In a data-driven landscape, every decision is made based on data. However, with so much data accessible in our database, how can we express complex data in an easy-to-understand manner? That is the reason why data visualization is such an incredible way to see the story behind data. But don't cringe when hearing “data visualization” cause it sounds so technical and complicated. And do you wonder how useful it is?
This blog will show you the foundations of data visualization, and the types of data visualization that you can use to be more natural and professional.
Raw data do not always offer the best storytelling to find data-driven insights. Therefore, data visualization is a visually interesting way to simplify and show information so that you can concentrate on the most relevant details.
Data visualizations transform crucial insights from vast, complicated datasets into natural and comprehensible information. By creating visual elements such as charts, graphs, maps, and other interactive visualizations, it helps people in any field like business, science, finance, healthcare, and many others. And it is more convenient than analyzing data by using clunky spreadsheets more than ever.
A good data visualization should be understandable and eye-catching. Using graphical elements to map raw data is a wonderful approach to tell stories. Data visualization may be fun. But first, let's understand how to make your data visualization charts become actionable insights.
Each field project needs different types of charts or graphs. For example, you need to create a comparison chart. You need pie charts, bar or line graphs. You might also need to research trends. You can use a line graph, dual-Axis line graph, etc...Knowing the different aspects of each chart can help you find actionable insights in an understandable graphical format. We can discuss more about which is the best types of data visualization for your needs later.
In addition to selecting the appropriate types of charts, it is crucial to consider the audience's level of data literacy and their familiarity with the visualization techniques used, and the clarity of the message being conveyed. Clear labeling, appropriate color choices, and thoughtful data storytelling techniques can help you find your needed insight. That’s why we need to note the effectiveness of any data visualization. If you do it wrong, it will take you too long to complete the task.
When you have multiple tasks at once, you need an easy and effective reporting technique that enables you to get a clear point quickly. Do you know which is the best types of data visualization for you? We can explore it right now!
Line chart helps business forecast projections for future outcomes in several business use cases. It is primarily used to illustrate the values of something over time, such as minutes, hours, days, months, or years. When should you use line graphs? When you want to compare trends across different categories and display them all at once.
For example, in sales and revenue tracking, line charts are used to monitor sales and revenue figures over time. Companies can analyze their sales data to identify growth, trends, seasonality, or changes in customer behavior, this helps in forecasting and decision-making.
A column chart is quite a popular type of data visualization as it is simple to show a comparison among different sets of data in an easy-to-understand manner.
A column chart displays data labels along with the horizontal (X) axis and uses the vertical (Y) axis on the left side to represent measured metrics or values. Make sure to avoid data overcrowding and consider grouping or aggregating the data to make the chart more readable.
Column charts can also be used to monitor metrics such as monthly sales data, revenue per landing page, and others. Note that the combination of a column chart and line chart is a good choice when showing figures and an overall trend.
Column charts may limit your label length and comparison space. So, if you want to Compare 10 or more items or work with lengthier labels, this type of chart is for you.
In a horizontal bar chart, the horizontal axis (X-axis) represents the measured values or metrics. Meanwhile, the vertical axis (Y-axis) represents the categories or variables being compared. The horizontal orientation allows for longer labels to be displayed without overlapping, making it easier to read and interpret the data.
One of the most wonderful types of data visualization is pie chart. A pie chart is a circular data visualization that shows various categories or components of a whole as slices. The size of each slice of the pie chart reflects the proportion or percentage it represents, and each slice represents a certain category.
The best use case to use a pie chart is when you want to highlight the percentage of the total that each number possesses or when you want to know how a group is broken up into smaller parts.
One thing to remember is that you should limit the number of categories or slices to maintain clarity and readability. You should do so because too many slices can make it difficult to distinguish between them and may lead to visual clutter.
A table chart is a form of data visualization that presents information in a structured tabular format. It consists of rows and columns, where each row represents a specific data entry or record, and each column shows a different attribute or variable.
Table charts are perfect for representing complex data structures, such as hierarchical data or multi-dimensional datasets. They can display nested categories, subtotals, or aggregated values, enabling users to understand the relationships and dependencies within the data.
Mixed charts provide a flexible and dynamic way to present multiple datasets or variables in a single visual display. By combining different chart types, you can enhance the understanding of complex data relationships and convey meaningful insights to your audience.
If you want to show trends and Patterns, mixed charts can effectively display trends and patterns in data. By using line or area charts along with other chart types, you can depict changes over time or variations across categories more comprehensively.
A scatter plot, also known as a scattergram, is a powerful type of data visualization that displays different variables plotted along two axes. Unlike other chart types, a scatter plot does not utilize a category axis and instead uses value axes for both the X-axis and the Y-axis.
It is especially useful for a large dataset to analyze diverse data points and find similarities. They make it possible to spot outliers and give a clearer picture of the data's overall distribution.
For instance, you want to analyze the relationship between your company's sales revenue and advertising expenditure. By plotting sales revenue on the Y-axis and advertising expenditure on the X-axis, you can determine if there is a correlation between the two variables. This information can help you make informed decisions about allocating your advertising budget.
A common variant of the scatterplot is the bubble chart that displays three variables simultaneously. It is similar to a scatter plot but with an additional dimension represented by the size of the markers, creating a bubble-like appearance with different-sized circles (rather than single points).
Using a bubble chart instead of a scatter plot when you want to incorporate an additional dimension or variable into your visualization. For example, if you have a third variable that you want to represent visually using the size of the markers, a bubble chart is a better choice.
A map chart often called a choropleth map that represents data values using geographic regions or areas. Map charts are particularly useful when you have data that is inherently tied to geographic regions and want to visualize regional variations or patterns. They provide an effective way to communicate spatial data and support a data-driven decision-making process.
A Sankey diagram represents the flow or transfer of quantities or values between different entities or categories. It uses the thickness of arrows or lines to represent the magnitude of the flow, allowing for the visualization of complex relationships and the identification of major contributors or pathways such as analyzing flows, optimizing processes, making data-driven decisions, assessing sustainability or energy aspects,..etc.
Maybe the Sankey diagram is not popular for all fields, but it’s so useful for a particular target project. For instance:
Material or Resource Flows: it is used to visualize the number of materials or resources in an industry, such as supply chain and environmental systems, to demonstrate the production, delivery, and consumption of resources. It also helps identify which area is efficient or not efficient to improve.
User Journeys: Sankey diagrams identify different stages or touchpoints to illustrate the flow of users within a system. It also enhances user experience by analyzing user behavior, conversation rates, or interactions.
Tips: Dataflake provides this chart. Try to create your first Sankey diagram now.
After analyzing the basic types of charts with a set of concepts and some notes when applying proper data visualization, you may panic. Don't worry! Welcome to Dataflake
In the market where there are many data visualization platforms, you should take into account to choose the best for your organization. Dataflake tool also provides a flexible design canvas for both experts and novices alike. With a user-friendly and powerful solution designed to simplify your data visualization journey, our intuitive interface allows you to effortlessly select the right data visualization chart types for your project.
In addition to user-friendly UI, interactive visualization charts, and graphs, Dataflake may offer other features such as:
Email Delivery: It provides the function to deliver the right dashboards, visualizations, or insights directly to the right person. This feature also allows us to schedule on-demand reports via email and make sure that it stays up to date without needing to access them again.
Embedded Analytics: it allows users to easily include graphs, charts, or dashboards in other applications or websites. With this feature, customers may embed visualizations right into their current infrastructures, giving stakeholders access to data-driven insights.
Besides some features that are available, our tool also provides some interesting upcoming features that you can't ignore! You must be amazed at how these features cut half of your workload. Let's spend a few minutes to see what they are!
We understand that budget is a consideration for many businesses. That's why Dataflake offers a fraction of the cost to ensure that everyone should have the opportunity to transform their data into actionable insights.
However, it's free for unlimited usage now! So, why don't you try it out?
