Last Updated on : 28 Jan, 2022
Data visualization is the go to answer for processing large data sets. You would find not just scientists and data analysts depending on data visualization tools but also those working in the field of tech, finance, design and marketing.
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Found our list of Data Visualization Tools helpful? We’re here to help you make the right choice and automate your business processes. Let’s discover some of the essential factors that you must consider to make a smarter decision!
Data visualization in most basic terms is the accurate representation of data in any visual form such as charts, maps and graphs. Being a key part of data analysis process, the method relies upon images for showing the relationship between data. As more and more facts get represented through data visualization tools, the easier it is to identify patterns and trends within any data set.
Data visualization tools are used for gathering insights from multiple datasets. These insights rely on the visualization data structure method to help make simplified information available right at your doorstep instead of complex rows of indiscernible spreadsheets. Moreover communicating information from visualized data is far easy and quick.
Suppose there is a large set of data right in front of you and you have to draw critical business or market insights from it. In order to accomplish this, you would have to present this dataset in multiple formats like maps, graphs, charts and images so that every information communicated is easy to digest.
Data visualization tools do the exact same job of automating these visualizations for multiple data points. You would thus see paid and open-source visualization tools being exclusively used for generating sales insights, marketing reports, dashboards, investor's slide decks and other kinds of reports.
Data may be large and complicated, but analytics visualization tools are easy to use. With exceptional mapping capabilities and multiple import options, even those with no prior analytics expertise can use these applications.
Python visualization, therefore, helps with:
For better knowledge of the basics of Python data visualization, you need to be aware of the following key plots of this technique.
Line plots help present sequence and time series of a set of data. Here the x-axis represents regular intervals whereas y-axis is for observations made. Both the axis in line plots help denote ordering in between observations.
The bar chart is used in multiple categories for presenting relative quantities. Here x-axis is for categories and y-axis for the quantity of every category. These charts are used also for comparing the estimations.
Summarising the distribution of data samples is easy with Histogram Plot. X-axis here shows intervals in between observations as well as discrete bins, whereas y-axis highlights the count of observations made.
Boxplot summarises the distribution of data samples. The x-axis represents the data sample and y-axis stands for observation values. If you want to check the spread, skewness, outlying points, tile length and location of variables then boxplot is the right tool.
Scatter Plot showcases the relationship between the two paired samples of data. Scatter Plot shows the correlation or association between two existing variables, which can be further quantified using the matrixes available.
Interactive visualizations help drill down patterns from vast layers of data samples and details. Data specialists and marketers use these tools to improve the future outcome for their business. Let us see how exactly the data visualization software helps with big data analysis.
Data visualization tools simplify complex data into visual forms in real-time. All analyses and reports thus presented through visualization data structure processes present real-time scenarios for different metrics. Real-time data information helps analysts, leaders and managers make quick decisions based on the market situation and other competitive advantages.
Use case: Geographic map would be a good example here as it helps political parties generate data for visualizing voting patterns.
Big data visualization applications support complex data sets though single graphics and brief series. Fever charts and choropleth maps are some good examples of the same. With these simplifying mechanisms in place, the hassle of going through complicated chart types and static tables is greatly reduced.
Use case: In the healthcare industry, choropleth maps help determine key variables. For instance, checking the mortality rate from heart diseases across different regions.
This is one of the prime uses of big data visualization systems that end users can track connections between a business's performance and its overall operations. By using charts and graphs, it is easy to locate variances for spotting bottlenecks and improving the performance.
Use case: Scientists use data tools for getting insights from the available experimental data. This is critical for making scientific visualizations.
Data visualizations are used to spot trends for performing well in a competitive environment. With support from paid or open-source visualization tools, you can spot patterns for undertaking key marketing activities and making campaigning decisions accordingly. Even for making logistical decisions, the tools serve their function well.
Use case: For instance, a software company offering a 30-days free trial on signup can gauge its success with data presented in the visual form about conversions, not interested, registered for the trail but did not convert, etc. With support from paid or open-source visualization tools, you can spot patterns for undertaking key marketing and make campaign decisions accordingly.
Data visualization tools help efficiently use operational data for understanding marketing indicators and checking performance in real-time. Heat maps are one good example where operational data is being used to assess data in multiple geographic locations.
Use case: Finance professionals use data visualization while buying or selling assets. This is critical for tracking how investment decisions perform over time.
Analytics visualization or predictive analytics are being used to understand the lag in sales processes. Drill-down sales analytics are similarly used for finding a correlation between the underperformance of certain products and sales figures. Both methods are key procedures used for data visualization purposes.
Use case: Companies and organisations use predictive and drill-down analytics to understand the past sales record. This helps businesses predict how future sales would be in a period of time.
Those in the field rely on big data visualization tools for an accurate analysis of customer's sentiments. The entire mechanism of the data visualization system helps organizations delve into consumer's sentiments to see where their business would perform the best.
Use case: Sales & marketing teams use big data visualization tools for analyzing web traffic and planning their marketing efforts accordingly.
Visualization of data isn't a straight forward process as it may seem like. You need tools that discern well data sets with multiple entries for doing data visualization.
The best part about Looker is the real-time dashboard support for analyzing data. The software further supports SQL dialects for easily connecting with multiple databases. Integrated insights and business analytic intelligence help companies make effective data-driven decisions. What makes the software unique is the existing data science workflows as well as multi-cloud support.
Key features of Looker:
FusionCharts supports multiple maps and chart format options for effective data visualization. You can use this application for developing data dashboards that highlight visualizations of datasets side by side. You can take advantage of its cross-browser capabilities as well as responsive charts function.
Key features of FusionCharts:
The best part about using Datawrapper is even those with no prior programming knowledge can use it. Easily add interactive maps and charts once data import is complete. In this tool, you are free to use visualizations such as scatter plots, area charts, choropleths, bar charts, election donuts and symbol maps.
Key features of Datawrapper:
Use live feeds built-in within ChartBlocks data visualization tool for easily importing data from any available source. Additionally available are extensions and automated modules for particular data sources. You can also use the specific feature called the chart building wizard for importing data.
Key features of ChartBlocks:
You can prepare effective data visualizations for social media posts, marketing reports, maps, or infographics by using infogram. You can easily export easily interactive visualizations and embed these into the website of your choice. Also available are several maps and chart types for embedded visualizations. Inbuilt within these applications is a drag and drop editor to import additional sources of data.
Key features of Infogram:
To get more value out of the existing data available, more and more businesses are turning towards data visualization tools. Some good examples include foreseeing sales volume, factors impacting user’s behavior, identifying areas that need improvement, etc.
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Author: Kalpana Arya
Q1 - What is Tableau data visualization?
Tableau data visualization is used for business intelligence and insights. The tool helps by developing interactive charts and graphs that are available as worksheets and dashboards. You can easily use Tableau for connecting it to files and spreadsheets for accurate data visualization.
Q2 - What are the benefits of data visualization in R?
Only a few code lines are required for developing data visualizations in R. The benefits of using data visualization in R are:
Q3 - What are the best data visualization software?
Some of the best data visualization software solutions are:
USP: Robust in-chip processing
USP: Easy set up and click & drop UI
USP: Multiple data connectors
USP: Best for optimization of SQL queries
USP: Real-time analytics for IoT (internet of things)
Q4 - What are the best free data visualization tools?
Let us have a look at free data visualization tools for key business intelligence and insights.
Q5 - What are the different types of data visualization?
For further making data visualization effective, the entire process has been divided into five different parts. These are:
Examples-Time series sequences, line graphs and polar area diagrams
Examples- Ring charts, sunburst diagrams and tree diagrams
Examples- Alluvial diagrams, matrix charts and word clouds
Examples- Stacked bar graphs, Venn diagrams and pie charts
Examples- Heat map, density map and flow map