Data visualization is crucial for any company that relies heavily on data. It visualizes information to make it simpler to comprehend, analyze, and draw conclusions for strategic use in business. An organization might get valuable insights from its data that it otherwise would have missed by not visualizing it.
What Is Data Visualization?
The term “data visualization” refers to visually presenting information on maps, charts, sparklines, infographics, waterfall charts, heat maps, or statistical graphs.
Images made from data that do not update in response to new or updated data are considered static visualizations. When data is updated, interactive visualizations update themselves accordingly.
To effectively extract valuable insights from data, it must be presented in a way that’s easily understood and analyzed. The appropriate parties may then use the results to make better choices in real-time.
What Do Data Visualization Tools Do?
Data visualization tools enable the visual representation of data by including features such as streaming data support, AI integration, embeddability, collaboration, interactive exploration, and self-service capabilities. Data scientists use these resources for both static and dynamic visualization.
What Makes A Good Data Visualization Tool?
Every data visualization tool on the market has a few similar characteristics. First, they are beneficial because of how easy it is to implement them. Software may be divided into two categories: those that are simple to use and those that are complicated and make it hard to see data.
Some are built in user-friendly ways and come with helpful documentation and tutorials. Despite their many redeeming features, some tools are simply not eligible for consideration on a “best of” list because they fall short in one or more key areas.
You’ll need to ensure the program can show massive datasets and diverse data types (waterfall charts, cone charts, scatter plots, etc.) without a hitch.
As expected, there will be competitors who frame the same information somewhat differently. Some data visualization programs are great at making some kinds of charts and maps but not others.
While different programs might specialize in other presentation methods, the best programs will allow you to present information in the way that best suits your needs. For example, waterfall charts are useful for quantitative analysis. A waterfall chart is a great way to present information if you’re a financial analyst.
There’s also the matter of costs. A higher price tag is sometimes good for a tool, but it does mean that it should provide more in the way of service, features, and value. So, what are the most important elements to consider when searching for a quality data visualization tool?
What To Consider When Selecting Data Visualization Tools
Here are the top considerations for selecting a data visualization tool in 2023.
Ease Of Use
An emphasis on ease of use facilitates rapid adoption by people with no technical background. Giving good default choices are a hallmark of high-quality data visualization tools.
However, in simplicity, you may lose some functionality and customization.
If you’re a non-technical user or a business user, ease of use should be one of your major considerations when selecting a data visualization tool.
Data Storytelling
Data sharing is at the heart of data storytelling tools. The goal is for the user to share their thoughts and ideas with others. The data can provide a complete picture when paired with additional information and visuals.
If you’re a consultant, subject matter expert, or business user, data storytelling tools are a good option for you.
Visual Analytic Tools
Data analysis is easier and more efficient using visual analytics tools. The visualizations reveal insights into the data’s structure, allowing the analyst to gain more nuanced insights into the data.
These tools are ideal for you if you’re a data analyst working on several data sets.
Flexibility
Flexibility-oriented visualization tools have extensive feature sets, allowing for full configuration control, granular adjustments to visualization design, and enhanced analytic capabilities.
Flexibility should be one of your main considerations if you’re a developer or an experienced data analyst.
Applications/Dashboards Creation
Data visualization tools for making apps and dashboards produce a program, dashboard, or other application to serve as the foundational building block. You can use them for assembling a set of graphic elements to convey more depth and breadth of data collection.
These data visualization tools are great for presenting results if you’re a consultant and for internal reporting.
Independent Visualization Tools
The charts you make with independent visualization tools are created individually. Each graph represents “island” data that you may analyze separately. These graphics are often integrated into a whole other webpage.
Independent visualization tools are great for students, data journalists, and PR managers.
Broad Data Platforms
The ability to visually interpret complex data will be a key feature of future platforms. Data management, governance, and modeling are all areas that such a system may support.
This is the perfect data visualization if you’re in the tech industry.
Focused Visualization Tools
Visualization-centric solutions often concentrate on what they do well. More specifically, none of these solutions aim to be the organization’s “one source of truth” for data.
Focused visualization tools are a great option for persons with direct access to data sources.
In the end, the purpose of data visualization tools is to aid in discovering useful insights inside large datasets. Remember why you’re doing this, and keep it in mind while you weigh your alternatives.
You can put together a group of experts to assist you in evaluating the problem. To better understand how the data visualization tools will be utilized in various scenarios, consulting with employees throughout the firm is a good idea.
Data Visualization Tools
Here are a few of the best data visualization software tools available on the market:
- Domo
- Zoho Analytics
- Looker
- Klipfolio
- Qlik Sense
- Tableau
- Microsoft Power BI
Be sure to factor in the type of data you’re dealing with, what you’ll be using the information for, and the audience you’re trying to reach before you select a data visualization tool. You should also consider how you want your data presented.
Some data presents best as a pie chart, while other types of data work best as a waterfall chart. You should know when not to use a waterfall chart. For example, you would likely not use a waterfall chart for geographic data.
Final Thoughts
From broad data platforms to focused visualization tools and everything in between, there is a lot to consider when selecting a data visualization tool as you enter 2023. The tool you ultimately choose depends on what you want to accomplish with your data.
Now that you know what to consider, you should be better prepared to choose the best data visualization tools in 2023 that suit your needs.