at the job you need it to do. For example, if you’re trying to create an interactive dashboard, you might want to use a tool like Tableau that is designed for that purpose.
- Ease of use – While you may want to select the highest-performing visualization tool, if it’s too difficult to use, your team won’t use it. Choose a tool that is easy to use, but still has the features that you need.
- Cost – While data visualization is an important part of any team’s work, it’s not something that needs to cost a fortune. In fact, many of the best visualization tools are free. Just make sure you’re comparing apples to apples to find the right tool for your team’s needs.
Examples of data visualization
- Interactive dashboards – Dashboards are a type of visualization that is often used in business and is designed to present key performance indicators in a visually compelling way. They are commonly used in the business sector, but can be a valuable tool for anyone, such as researchers, journalists, and academics, who regularly uses visualization.
- Data journalism – Data journalism is the process of using data to create compelling visual stories that are designed to inform and engage readers. When done correctly, data stories can be an incredibly engaging way to convey information.
- Infographics – Infographics are a type of visualization that combines text and images to convey information. They can be used to present data or information in a way that is easy to understand and easy to share.
Challenges associated with data visualization
- Data selection – Finding the right data to visualize can be challenging, especially if you’re dealing with large data sets. You need to ensure that you choose data that is representative enough of your larger data set. This can be difficult if you’re dealing with very large data sets, such as social media data, which could be misleading if you don’t use enough of it.
- Data cleaning – Once you’ve found a data set that you want to visualize, you need to make sure it’s clean enough to use. This is important because if your data isn’t clean enough, your visualizations won’t be accurate.
How to get started with data visualization
- Define your goals – First, you need to determine exactly what you want to achieve with your visualizations. Do you want to use visualizations to explore existing data, or do you want to use them to collect new data? Do you want to use visualizations to communicate your data to others, or do you want to use them to explore your data in greater detail? Once you know what you want to achieve, it will be easier to determine what type of visualization is best for you.
- Choose the right visualization type – Once you know what you want to visualize, you need to determine what type of visualization is best for your data. You can start by outlining the different options available to you, and then use that as a guide for selecting a visualization type.
- Clean your data – Cleaning your data correctly is a critical step in the visualization process because it will affect the accuracy of your data visualizations. It’s important that you spend enough time cleaning your data so that you can be confident in the results.
Data visualization best practices
- Be selective – Sometimes the data you have available might not be enough to create a compelling visualization. While it’s tempting to grab every piece of data you have, it’s important to resist the urge to do so, or you can end up with visualizations that don’t tell a story. Instead, be selective, and only use data that is relevant to your story.
- Variety is important – While you may want to create visualizations that only focus on one type of visualization, such as bar graphs, this can make your data visualization less engaging. It’s important to mix up your visualizations by using different types of visualizations, such as bar graphs, line graphs, pie graphs, and other types.
Conclusion
The right data visualization can help you understand your data and make better decisions with it.
When doing data visualization projects, make sure to choose the right type of chart to use based on the type of data being visualized. Depending on the data you’re visualizing, you might want to choose a map instead of a table, a graph instead of a bar graph, or a chart instead of a pie graph.
Once you’ve decided the most appropriate chart type for your data, make sure to do a thorough data cleaning exercise to ensure that your data is well-formatted and error-free.
