Simply put, data visualization is the process of illustrating data to communicate a message. This way, raw data can become more actionable and insightful for readers and audiences.
A picture is worth a thousand words, and nowhere is this more true than in data analytics.
Some of the common ways to visualize data are using charts, graphs, images, and interactive maps – it's all about making complex numbers understandable.
Data visualization is a critical step in the data science process. it helps teams convey data more effectively to colleagues, stakeholders, and decision-makers.
Data analysts and data scientists use it to discover and explain patterns and trends — frequently using open-source programming languages, like Python, that enable complex data analysis.
So it almost goes without saying that, today, data visualization apps play a key role in becoming a data-driven company. Tools like Whatagraph champion the whole sector as an easy way to see and understand data. Once you have understandable data, you can start making more informed decisions.
In today's article, we'll go through a few excellent data visualization examples. Then, we'll analyze different types of data visualization and where they are used. Finally, we'll look at a few examples created using a professional tool. f
Visualizing your data is a skill set that fortunately can be learned. Even if your data tells a compelling story about your success, it won't mean much if it's visualized in the wrong way. Here are some tips to make your data visualization all that better.
That's the part where it gets a little dull. But it's worth the different types of data visualization to make the most of your data storytelling.
What do you think of when you hear data visualization? If you're thinking of pie charts or pictograms, you're on the right track. But these are barely scratching the surface when we look at the totality of data visualization.
There are a plethora of different ways you can visualize your data. At this point, you want to make sure your visuals fit your data, and sometimes a pie chart just won't do the trick.
Here's a list of the data visualization types to build a better story in your reports.
- Area Chart
- Bar Chart
- Box-and-whisker Plots
- Bubble Cloud
- Bullet Graph
- Choropleth Map
- Circle View
- Dot Distribution Map
- Gantt Chart
- Heat Map
- Highlight Table
- Node Link Diagram
- Polar Area
- Radial Tree
- Scatter Plot (2D or 3D)
- Text Tables
- Wedge Stack Graph
- Word Cloud
With all of these different storytelling methods, you may get a little overwhelmed thinking about where to apply them. So, where is data visualization actually used?
Data visualization is used in pretty much every industry you could think of. Making data actionable and visual is a universal positive.
Companies of all creeds and industries can benefit from understanding data better. You can present trends, outliers and results in a way that can essentially boost your business if acted upon.
The better you present your data, and the more understandable your points are, the more like you will be able to command sentiment and action. Whatagraph here is one of the tools that help you create visually appealing reports and monitor your data in real time.
But don't just take our word for it – below are a few data visualization examples made with Whatagraph.
Choosing from a ton of options can be difficult, so here are a few of our favorites from Whatagraph. These are some of the best data visualization examples that we could come up with. To keep it modern, we didn't include John Snow's Cholera map - no, not that John Snow.
Anyway, here are the examples:
These normally feature lines that either stand alone or overlap with each other, with a start and finish time.
These usually display clusters of information, especially if they flow from a single origin point.
Network visualizations typically demonstrate relationships between datasets without wordy explanations.
Naturally, multi-dimensional data visualizations have multiple dimensions.
Geospatial visualization generally relates to real-life physical locations, overlaying familiar maps with different data points.
Data visualization tools are types of software designed to visualize and present data. Each tool’s capabilities are different, but they all allow you to input different data sources and use different visualization techniques. most of these tools come with built-in templates that you can use for interactive data visualization.
Whatagraph is a tool that allows you to create personalized and automated visual marketing reports. Whatagraph supports more than 40 integrations and more than 95 pre-made marketing report templates.
Remove and add relevant metrics and show your clients different forms of data visualization with just a few clicks.
With Whatagraph, you don’t need a team of data scientists to spot correlations and merge data from multiple sources.
Integrate any platform and create a perfect customized dashboard to your client’s specifications.
Technically a spreadsheet software, not a data visualization tool. Still, Excel is capable of visual representations with the added bonus that Microsoft products are widely available and often used for decision-making at the enterprise level.
According to Microsoft tutorials, you can use Excel to design at least 20 chart types or graphical representations in spreadsheets.
These include bar graphs, line charts, and scatter plots, as well as more advanced ones like histograms and treemaps.
Still, there are limitations to what Excel can do. If you need a more capable tool for visualizing complex data but want to stay in the Microsoft ecosystem, Power BI is an excellent alternative.
Built specifically to analyze large data sets, the tool uses business intelligence to import data from various business processes and presents it in a range of visualization methods.
Google Charts is a popular option for data analysis professionals who need to create interactive data visualizations that live on the internet.
this tool offers 18 types of charts including line graphs, pie chars, geo charts, and area charts.
Of course, there are different data visualization platforms out there. But they all vary in complexity and depth of analysis. Your needs will need to be accounted for when checking out some of these different platforms.
Published on Dec 14, 2022
WRITTEN BYVytautas Pučka
Vytautas is a marketing specialist, always looking to find and share knowledge in data-driven marketing. He is excited to have discussions with his readers and debate new and innovative ideas about marketing.
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