What is data visualization?
Data visualization is the process of using visual elements like charts, graphs, or maps to represent data. It translates complex, high-volume, or numerical data into a visual representation that is easier to process. Data visualization tools improve and automate the visual communication process for accuracy and detail. You can use the visual representations to extract actionable insights from raw data.
Why is data visualization important?
Modern businesses typically process large volumes of data from various data sources, such as the following:
- Internal and external websites
- Smart devices
- Internal data collection systems
- Social media
But raw data can be hard to comprehend and use. Hence, data scientists prepare and present data in the right context. They give it a visual form so that decision-makers can identify the relationships between data and detect hidden patterns or trends. Data visualization creates stories that advance business intelligence and support data-driven decision-making and strategic planning.
What are the benefits of data visualization?
Some benefits of data visualization are as follows:
Key stakeholders and top management use data visualization to interpret data meaningfully. They save time through faster data analysis and the ability to visualize the bigger picture. For example, they can identify patterns, discover trends, and gain insights to remain ahead of the competition.
Improved customer service
Data visualization highlights customer needs and wants through graphical representation. You can identify gaps in your customer service, strategically improve products or services, and reduce operational inefficiencies.
Increased employee engagement
Data visualization techniques are useful for communicating data analysis results to a large team. The entire group can visualize data together to develop common goals and plans. They can use visual analytics to measure goals and progress and improve team motivation. For example, a sales team works together to increase the height of their sales bar chart in one quarter.
What are the components of data visualization?
Data scientists combine three main components for visualizing data.
The story represents the purpose behind data visualizations. The data scientist communicates with several stakeholders regarding what they want to achieve by analyzing data. For example, they may want to measure key performance indicators or predict sales volumes. Data scientists and business users collaborate to identify the story they want the data to tell them.
Data analysts then identify the appropriate datasets that will help them narrate the data story. They modify existing data formats, clean the data, remove outliers, and perform further analysis. After data preparation, they plan the different methods of visual exploration.
Data scientists then select the visualization methods best suited to share new insights. They create charts and graphs highlighting key data points and simplifying complex datasets. They think of efficient ways to systematically present data for business intelligence.
What are the steps in the data visualization process?
There are five steps for effective data visualization.
Define the goal
You can define a data visualization goal by identifying questions that your existing dataset can potentially answer. A clear goal helps determine the type of:
- Data you use
- Analysis you do
- Visuals you use to communicate your findings effectively
For example, a retailer may seek to understand which type of product packaging gets the most sales.
Collect the data
Data collection involves identifying internal and external data sources. There are massive datasets available online for purchase and use. Your company may also have existing data archives available for analytics. For example, you could collect historical sales volume, marketing campaigns, and product packaging data to find the best packaging.
Clean the data
Data cleaning involves removing redundant data, performing mathematical operations for further analysis, or filtering and converting data to meet the question criteria. For instance, you may remove sales volume data from the holiday months and after marketing campaigns to identify average sales by packaging type.
Select the data visuals
You can choose from several different chart types for efficient visual discovery. Relationships between the data points and the insights you want to communicate will determine the best graphical representations. For example, you may use a bar graph to represent packaging sales by color in the last month. However, a pie chart may be better suited to show the percentage of colored packaging in your inventory. There are two main types of data visualizations.
A static visualization provides only a single view of a specific data story. An infographic is an example of a static visualization.
Interactive visualization allows users to interact with graphs and charts. Viewers can change variables in visualization parameters to find new insights or access in-depth information. Data visualization software typically includes a dashboard for user interaction with the system.
Create the data visuals
You can create the data visuals you need by using data visualization tools. Most tools import your final dataset and automatically generate the required reports. Some design principles for effective data visualization include the following:
- Draw audience attention to important details using sizes, colors, fonts, and graphics
- Provide context to data using visual cues
- Choose the right color combinations
- Use explanatory titles to provide key insights to the audience and help them focus on the right questions
- Add clear labels and numbers
What are the different types of data visualization techniques?
While charts and graphs are the most common, you can use several different data visualization methods. Five main types of data visualization methods are provided below.
Temporal data visualization
Temporal data visualizations are used to represent linear one-dimensional objects like a line graph, line chart, or a timeline. For example, you can use line charts to show changes that occur continuously over a given period. Several lines in the line chart demonstrate variations of different factors over the same period.
Hierarchical data visualization
Hierarchical data visualization refers to a group or a set of items that have common links to a parent item. You can use these data trees to display clusters of information. For example, you can show inventory data quantities as a tree with a parent node (clothes) and child nodes (shirts, trousers, and socks).
Network data visualization
Network data visualization is useful for representing the complex relationship between different types of co-related data. For example:
- Scatter plots that represent data as points on a graph
- Bubble charts that add a third data factor to the scatter plot
- Word clouds that represent word frequency by using words of different sizes
Multidimensional data visualization
Multidimensional data visualization represents two or more data variables as a single 2D or 3D image. Bar charts, pie charts, and stacked bar graphs are popular examples of these visualizations. For instance, a bar chart compares two or more data factors and demonstrates changes of one variable over a period of time. Pie charts visualize parts of the whole under each category.
Geospatial data visualization
Geospatial data visualization, such as heat maps, density maps, or cartograms, present data in relation to real-world locations. For example, a data visualization shows the number of customers who visit different retail store branches.
What are data visualization best practices?
Data visualization best practices add clarity, completeness, and accuracy to your data reports.
Using creative design elements can make your data visualization more engaging. You can use colors, shades, and shapes to add more detail to the visual. For example, you can use water-drop icons to represent data values on a water usage report.
Using a large volume of data in your analysis can improve the accuracy of the data visualization. More evidence increases confidence and also helps outliers to stand out. You can always include a data summary report or a consolidated data representation for an overview of a more detailed visualization.
Comparisons give context to data and reinforce the point you are making. They also make the data more actionable. For example, displaying current data after trialing a new idea alongside relevant data before the trial shows the reader how things were and how they could be.
What are the challenges in data visualization?
Data visualization presents some challenges that can lead to misrepresentation of information or exaggeration of certain facts.
Oversimplification of data
Data scientists must find a balance between data comprehension and communication. Oversimplifying the data can result in the loss of key information. For example, consider a scientific data report on academic performance. The report shows a bar chart indicating that academic performance has declined, while students' video game usage has increased in the last decade. The report concludes that video game usage has adversely impacted academics. However, the data visualization is oversimplified—it does not consider demographics and several other factors that also impact academic performance.
Human prejudice adversely impacts data visualization. The team creating data reports might bias results by preselecting data that suits their personal agendas. While data visualization tools are more accurate, the team operating them may unwittingly introduce bias through prejudiced data selection and cleaning. Hence, it is important that you include diverse teams and opinions in your data visualization efforts.
You can visualize unrelated data to create nonexistent correlations. Bad actors may use such inaccurate data visualization to justify harmful behavior or poor decision-making. For example, a team overspends on manufacturing equipment to support a supplier with a family relationship. They then justify the purchase by using data visualization reports highlighting how worker safety improved after the new equipment installation. However, several factors contributed to worker safety that had nothing to do with the new equipment.
What should you look for when selecting data visualization software?
There are several free and paid data visualization tools, and selecting the best one depends on your requirements.
Your data visualization software should integrate with your existing IT infrastructure and databases. It should also support several third-party data sources so you can directly import external data when needed.
Interactive reports improve big data analysis and help in pattern discovery by nontechnical users. They can filter, sort, or move data variables in an interactive chart as they plot data values. They do not have to depend on a technical team every time changes are suggested or required.
Data visualization tools can create additional vulnerability in your business intelligence system. They should have strong security features that limit access to unauthorized users and roles.
We recommend big data visualization tools that can handle massive datasets with ease. They should also have machine learning (ML) and artificial intelligence (AI) capabilities to automate data visualization tasks at scale.
How can AWS help with data visualization?
AWS has two main data visualization tools that you can use to make detailed reports on all types of data.
Amazon Managed Grafana
Amazon Managed Grafana is a fully managed service for open-source Grafana, a popular open-source analytics platform for querying, visualizing, and understanding your metrics no matter where they are stored. Amazon Managed Grafana natively integrates with AWS data sources in your AWS account. You can choose from various pre-built visualizations to quickly start analyzing metrics, logs, and traces without having to build a dashboard from scratch.
Amazon QuickSight is a cloud-native serverless business intelligence service that provides data visuals, interactive dashboards, and data analytics powered by ML. You can use it to discover hidden insights from your data, perform accurate forecasting, and unlock new monetization opportunities. QuickSight uses ML to generate accurate responses to natural language questions about data.
Get started with data visualization on AWS by creating a free account today.