What are Data Visualization Tools?
If you handle tons of information daily, using tools for data visualization can help you represent gathered information and data with ease. Data visualization tools allow you to have a graphical representation via graphs, maps, and charts, enabling you to understand patterns, trends, and outliers easier and help you pull insights from the information.
Why Do You Need Data Visualization Tools?
Data visualization tools could mean the difference between saying something versus being heard. These tools can benefit you in terms of marketing, collaboration, and planning. Here key reasons why you need data visualization tools.
Amplifies Your Message
Any type of visualization gives a more significant impact on your audience than plainly written words. That is because mentally, visuals are easier to remember and tie firmly to memory.
Using data visualization tools for data science simplifies complex statistical data and basic statistical methods such as the mean, median, range, and more. It helps you deal with more complicated information and even enhances your memory.
Make Decisions Quicker
Online data visualization tools display your gathered data in a meaningful format, helping you make decisions faster by analyzing previously-recorded data and predicting future patterns. However, you should make data visualization tools comparison and see what tools provide advanced analytics, trend lines, and other crucial metric assessment features to aid in your decision-making ability.
Examine a Network with Ease
Visualization tools for big data play a huge role in the marketing industry. It helps you determine which specific demographic or audience to target with your message. Using data visualization tools online enables you to analyze the entire market's current state and lets you quickly identify bridges between clusters, audience clusters, influencers within groups, and outliers.
Keep Track of the Changes Over Time
Seeing changes over time in your investments, health data of patients, stocks, sales, and more is the most basic reason why you should use data visualization tools open source. It helps you see the changes made over time in your data, letting you make necessary adjustments or changes easier and see how the information trends over time.
Understand the Correlations of Gathered Data Easier
Identifying correlations or relationships is a valuable use of the best free data visualization tools because it's hard to determine the relationship between variables without a display. Using these tools in your data analysis can help you pinpoint the associations of two variables in your gathered information.
Plan Schedules Better
If you are working on a complex project and want to create a straightforward schedule on the project on a more organized matter, using free data visualization tools can help you resolve this complicated process by displaying each task within your project. It can also help you determine how long the project will take to complete.
A Clearer Understanding of Operations
Using free online data visualization tools help you process vast amounts of information at once, opening enormous windows of understanding into the operations of your business or agency. It measures the impact on your organization or provides you with detailed visual insight. If you are managing a small business or an empire, knowing the data visualization tools ranking and comparison will help you make quicker and more informed decisions.
Analyze Values and Risks
When determining complex metrics, it typically requires several variables, making it difficult for you to see precisely with a conventional spreadsheet. Data visualization tools make it easy for you to see even the most complicated metrics with methods such as color-coding formulas to indicate which opportunities are valuable and which are risky.
Understand Data Relating to Any Industry
Using free data visualization tools helps you understand information easier if you have a career in government, STEM field, history, or any niche. You don't have to worry about reading data in conventional word form or spreadsheets because using these tools can help anyone in the industries of sports, service, education, consumer goods, finance, marketing, and more.
Aids You in Decision Analysis
You do not make decisions on a whim, and decision analysis is needed if you want to make the best decision that is beneficial to you. Decision analysis is structured with the data and information available to help you make better decisions for your business or company. However, you should keep the data unbiased to make informed decision-making because biased details in your data visualizations can do more harm than good.
Core Features of Data Visualization Tools
Tools for data visualization help your companies, businesses, organizations, and enterprises display data in a structured and ordered format. To make it easier for you, we have highlighted the features that you must consider before choosing the best data visualization tools.
The best data visualization tools should have an efficient dynamic infrastructure to help you get insights better. This feature is critical, mainly if you handle large quantities of data because it requires more complex storage, management, and analysis. A good infrastructure will help you manage and configure extensive workloads without spending any additional expenses for other extensions to represent data more clearly.
High Integration Capability
The insights or results you interpret from visualization tools for big data can only help you if they are seamlessly integrated into the operational business systems and directly shown on the dashboard. The dashboard helps you quickly see the reasons for failure or success, predict forthcoming, view metrics associated with the failure and success, and infer methods or procedures to produce the desired result.
If your company wants to track several different datasets or if the hospital you are working for had a surge in outpatients and needs to monitor numerous health data, you should be able to customize which datasets appear prominently on the tool's dashboard. This feature helps you and your team prioritize different datasets in one sitting.
If you handle big datasets and complex information, data mining should be a feature your tool possesses. It is the process of studying large data sets to identify patterns and trends in it. Data mining helps you distill all the data in them and yield intuitive and straightforward visual reports.
Although this feature is not vital for most jobs, if you want the top data visualization tools that automatically explore and predict trends, you should consider the tool's artificial intelligence. The recent advancements in these tools let you analyze, explore, forecast trends, and suggest future actions based on your data's past trend.
Fast Discovery of Results and Insights
The top data visualization tools provide you with interferences from data analysis in real-time. If you want you to maximize your users' experience, your data visualization tool should be prompt and have features such as dynamic edit, update, delete, and other data optimization controls. If you are experiencing slow results, you can check if your tool has direct access to memory for prompt results and acceleration of complex data analysis.
Simple and Straightforward Dashboard
The dashboard of any data visualization tool is considered as the very face of the whole data visualization program, and it should be able to present all the critical information in a single glance. An excellent data visualization dashboard should be navigable for you to interpret data much easier; it also needs to be concise and present facts clearly and decipherable in one glance.
If you work with a team, there will be times when you need to connect with them, discuss your different insights, or have a conversation about the current data. The best data visualization tools should have a real-time interaction feature to help you and your team conclude things immediately instead of forwarding the gathered information via documents, files, and screenshots.
Another critical aspect of the dashboard that you should consider is that it should be able to summarize all the data that matters accurately, essentially helping you see a clearer picture of complex data in one glance. It should help you keep track of the top KPIs (Key Performance Indicators), vital trends you are monitoring, and other datasets that are important to your work or business in a clear visualization on the dashboard.
Quality Transferring of Visual Reports
Not every department needs you to analyze the big data your data visualization tools collect; most only need a specific part of the information to integrate with their particular applications seamlessly. However, keep in mind that the quality of the visual reports you transfer into different applications and platforms should be retained, and it should be interactive, allowing you to interpret the gathered data further.
Data Collection and Sharing
Importing raw data to online data visualization tools and then exporting the visual reports in various forms is something that you need to manage in the way you want. Some datasets can be placed into your tool even in their rawest form, while other datasets primarily need to be aggregated first if they are too large. To get the best of this feature, you should opt for a data visualization tool that can collect data from several sources and show it all at once in the same dashboard, allowing you to see the bigger picture in one place.
The feature of seamlessly transferring your visual reports into other applications or platforms you use is vital to use the power of visualization tools for big data. If you manage a team, you may want to have a data visualization software that has allows you and your team to use various media like graphs and charts into different applications to let you all work and collaborate more efficiently. The best free data visualization tools must allow for easy embeddability.
The quality of the visual reports of your tools for data visualization matters, but it should also be interactive—allowing you to investigate trends and insights quickly. Interactive data visualization also helps you interpret these trends and insights—signs of an interactive tool that it should enable you to move, filter, sort, and move different variables until you get your desired results.
Accurate Location and Geotagging
If you work in a business or industry where geolocation is essential, you need to see the level of the location intelligence of your data visualization tools. It should be able to let you know where the data is coming from; which states or regions are more active in using your services, and which areas need adjustments. This feature is most critical, especially if you need to track location-based KPIs because the tool should be able to layer data sets spatially and chronologically.
Types of Data Visualization Tools
Studies proved that we create more than two quintillion bytes of data daily. With so much information accessible at your fingertips, you must know how to organize these data into a more analyzable and actionable insight. That is why you should know what the best tools for data visualization to use.
Data visualizations that are considered as temporal are those who are linear and are one-dimensional. Temporal tools for data visualization feature lines that either stand-alone or overlap with one another, with a start and finish time.
Pros of Temporal
It features familiar charts
Charts in the temporal category are charts that you can recognize from school or your workplace, making it easier to understand complex data in one glance.
It gives you a quick analysis of data
Insights given by this type of visualization tool helps you quickly determine the range, maximum, minimum, gaps, and clusters in your gathered information.
It easily observes changes
Temporal data visualization tools help you observe changes in your data over time, making it easier for you to see where to make necessary adjustments.
Cons of Temporal
It can only handle minimal data values
Although it can accommodate larger data values, it is only generally ideal if you have less than 50 data values; otherwise, it can become confusing.
It requires your data range to be minimal
The range of your data should not be too broad since the graphs representing this type of data visualization tool can become messy and unorganized.
Data visualizations that are considered as hierarchical are those that order groups within larger groups. Hierarchical data visualization tools feature ring charts, sunburst diagrams, and the most common tree diagrams.
Pros of Hierarchical
It helps you show clusters of data easier
Hierarchical visualizations are ideal if you want to show clusters of data in one understandable format, especially if the data flows from one source.
It lets you consider several possible outcomes
This type of data visualization tool provides you with insights that let you consider numerous possible outcomes of a decision.
Cons of Hierarchical
They are more complex to understand
Hierarchical visualization data tools are more complicated and are harder to understand and interpret; you may have a hard time interpreting data, though you can use a tree diagram, which is a chart under the hierarchical category.
It is known that some datasets connect with different datasets on a deeper level. Network data visualization tools feature matrix charts, word clouds, node-link diagrams, and alluvial diagrams.
Pros of Network
It helps you demonstrate relationships between variables easier
The use of this data visualization tool helps you relate several visual data with one another, helping you understand correlations between datasets easier.
It improves collaboration between teams
It provides you and your team with collaborative working environments that allow you to share your different interpretation of data with ease.
It allows you to work on multiple projects
Network data visualization tools let you combine several structures into one and enable you to create a system that works for you the best and can help you work on active multiple projects at once.
Cons of Network
It takes longer to plan
The Network data visualization tool consists of more complex graphs that let you plan the whole structuring longer
Multidimensional data visualization has several dimensions, meaning there will always be two or more variables to help you create a three-dimensional data visualization. The tools feature scatter plots, pie charts, Venn diagrams, stacked bar graphs, and histograms.
Pros of Multidimensional
It breaks down a ton of data
Graphs and diagrams from this type of visualization data tool help you break tons of data down to the essential takeaways.
It provides you with more eye-catching visuals
Multidimensional data visualization tools provide you with more vibrant and attention-grabbing visuals, helping you interpret or display data more efficiently.
It emphasizes data on a few units
It summarizes complex data in only a few units, making it easier to make a comparison of different datasets.
Cons of Multidimensional
It can't compare a lot of data
Since the graphs that come with multidimensional tools aim for a more straightforward approach, you can only compare a few pieces of data in one sitting.
It can't help you observe changes over time
Multidimensional data visualization tools can't help you observe changes or trends that your data has undergone in a specific period.
Geospatial is also known as spatial visualization, and it relates to real physical locations that overlay maps with different types of data points. Geospatial data visualization tools feature flow maps, density maps, heat maps, and cartograms.
Pros of Geospatial (Spatial)
It helps you track the movement of variables
Using charts and diagrams in this type of data visualization tool helps you map out your variables' movement easier.
It gives you a good visual impression of data
Most of the charts in geospatial data visualization tools use lines and the width of the lines to display the volume of movement, providing you with an excellent visual impression of your gathered data.
Cons of Geospatial (Spatial)
Not all of the data is interpreted
Geospatial data visualization tools group information in categories so you can't see individual values.
Who Needs Data Visualization Tools?
Salespeople and Marketers
If you work in the marketing and sales industry, it is essential for you to keep track of your trends, especially in their online presence. Since most sales are now conducted via the internet, you should pay close attention to your sources of web traffic and see if you can generate revenue from your websites. Online data visualization tools help you overview your traffic trends in a specific period as a result of your marketing efforts.
If you work in logistics, you can use data visualization tools to help you understand and choose the best shipping routes for your clients globally, helping you save time and effort.
The healthcare industry has improved in recent years and now uses top data visualization tools to help you visualize patients' important health data much more accessible. Healthcare facilities use choropleth maps to help you see the number of conditions that area is facing; for instance, the mortality rate of heart disease and changes across specific territories.
Using data visualization tools for data science allows you to gain a better insight into your experimental data. The process is termed as scientific visualization, making the whole interpreting process so much easier.
Entrepreneurs, finance professionals, and investors should always monitor the performance of their decisions regarding investments or when deciding on buying or selling assets. Visualization tools for big data help you analyze the price movements over time and display critical data to you, such as derivatives, currencies, stocks, bonds, securities, and commodities, ultimately helping you detect trends.
The use of the best free data visualization tools helps scientists and researchers understand the complex data sets derived from their visualization libraries of the chosen programming languages and tools, and identify patterns or trends that can affect their whole research study. It would be best if you used open-source data visualization tools for interpreting complex data analysis.
Frequently Asked Questions
When evaluating the best free data visualization tools to ensure you get the best tools to help you interpret data, you should consider the following:
Sharing capability - See if other people can view and edit your visualization and data analysis with ease. An excellent data visualization tool should be able to let you share your visual reports with other people while keeping its quality.
Output - See if you can publish your visual reports to other platforms on the internet, create a PDF version of it, and easily embed them into other applications.
The most common types of data visualization that almost everyone is familiar with is the table and graph. The table data visualization consists of quantitative data placed into columns and rows, and are separated with categorical labels. The graph data visualization shows correlations or relationships among data variables and displays these values as visual objects, such points, lines, or bars.
Before deciding what data visualization tool you should use for your next project, first, you should be able to differentiate between exploratory graphics and presentation. Then the form of your data visualization should be able to follow its function with ease, draw your preferred display before choosing the tool, and tailor the data visualization tool for your target demographic or audience.
Data visualization skills are the ability to portray information or data in a picture or graphical format to identify or uncover patterns, correlations, and trends to help you interpret data easier to your audience. The significance of this process is referred to as data visualization skills.
We need to visualize information or data, especially complex ones, because some individuals or audiences may understand data in a visual format. It helps you tell a story to your audience much easier, capturing their undivided attention throughout your lectures, presentations, and speeches.
You can display a lot of data by using big data visualization tools, such as the line chart, bar chart, area chart, scatter plot chart, pie chart, pivot table, area map, indicator, and scatter map. These tools will pack your complex and big data in one simple and straightforward visual report and will help you understand and interpret your gathered data better.
Step-by-Step Guide on Using Data Visualization Tools
Step 1: Understand the data.
Before using the data visualization tool, make sure that you understand the data you are collecting and trying to visualize, which includes its size.
Step 2: Analyze the type of data.
You should know what type of data you are trying to visualize, and the information or message you want to portray via an organized structure and a proper arrangement via their categories.
Step 3: Filter information.
It would be best if you filtered out the most crucial information or data you have gathered, identifying which part or of the data is the most informative, and separating it from the different parts of data. This step is essential to ensure you encounter no confusion later on when using tools for data visualization.
Step 4: Input the gathered data.
Input the gathered data in a mathematical context by either data mining or statistics; both are practices of examining large databases to narrow down the complex data or attain new information.
Step 5: Choose a data representation.
This next step encourages you to choose the basic visual representation of your data, such as a tree diagram, flow map, bar graph, pie chart, line chart, bubble chart, or scatter plot.
Step 6: Improve your initial representation.
This step emphasizes that you should improve the initial representation to make it more transparent and visually engaging. It also involves the process of choosing and using different colors, sizes, scales, shapes, and labels to emphasize the key messages of your gathered data.
Step 7: Add the finishing touches.
Finally, you should add the finishing touches to your whole visual presentation and manipulate the data. You should ensure you have full control over what features or messages that you want your audience to see.