In our increasingly data-driven world, it’s more important than ever to have accessible ways to view and understand data. After all, the demand for data skills in employees is steadily increasing each year. Employees and business owners at every level need to have an understanding of data and of its impact.

That’s where data visualization comes in handy. With the goal of making data more accessible and understandable, data visualization in the form of dashboards is the go-to tool for many businesses to analyze and share information. 

In this article, we will discuss all you need to know about data visualization, why it is important, and how it can help your business grow. First, what is data visualization?

• What is Data Visualization and Why is it Important?
• How Data Visualization Works
• Data Visualization Tools
• Data Visualization Examples
• Data Visualization Techniques
• Data Visualization Jobs
• Data Visualization Course
• Data Visualization Software
• Data Visualization Python
• Data Visualization Types
• Data Visualization Best Practices
• Tableau Data Visualization
• Data Visualization Skills
• Data Visualization Book
• Future Trends in Data Visualization
• Importance of Data Visualization
• Data Visualization in Excel
• What are the 3 Main Goals of Data Visualization?
• What are the 4 Characteristics of Data Visualization?
• Charts in Data Visualization
• How Data Visualization is Transforming the Healthcare Industry
• How Data Visualization Help the Business
• Where to Learn Data Visualization
• Where Data Visualization is Used
• Why Data Visualization is Important in Machine Learning
• Why Data Visualization is Important in Data Mining
• Why Data Visualization is Important in Data Science
• Why Data Visualization is Important in Business Analytics
• Does Data Visualization Require Coding?
• How Can Data Visualization Improve Healthcare Decision Making?
• Data Visualization Analyst Salary
• Data Visualization Benefits
• Data Visualization Engineer Salary
• Data Visualization Interview Questions
• Data Visualization Jobs Salary UK
• Data Visualization Objectives
• Data Visualization Software Engineer
• Data Visualization Software Comparison
• How to Use Data Visualization for SEO

What is Data Visualization and Why is it Important?

Data visualization is the graphical representation of information and data. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for employees or business owners to present data to non-technical audiences without confusion.

Read Also: What is Green Marketing and Its Importance?

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.

Why is data visualization important?

Data visualization provides a quick and effective way to communicate information in a universal manner using visual information. The practice can also help businesses identify which factors affect customer behavior; pinpoint areas that need to be improved or need more attention; make data more memorable for stakeholders; understand when and where to place specific products; and predict sales volumes.

Other benefits of data visualization include the following:

• the ability to absorb information quickly, improve insights and make faster decisions;
• an increased understanding of the next steps that must be taken to improve the organization;
• an improved ability to maintain the audience’s interest with the information they can understand;
• an easy distribution of information that increases the opportunity to share insights with everyone involved;
• eliminate the need for data scientists since data is more accessible and understandable; and
• an increased ability to act on findings quickly and, therefore, achieve success with greater speed and fewer mistakes.

How Data Visualization Works

Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets. The term is often used interchangeably with others, including information graphics, information visualization and statistical graphics.

Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made. Data visualization is also an element of the broader data presentation architecture (DPA) discipline, which aims to identify, locate, manipulate, format and deliver data in the most efficient way possible.

Data visualization is important for almost every career. It can be used by teachers to display student test results, by computer scientists exploring advancements in artificial intelligence (AI) or by executives looking to share information with stakeholders. It also plays an important role in big data projects.

As businesses accumulated massive collections of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. Visualization tools were a natural fit.

Visualization is central to advanced analytics for similar reasons. When a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.

Data Visualization Tools

Data visualization tools can be used in a variety of ways. The most common use today is as a business intelligence (BI) reporting tool. Users can set up visualization tools to generate automatic dashboards that track company performance across key performance indicators (KPIs) and visually interpret the results.

The generated images may also include interactive capabilities, enabling users to manipulate them or look more closely into the data for questioning and analysis. Indicators designed to alert users when data has been updated or when predefined conditions occur can also be integrated.

Many business departments implement data visualization software to track their own initiatives. For example, a marketing team might implement the software to monitor the performance of an email campaign, tracking metrics like open rate, click-through rate and conversion rate.

As data visualization vendors extend the functionality of these tools, they are increasingly being used as front ends for more sophisticated big data environments. In this setting, data visualization software helps data engineers and scientists keep track of data sources and do the basic exploratory analysis of data sets prior to or after more detailed advanced analyses.

The biggest names in the big data tools marketplace include Microsoft, IBM, SAP and SAS. Some other vendors offer specialized big data visualization software; popular names in this market include Tableau, Qlik and Tibco.

While Microsoft Excel continues to be a popular tool for data visualization, others have been created that provide more sophisticated abilities:

• IBM Cognos Analytics
• Qlik Sense and QlikView
• Microsoft Power BI
• Oracle Visual Analyzer
• SAP Lumira
• SAS Visual Analytics
• Tibco Spotfire
• Zoho Analytics
• D3.js
• Jupyter
• MicroStrategy
• Google Charts

Data Visualization Examples

In the early days of visualization, the most common visualization technique was using a Microsoft Excel spreadsheet to transform the information into a table, bar graph or pie chart. While these visualization methods are still commonly used, more intricate techniques are now available, including the following:

• infographics
• bubble clouds
• bullet graphs
• heat maps
• fever charts
• time series charts

Some other popular techniques are as follows.

Line charts. This is one of the most basic and common techniques used. Line charts display how variables can change over time.

Area charts. This visualization method is a variation of a line chart; it displays multiple values in a time series — or a sequence of data collected at consecutive, equally spaced points in time.

Scatter plots. This technique displays the relationship between two variables. A scatter plot takes the form of an x- and y-axis with dots to represent data points.

Treemaps. This method shows hierarchical data in a nested format. The size of the rectangles used for each category is proportional to its percentage of the whole. Treemaps are best used when multiple categories are present, and the goal is to compare different parts of a whole.

Population pyramids. This technique uses a stacked bar graph to display the complex social narrative of a population. It is best used when trying to display the distribution of a population.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data.

Here are some important data visualization techniques to know:

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart, or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis.

Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot, or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart, or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot. A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts, or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

Data Visualization Jobs

For each of these jobs, a bachelor’s degree in computer science, information technology, statistics, business administration, or graphic design are highly recommended. In most cases, earning a master’s degree can boost your ability to land better, higher-paying job opportunities.

1. Data visualization engineer

Data visualization engineers (or specialists) are responsible for the design and development of data visualizations and dashboards using data sets from various sources. They are experts in data and storytelling and whizzes at business intelligence (BI) tools, often creating user-friendly dashboards for business teams.

Data visualization engineers may also write and test analytics platform code, toggle with software tools like Tableau or Looker to produce dashboards, and fix bugs whenever necessary.

Annual median salary: $94,955

2. Data analyst

As a data analyst, you’ll be in charge of collecting, cleaning, and interpreting data sets to answer a question or solve a problem within an organization. Data analysts can be found across industries as varied as business, finance, science, medicine, government, and criminal justice.

Questions that data analysts might answer include: What segment of customers should be targeted in this advertising campaign? Which age group is most vulnerable to COVID-19? Data visualization skills will enable you to present this information to the team with graphs and charts.

To become a data analyst, you’ll want to learn technical skills such as Excel, R or Python programming language, statistics, and SQL.

Annual median salary: $63,943

3. Data scientist

Data scientists look for trends and patterns in data to help organizations make informed decisions about their operations, marketing, and more. They create algorithms and data models to forecast outcomes, sometimes adopting machine learning techniques to improve products or tools.

Once data scientists are comfortable with their outcomes, they’ll present their findings and recommendations to stakeholder teams, employing data visualizations to help make a compelling case. This job has a growth rate of 31 percent, which is much faster than the average for all occupations (8 percent).

Annual median salary: $100,209

4. Business intelligence analyst

Business intelligence analysts analyze and interpret data specific to businesses, such as sales revenue, market trends, and customer engagement metrics. BI analysts are always looking for potential improvements that can be made to drive business profitability and relevance in the market. They’ll share their findings with the team, crafting reports to present to other teams or clients on how to grow the business.

BI analysts can ascend into a managerial role, where they can earn a higher median salary of $110,321.

Annual median salary: $75,799

5. Analytics manager

Analytics managers are in charge of translating data into actionable insights for an organization. They typically lead a team of data analysts, coaching them to complete data analytics projects with accuracy and efficiency. Together they’ll develop strategies, track their performance, and oversee analytics operations. Those analyses will be transformed into reports and communicated to stakeholders.

Annual median salary: $108,200

Data Visualization Course

Online courses offer an effective medium to learn the right skills and increase your employability. We’ve rounded up this list of Best Online Data Visualization courses, certifications, classes, training programs and tutorials that you can take this year. All of these are available online, some are free while some are available at minimal costs. These cover Data Visualization with Tableau, Python, R, d3.js, Excel etc.

1. Data Visualization with Tableau Certification by UCDavis (Coursera)

This Tableau training for Data Visualization has been developed by University of California in collaboration with Tableau and offered via Coursera platform. It is one of the best combinations of theory and practical training in Tableau that starts with the fundamental concepts of data visualization, and various tools that Tableau has to offer and builds from there to finally creating multi-frame data stories in Tableau.

This data Visualization training program leverages Tableau’s library of resources to teach learners the best practices for data visualization and data storytelling. It comprises of following 5 courses:

1. Fundamentals of Visualization with Tableau – This course introduces learners to data visualization and the Tableau interface. It also covers how to prepare and import data into Tableau.
2. Essential Design Principles for Tableau – This course teaches how to analyze and apply essential design principles to your Tableau visualizations.
3. Visual Analytics with Tableau – This course delves into the tools Tableau offers in the areas of charting, dates, table calculations and mapping.
4. Creating Dashboards and Storytelling with Tableau – This course teaches how to create dashboards to identify the story within your data, and how to use Storypoints to create a powerful, impressive and effective story for your audience.
5. Data Visualization with Tableau Project – This is a capstone project where learners are required to create a single-frame viz or multi-frame data story that is then shared on Tableau Public.

This is a beginner level Data Visualization using Tableau certification program, and is an excellent fit for anyone who is comfortable working with data and datasets and wants to get started with learning data visualization and tableau. No prior technical or analytical background is required to enrol in this specialization.

2. Data Visualization with Python by IBM (Coursera)

This Data Visualization online course has been created by IBM and is available on Coursera. It is rated amongst the top data visualization with Python courses with more than 85K students already enrolled in the course. It teaches learners how to leverage Python to visualize data to enable them to extract information, better understand the data, and make more effective decisions. It covers several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium that are used to present the data visually.

The course is spread over 3 weekly modules requiring approximately 18 hours of total effort to complete. These modules cover the following concepts:

• Introduction to data visualization tools
• Basic plotting with Matplotlib
• How to read csv files into a pandas dataframe and process and manipulate the data in the dataframe
• How to generate line plots using Matplotlib
• Specialized Visualization Tools like area plots, histograms, bar charts, pie charts, box plots, scatter plots and bubble plots etc. and creating them with Matplotlib
• Advanced visualization tools such as waffle charts and word clouds and how to create them
• Visualization library Seaborn and how to use it to generate regression plots
• Geospatial data and visualizing it using Folium library
• How to create Choropleth maps
• How to use Folium to create maps of different regions of the world and superimpose markers on top of a map

Upon completing this data visualization training course, learners are able to take any seemingly meaningless data set and present it in a form that others can comprehend well and make sense of. It is intended for intermediate users with some experience in Python and handling data.

This course is a part of following Data Science specializations and professional certificate programs by IBM. When you complete this course, it will be counted towards your learning in these programs.

• IBM Data Science Professional Certificate
• IBM Applied Data Science Specialization

3. Data Visualization and Communication with Tableau by Duke University (Coursera)

This Course on Data Visualization with Tableau is offered by Duke University as part of its Excel to MySQL: Analytic Techniques for Business Specialization. The goal of this course is to prepare learners to become an expert at communicating business-relevant practical implications of their data analyses.

It teaches them to streamline their analysis by asking the right questions and use Tableau to convey critical findings. Students learn Storyboarding skills and how to visualize the results of data analysis or machine learning models in Tableau, so that it can be better understood by both technical and non-technical stakeholders.

Tableau is the most popular visualization tool in the market. This course explores various Tableau features that can be used to create effective visualizations that convey conclusions clearly and directly. Spread over 5 weeks, the course is approximated to require around 25 hours to complete. It includes a peer-reviewed final project that assesses a student’s learning of the subject. The topics covered in the course are:

• Creating graphs that are used in business analysis
• Transforming data in Tableau
• Designing business dashboards with Tableau
• Creating and delivering compelling business presentations
• How to craft the right questions to ensure your analysis projects succeed
• Writing calculations and equations in Tableau
• Adapting visualizations to make them maximally communicative

4. Tableau Training and Certification Courses (Udemy)

Tableau is a very powerful Data Visualization and Analytics tool. With increasing focus on data and data-driven technologies, knowledge of tools like Tableau is becoming a must skill for anybody working in Big Data, Data Analytics or Data Science. Udemy offers a good range of courses to learn Tableau to understand, handle and interpret data better.

From introductory courses well-suited for beginners to advanced ones for people already working in the field of data visualization, these Tableau tutorials can be availed at a minimal cost to learn Tableau online at one’s own pace and schedule.

Of all Udemy Tableau courses, particularly noteworthy is Tableau training series by Kirill Eremenko (a very well regarded instructor with around 1.5 million students on Udemy) and SuperDataScience Team, which includes following two courses.

• Tableau 2020 A-Z: Hands-On Tableau Training for Data Science
• Tableau 20 Advanced Training: Master Tableau in Data Science

Together these two Tableau courses have more than 270000 students enrolled with an average rating of 4.6 and favourable reviews. The series has more than 18 hours of video content and focuses mostly on the coverage of Tableau tool, rather than the theory of data visualization.

5. Udacity Data Visualization Nanodegree Certification (Udacity)

This Data Visualization program offered by Udacity is a world-class, cutting edge training program to build data visualization and communication skills. Udacity has collaborated with Tableau, the most popular data visualization tool, along with an excellent group of industry professionals with extensive experience to create curriculum that focuses on most in-demand skills. The program teaches learners to create clear and impactful data visualizations and use their analysis to make data-driven recommendations.

A lot of focus of this Data Visualization certification program is on building storytelling skills. Since, stories offer a powerful way to draw in the audience, make sense of the data and help businesses make better decisions, this program teaches learners how to weave data into stories and visuals using various tools and techniques. Two data visualization platforms Tableau and Flourish are covered in the program.

The program comprises of four courses that cover the following topics:

• Introduction to Data Visualization
• How to select the most appropriate data visualization for an analysis
• How to create interactive and engaging Tableau dashboards
• Design and create a dashboard in an enterprise environment
• End to end process for Data Storytelling
• Advanced data visualization and storytelling techniques
• How to add interactivity and other visual elements to a story
• How to add animation and narration with Tableau Pages and Flourish

Throughout the program, students build a portfolio of high-quality projects to showcase the data visualization and storytelling skills that they learn.

This program is an excellent fit for people with data background who wish to maximize the impact of their analyses or those with business background who want to persuade others to make more data driven decisions. It requires students to have basic skills in data analysis including spreadsheets and descriptive statistics.

Data Visualization Software

Data visualization software provide data visualization designers with an easier way to create visual representations of large data sets. When dealing with data sets that include hundreds of thousands or millions of data points, automating the process of creating a visualization, at least in part, makes a designer’s job significantly easier.

These data visualizations can then be used for a variety of purposes: dashboards, annual reports, sales and marketing materials, investor slide decks, and virtually anywhere else information needs to be interpreted immediately.

1. Tableau

One of the most widely used data visualization tools, Tableau, offers interactive visualization solutions to more than 57,000 companies.

Providing integration for advanced databases, including Teradata, SAP, My SQL, Amazon AWS, and Hadoop, Tableau efficiently creates visualizations and graphics from large, constantly-evolving datasets used for artificial intelligence, machine learning, and Big Data applications.

2. Dundas BI

Dundas BI offers highly-customizable data visualizations with interactive scorecards, maps, gauges, and charts, optimizing the creation of ad-hoc, multi-page reports. By providing users full control over visual elements, Dundas BI simplifies the complex operation of cleansing, inspecting, transforming, and modeling big datasets. 

3. JupyteR

A web-based application, JupyteR, is one of the top-rated data visualization tools that enable users to create and share documents containing visualizations, equations, narrative text, and live code. JupyteR is ideal for data cleansing and transformation, statistical modeling, numerical simulation, interactive computing, and machine learning. 

4. Zoho Reports

Zoho Reports, also known as Zoho Analytics, is a comprehensive data visualization tool that integrates Business Intelligence and online reporting services, which allow quick creation and sharing of extensive reports in minutes. The high-grade visualization tool also supports the import of Big Data from major databases and applications. 

5. Google Charts

One of the major players in the data visualization market space, Google Charts, coded with SVG and HTML5, is famed for its capability to produce graphical and pictorial data visualizations. Google Charts offers zoom functionality, and it provides users with unmatched cross-platform compatibility with iOS, Android, and even the earlier versions of the Internet Explorer browser.

6. Visual.ly

Visual.ly is one of the data visualization tools on the market, renowned for its impressive distribution network that illustrates project outcomes. Employing a dedicated creative team for data visualization services, Visual.ly streamlines the process of data import and outsource, even to third parties.

7. RAW

RAW, better-known as RawGraphs, works with delimited data such as TSV file or CSV file. It serves as a link between data visualization and spreadsheets. Featuring a range of non-conventional and conventional layouts, RawGraphs provides robust data security even though it is a web-based application.

8. IBM Watson

Named after IBM founder Thomas J. Watson, this high-caliber data visualization tool uses analytical components and artificial intelligence to detect insights and patterns from both unstructured and structured data. Leveraging NLP (Natural Language Processing), IBM Watson’s intelligent, self-service visualization tool guides users through the entire insight discovery operation.

9. Sisense

Regarded as one of the most agile data visualization tools, Sisense gives users access to instant data analytics anywhere, at any time. The best-in-class visualization tool can identify key data patterns and summarize statistics to help decision-makers make data-driven decisions.

10. Plotly

An open-source data visualization tool, Plotly offers full integration with analytics-centric programming languages like Matlab, Python, and R, which enables complex visualizations. Widely used for collaborative work, disseminating, modifying, creating, and sharing interactive, graphical data, Plotly supports both on-premise installation and cloud deployment. 

Data Visualization Python

Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends, and correlations that might not otherwise be detected can be exposed.

Python offers multiple great graphing libraries packed with lots of different features. Whether you want to create interactive or highly customized plots, Python has an excellent library for you.

To get a little overview, here are a few popular plotting libraries:

• Matplotlib: low level, provides lots of freedom
• Pandas Visualization: easy to use interface, built on Matplotlib
• Seaborn: high-level interface, great default styles
• plotnine: based on R’s ggplot2, uses Grammar of Graphics
• Plotly: can create interactive plots

Data visualization in python is perhaps one of the most utilized features for data science with python in today’s day and age. The libraries in python come with lots of different features that enable users to make highly customized, elegant, and interactive plots.

Matplotlib

Matplotlib is a visualization library in Python for 2D plots of arrays. Matplotlib is written in Python and makes use of the NumPy library. It can be used in Python and IPython shells, Jupyter notebook, and web application servers. Matplotlib comes with a wide variety of plots like line, bar, scatter, histogram, etc. which can help us, deep-dive, into understanding trends, patterns, correlations. It was introduced by John Hunter in 2002.

Seaborn

Seaborn is a dataset-oriented library for making statistical representations in Python. It is developed atop matplotlib and to create different visualizations. It is integrated with pandas data structures. The library internally performs the required mapping and aggregation to create informative visuals It is recommended to use a Jupyter/IPython interface in matplotlib mode.

Bokeh

Bokeh is an interactive visualization library for modern web browsers. It is suitable for large or streaming data assets and can be used to develop interactive plots and dashboards. There is a wide array of intuitive graphs in the library which can be leveraged to develop solutions. It works closely with PyData tools.

The library is well-suited for creating customized visuals according to required use-cases. The visuals can also be made interactive to serve a what-if scenario model. All the codes are open source and available on GitHub.

Altair

Altair is a declarative statistical visualization library for Python. Altair’s API is user-friendly and consistent and built atop Vega-Lite JSON specification. Declarative library indicates that while creating any visuals, we need to define the links between the data columns to the channels (x-axis, y-axis, size, color). With the help of Altair, it is possible to create informative visuals with minimal code. Altair holds a declarative grammar of both visualization and interaction.

plotly

plotly.py is an interactive, open-source, high-level, declarative, and browser-based visualization library for Python. It holds an array of useful visualization which includes scientific charts, 3D graphs, statistical charts, financial charts among others. Plotly graphs can be viewed in Jupyter notebooks, standalone HTML files, or hosted online. Plotly library provides options for interaction and editing. The robust API works perfectly in both local and web browser mode.

ggplot

ggplot is a Python implementation of the grammar of graphics. The Grammar of Graphics refers to the mapping of data to aesthetic attributes (colour, shape, size) and geometric objects (points, lines, bars). The basic building blocks according to the grammar of graphics are data, geom (geometric objects), stats (statistical transformations), scale, coordinate system, and facet.

Using ggplot in Python allows you to develop informative visualizations incrementally, understanding the nuances of the data first, and then tuning the components to improve the visual representations.

Data Visualization Types

Part of the strategy of visualizing data is choosing what type of data visualization to use. The trick is to select the one that will best represent your data’s message and story. There are many types of data visualization. The most common are scatter plots, line graphs, pie charts, bar charts, heat maps, area charts, choropleth maps and histograms.

Some of the most common types of data visualization chart and graph formats include:

1. Pivot tables

You might not think of tables as a form of data visualization, but they are! When dealing with vast repositories of information—ones that are too large to easily comprehend—pivot tables help us summarize key statistics in a single view. The type of information collected in pivot tables might include sums, means, or other numerical summaries.

While pivot tables aren’t always the most visually inspiring form of data viz, they are useful in the right context. For instance, highlight tables, as shown in the image, use different shades or colors to easily flag the highest and lowest values in a dataset. Sometimes, this is all you need, making pivot tables a basic but effective form of data viz. They are also commonly used to underpin more complex forms of data visualization, hence making it on to our list.

2. Boxplots

Another useful (if not particularly flashy) type of descriptive visualization is the boxplot (also known as a box-and-whisker plot). Like pivot tables, boxplots are useful for visualizing a dataset’s key statistics. We can use them to represent minimum and maximum values, the median value, and the lower and upper quartiles (i.e. the median of the lower and upper halves of the data).

Boxplots are what is known as ‘non-parametric.’ This means they display variation in a data sample without making any assumptions about the data’s distribution. This makes them useful for exploratory and explanatory data analysis, i.e. getting to understand a dataset’s key features before drawing any broad conclusions about it.

3. Scatterplots

A scatterplot (also known as a scattergraph, scattergram, or scatter chart) displays the relationship between two variables on an x- and y-axis. Each item of data is shown as a single point, creating the chart’s visual ‘scatter’ effect. When there are three interrelated data points (i.e. if there’s a z-axis) 3D scatterplots are also possible.

Scatterplots are best used for large datasets where time is not a significant factor. For instance, a simple scatterplot might measure people’s weight against height. This would help identify any correlation between the two measures.

However, because other factors affect the data (e.g. people’s weights are also related to their diet) scatterplots are best for inferring relationships between variables rather than drawing firm conclusions. Nevertheless, they are an excellent tool for hypothesis creation.

A common variant of the scatterplot is the bubble chart. Displaying different-sized circles (rather than single points), bubble charts represent three dimensions of data, rather than the usual two.

4. Line graphs

Line graphs, or line charts, are a simple but effective staple for representing time-series data. They are visually similar to scatterplots but represent data points separated by time intervals with segments joined by a line. This allows for quick observation of features like acceleration (when the line goes up), deceleration (when the line goes down), and volatility (when the line moves up and down erratically).

While the simple line graph shown represents a single dataset, more complex line graphs may overlay several lines to represent different data. This is useful for spotting correlations or deviation. A common example of a line graph in action is the measure of stock market behavior or resource costs over time, e.g. the price of gold over several years.

5. Area charts

Area charts, similar to line charts, are also used for tracking data over time. However, in an area chart, the space between the plotted line and the x-axis is shaded or colored for visibility. This is particularly useful for highlighting the difference between multiple variables, or for measuring overall volumes (rather than highlighting the difference between discrete data points).

For example, in the image provided—which is known as a stacked area chart—the most important factor to note is the volume of products sold in each country, which is represented by the shaded areas. A common variant on the area chart is the streamgraph, where data is plotted around a central axis to minimize so-called ‘wobble.’

6. Bar charts

Another common visualization—one you’ll no doubt be familiar with from school—is the bar chart. Bar charts are a simple but highly effective way of plotting categorical data against discrete values. The heights (or widths) of the bars are in direct proportion to the values they represent. This makes bar charts an excellent way of comparing discrete variables at a glance.

Some bar charts cluster bars into groups of two or three (or more) allowing you to compare numerous variables at different points in time. Another variation is the stacked bar chart, which divides each bar into separate sub-bars, one stacked on top of another. This allows for the introduction of additional variables.

7. Histograms

Although visually similar to bar charts, histograms are not the same thing. Bar charts measure categorical data, while histograms measure the distribution of numerical data, i.e. the frequency with which a discrete data point appears in a dataset.

In a histogram, each bar represents how often a data point falls within a given range. For example, each column might represent different age groups (20 to 29, 30 to 39, and so on). This makes histograms excellent for summarizing large amounts of continuous data without needing to inspect every single value.

If you struggle to distinguish between bar charts and histograms, look out for spacing—there should always be a space between bars on a bar chart (to signify that the categories are discrete) while there should be no gap between the bars on a histogram (signifying that the data are continuous). You’d be surprised how often people get this wrong though, so keep your eyes peeled!

8. Pie charts

Another visualization you may remember from school is the pie chart. While pie charts are similar to bar charts in that they represent categorical data, this is where the similarities end. The main difference (besides how they look) is that bar charts represent numerous categories of data, while pie charts represent a single variable, broken down into percentages or proportions.

Each ‘slice of the pie’ in a pie chart is proportional to the quantity it contributes to the whole, i.e. the entire circle. For this reason, pie charts are best-suited to data that is split into about five or six categories…add more than that and it quickly becomes too complex to effectively represent the data.

9. Network graphs

As sources of data grow more complex and interconnected, so must the visualizations we use to represent them. Enter network graphs, which are used to show how different elements of a network relate to one another. Each element in a network graph is represented by an individual node, interconnected to related nodes via lines. This approach is excellent for visualizing clusters within the larger whole—patterns that can otherwise be hard to spot.

The joy of this type of visualization is that you can represent networks with varying degrees of complexity without impacting the usefulness of the visualization. In fact, the more elements and connections a diagram includes, the more likely it is to help you spot the larger clusters hidden in the data.

10. Geographical maps

One of the most versatile types of data visualization is the geographical map, which can bring life to a whole range of different location-specific data. A common example is the distribution of vote share during an election, like that shown in the image.

Maps can be used in diverse ways. For example, geographical heat maps use color to show the variation of a particular element over a given area, offering visual clues about data distribution. A simple example is the social media company Snapchat, which uses heat maps to show where the highest density of snaps are being shared.

Other types of maps include dot distribution maps (which combine the idea of a scattergram with a map) and cartograms, where the size of geographical locations are distorted to match the proportion of a selected variable, e.g. world population.

Data Visualization Best Practices

All of us want our Business Intelligence (BI) and analytics solutions to transform data into insights. However, the data is handy only if one understands and shares it in a better way. In this section, we will be listing the best practices of Data Visualization and here are some of them:

• Entitle a specific audience and mark their needs
• Choose the right visual (The Key)
• Apply Text Carefully and Intentionally
• Use the predictable pattern for layouts
• Select the right data visualization tool
• Use attractive colors for telling data stories
• Use attractive and straightforward Dashboards
• Try to make visualization inclusive
• Examine Business Insights

Tableau Data Visualization

Tableau is a Data Visualisation tool that is widely used for Business Intelligence but is not limited to it. It helps create interactive graphs and charts in the form of dashboards and worksheets to gain business insights. And all of this is made possible with gestures as simple as drag and drop!

What Products does Tableau offer?

You don’t need to know much to use Tableau, but still, a basic awareness of all the types of graphs such as bar graph, line charts, histograms, etc is preferred.

Along with that, it will be beneficial if you possessed some basic understanding of database management ( datatypes, joins, drill down, drill up, etc ) too.

Data Visualization Skills

To be a good data storyteller and communicator of data, you need a collection of “soft skills” that are overlooked when people think of data analysis. Here are the 5 skills we think are most important:

1. Ask the tough questions

The common trap for data analysts is to become an order taker. Your boss asks a series of off-the-cuff questions, and you run off to answer them — only to find that the answers don’t get to the crux of the issue. The cycle continues.

A skilled analyst asks tough questions before they start getting into the data. “What actions could we take if you knew that answer?” Nancy Duarte, a leader in business storytelling, emphasizes the phrase “therefore we need to…” appended to the results of an analysis. Know the purpose of your work and how it will effect change.

2. Develop audience understanding

As a data analyst, you need to get into the shoes of your audience. What are their priorities? What actions can they take in their role? The more you know about the people who will consume your insights, the more you’ll be able to shape them in a way that they are useful.

3. Basic visual design skills

When you are sharing your analysis, you will impress a lot of people if you apply some basic design principles.

Learn how to properly use color and contrast in your charts, apply our Simple Font Framework to make your text look great, and remove distracting visual elements (e.g. chartjunk). You’ll be considered the data artist in many organizations.

4. Edit and simplify to preserve attention

In your role as a data analyst, you will be challenged to get the attention of your manager or peers when you share data. Therefore it is incumbent on you to bring focus and clarity to your message. You’ll want to remove data that is merely interesting and guide attention to the data that is most actionable.

5. Practice rapid prototyping

When you first create a report, dashboard, or data presentation, you will inevitably leave some important questions unanswered. Your audience won’t fully understand your message or how to read you data visualizations. That’s ok. Putting data solutions in front of customers and then learning from their reactions is part of the process. Make sure to listen carefully and move quickly to respond.

6. Gather feedback

Delivering a presentation, analysis, or report can feel like the finish line. Often it is not. As noted above, you may need more cycles of refinement to ensure your hard work is having the impact it deserves.

Seek out feedback: Did they understand your metric definitions or the charts you used? Did they interpret the data in the same way as you? The more you ask, the more you will learn and grow.

7. Learn storytelling with data

Storytelling with data is a powerful skill for analysts to connect with their audiences. A collection of lessons on data storytelling includes guidance around structuring your data stories, choosing the right metrics, and writing a guided narrative.

Data Visualization Book

The field of online data visualization is growing, and whether you’re a data viz expert or just getting started, there is a wide range of books that will help you learn new skills and remain ahead of the pack.

Not sure where to start? A mere Amazon search of this topic returns over 15k items. That’s a colossal number of books on visualization. And while some of them we consider the best books on data visualization, some are really not.

1. “Information Dashboard Design: Displaying Data for At-a-glance Monitoring” by Stephen Few

This is a fantastic resource packed full of examples of good and bad dashboards. In explaining how to create great dashboards, Stephen Few also gives a great introduction to the principles of the theory of data visualization and design. We would also recommend any of Stephen Few’s other books, but this one is the most accessible.

This book focuses less on data visualization itself and more on leveraging visualization techniques to your advantage in order to efficiently and critically monitor data at a glance. Dashboards are a common way to keep track of multiple sets of data, and Few’s goal is to help make sure dashboards aren’t unwieldy and inefficient, as they often can be. In this book, he points out common mistakes with dashboard design, as well as best practices to avoid falling into those traps.

2. “Beautiful Visualization, Looking at Data Through the Eyes of Experts by Julie Steele, Noah Iliinsky”

Steele and Iliinsky’s book is a look into the thought process and design behind real-world examples of data visualization projects. 24 experts weigh in on their methods, approach, and perspectives in designing data visualization for their respective fields. “Beautiful Visualization” explores storytelling with data, communicating through visual indicators such as color, and research methods to put it all together.

This book describes the design and development of some well-known visualizations. It’s a great way to learn how others approach visual design. A particular insight is that every single author points out that collecting and cleaning their data is the hardest part of any project. It is heartening to know that this is a pain point for even the best analysts!

3. “The Accidental Analyst: Show Your Data Who’s Boss” by Eileen and Stephen McDaniel

This book is about what to do when you find yourself suddenly thrust into the world of analytics projects, even when you weren’t trained as an analyst. Lots of business users find themselves in this position every day. We have access to data, but maybe our background is in sales, HR, editorial, or operations. This book provides a great template for breaking down any analytics project into distinct segments and delivering something successful and effective.

Whether you landed a new job as a data analyst or had the responsibility plopped on you unexpectedly, this book is great for anyone looking to start digging into data for results. “The Accidental Analyst” provides a step-by-step guide on how to decide what questions you want answered, collect the data, organize the information, display it through visual graphics, and communicate the results.

4. “The Functional Art” by Alberto Cairo

Alberto Cairo is a data journalist and teaches information graphics and visualization. What is great about this book is that it explains how important it is to stick as close to best practices as possible but also ensure that your work is beautiful in order to engage the end user.

“Functional art” as a concept is something that serves a purpose while still retaining aesthetic qualities to be appealing and captivating. Cairo argues that this is the goal with data visualization as a whole. In this book, Cairo reviews the aesthetic qualities of data visualization and how to create good-looking pieces while still keeping within industry best practices. He focuses on using color and design to enhance the aesthetic and taking advantage of our brains’ quirks when perceiving and remembering information.

5. “The Visual Display of Quantitative Information” by Edward R.Tufte

While Edward Tufte’s book was first published in 1983 and is beginning to show age, it covers the theory and design of data graphics and is illustrated with some of the best and worst examples. Now over 30 years old, it is still a great introduction to some timeless design theories and provides a historical context for data visualization. The book includes 250 illustrations of data visualization examples of statistical graphics along with detailed breakdown analysis.

Future Trends in Data Visualization

These days, the mere collection of information from various sources no longer poses a challenge to data scientists. The increasingly important factor in the years to come will be the quality of its presentation.

Moving forward, we’ll see emerging technologies like augmented reality, virtual reality, and AI play a more significant role in effective data visualization.

Future trends will have machine learning and AI utilize their Natural Language Processing capabilities to generate insights. AR and VR will provide visual controllers making it possible for the viewers to explore data on a new level.

All this will lead to even more informed decisions ensuring business success. Business intelligence has a bright future for sure.

As organizations try to gain a competitive advantage by obtaining insights from large data sets, visualization turns into an increasingly important aspect of their operations. The market size for data visualization software is also predicted to grow by more than 11% annually, from $5.9 billion in 2021 to $10.2 billion in 2026.

As technology matures, so will the visual representations of information. In order to gain an upper hand, keep an eye on and perhaps even implement some trends in data visualization discussed here.

The better you understand data, the easier it will be for you to cope with any business challenges you may face.

Importance of Data Visualization

Let’s see some reasons why data visualization is so important.

1. Data Visualization Discovers the Trends in Data

The most important thing that data visualization does is discover the trends in data. After all, it is much easier to observe data trends when all the data is laid out in front of you in a visual form as compared to data in a table. For example, the screenshot below on Tableau demonstrates the sum of sales made by each customer in descending order.

However, the color red denotes loss while grey denotes profits. So it is very easy to observe from this visualization that even though some customers may have huge sales, they are still at a loss. This would be very difficult to observe from a table.

2. Data Visualization Provides a Perspective on the Data

Data Visualization provides a perspective on data by showing its meaning in the larger scheme of things. It demonstrates how particular data references stand with respect to the overall data picture. In the data visualization below, the data between sales and profit provides a data perspective with respect to these two measures. It also demonstrates that there are very few sales above 12K and higher sales do not necessarily mean a higher profit.

3. Data Visualization Puts the Data into the Correct Context

It is very difficult to understand the context of the data with data visualization. Since context provides the whole circumstances of the data, it is very difficult to grasp by just reading numbers in a table. In the below data visualization on Tableau, a TreeMap is used to demonstrate the number of sales in each region of the United States.

It is very easy to understand from this data visualization that California has the largest number of sales out of the total number since the rectangle for California is the largest. But this information is not easy to understand outside of context without data visualization.

4. Data Visualization Saves Time

It is definitely faster to gather some insights from the data using data visualization rather than just studying a chart. In the screenshot below on Tableau, it is very easy to identify the states that have suffered a net loss rather than a profit.

This is because all the cells with a loss are colored red using a heat map, so it is obvious states have suffered a loss. Compare this to a normal table where you would need to check each cell to see if it has a negative value to determine a loss. Obviously, data visualization saves a lot of time in this situation!

5. Data Visualization Tells a Data Story

Data visualization is also a medium to tell a data story to the viewers. The visualization can be used to present the data facts in an easy-to-understand form while telling a story and leading the viewers to an inevitable conclusion. This data story, like any other type of story, should have a good beginning, a basic plot, and an ending that it is leading towards.

For example, if a data analyst has to craft a data visualization for company executives detailing the profits on various products, then the data story can start with the profits and losses of various products and move on to recommendations on how to tackle the losses.

Data Visualization in Excel

There are many data visualization tools on the market designed for creating illustrations for business purposes. Fortunately, one of the most popular and easy-to-use options is likely already installed on your computer: Microsoft Excel.

If you don’t have access to Microsoft Excel, consider using free options like Google Sheets for a similar, albeit more limited, experience.

While Excel isn’t visualization software, it’s a versatile, powerful tool for professionals of all levels who want to analyze and illustrate datasets. Here are the types of data visualizations you can create in Excel and the steps involved in doing so

There are different data visualization techniques you can employ in Excel, depending on the data available to you and the goal you’re trying to achieve, including:

• Pie charts
• Bar charts
• Histograms
• Area charts
• Scatter plots

Other visualization techniques can be used to illustrate large or complex data sets. These include:

• Timelines
• Gantt charts
• Heat maps
• Highlight tables
• Bullet graphs

More advanced visualizations, such as those that include graphic elements like geographical heat maps, may not be possible to create in Excel or require additional tools.

How to Create Data Visualizations in Excel

The steps involved in creating data visualizations in Excel depending on the type of graph or chart you choose. For basic visualizations, the process is largely the same. More complex datasets and illustrations may require additional steps.

To craft a data visualization in Excel, start by creating an organized spreadsheet. This should include labels and your final dataset.

Then, highlight the data you wish to include in your visual, including the labels. Select “insert” from the main menu and choose the type of chart or graph you’d like to create. Once you’ve made your selection, the visualization will automatically appear in your spreadsheet.

Right-click on the chart or graph to edit details, such as the title, axes labels, and colors. Doing so will open a pop-up or side panel that includes options to add a legend, adjust the scale, and change font styles and sizes.

What are the 3 Main Goals of Data Visualization?

1. It understands the audience.

Before you throw up (pun intended) data in your visualization, start with the goal, which is to convey great quantities of information in a format that is easily assimilated by the consumers of this information — decision-makers. A successful visualization is based upon the designer understanding whom the visualization is targeting, and executing on three key points:

• Who is the audience, and how will it read and interpret the information? Can you assume it has knowledge of the terminology and concepts you’ll use, or do you need to guide it with clues in the visualization (e.g., indicated good is up with a green arrow)? An audience of experts will have different expectations than a general audience.
• What are viewers’ expectations, and what type of information is most useful to them?
• What is the visualization’s functional role, and how can viewers take action from it? An exploratory visualization should leave viewers with questions to pursue; educational or confirmational visualizations should not.

2. It sets up a clear framework.
The designer needs to ensure that everyone viewing the visualization is on common ground about what it is representing. In order to do so, the designer needs to set up a clear framework, which involves the semantics and syntax under which the data information is designed to be interpreted.

The semantics involve the meaning of the words and graphics used, and the syntax involves the structure of the communication. For example, when using an icon, the element should bear resemblance to the thing it represents, with size, color and position all communicating meaning to the viewer.

Lines and bars are simple, schematic geometric figures that are an integral component of many kinds of visualizations: lines connect, suggesting a relationship. Bars, on the other hand, contain and separate. In studies, when people have been asked to interpret an unlabeled line or bar graph, people overwhelmingly interpreted lines as trends and bars as discrete relations — even when conflicting with the nature of the underlying data.

There is one other element to the framework: Before everything else, make sure your data is clean and you understand its nuances. Does your data set have outliers? How is it distributed? Where does your data have holes? Are you making pre-judgments about the data? Real-world data is often complex, of diverse types from diverse sources, and not always reliable. Getting to know your data will help you select and appropriately use a framework.

3. It tells a story.
Visualization in its educational or confirmational role is really a dynamic form of persuasion. Few forms of communication are as persuasive as a compelling narrative. To this end, the visualization needs to tell a story to the audience. Stories package information into a structure that is easily remembered which is important in many collaborative scenarios when an analyst is not the same person as the one who makes decisions, or simply needs to share information with peers.

Data visualization lends itself well to being a communication medium for storytelling, in particular when the story also contains a lot of data. Minard’s graphic of Napoleon’s march on Moscow in 1812 is an exemplar. With newer technology freeing designers from the paper-based paradigm of images, even more compelling narratives can be constructed.

Storytelling helps the viewer gain insight from the data. Information visualization is a process that transforms data and knowledge into a form that relies on the human visual system to perceive its embedded information. The goal is to enable the viewer to observe, understand and make sense of the information.

The difference between information visualization and traditional storytelling in film, theater or television is that the information and story conveyed in information visualization environments are much more complicated. Design techniques that prioritize particular interpretations in visualizations that “tell a story” can significantly affect end-user interpretation.

What are the 4 Characteristics of Data Visualization?

Before you begin building your data visualization, you should also think about the criteria to make a data visualization excellent. Excellent data visualization usually has the following characteristics:

1.It is visually appealing.

The sophisticated visualization tools and the high quality of mobile applications have raised the bar on quality and usability. The quality requirements for visualization will only become higher with the new and emerging technologies such as VR Glasses. If your visualization was developed with old technology and poor quality, probably no one will be using it.

2.It is scalable.

If you have great datasets, and you want more people to use them, you need to make sure your visualization is scalable. As data volume and number of users grow, your visualization application needs to maintain the same performance. In other word, the system architecture should be scalable for future maintenance and modifications.

3.It gives the audience the right information.

It will be a problem you lead your audience to focus on a particular feature of your visualization but that is not the information they need. Before creating a visualization product, you need to understand your audience; you need to define exactly what they need. For example, are they the technical audience who want to drill down into the analysis or non-technical audience who want to understand the high-level information?

4.It is accessible.

You need to make sure people can use different devices to access your visualization, whether it is a high resolution monitor or a mobile device. Furthermore, an accessible visualization is not only easy to use, but also easily be changed if necessary. The accessibility function is critical for user acceptance.

5.It enables rapid development and deployment.

After you collect some interesting data, your audience may want to see the information as soon as possible. In some cases like the traveller information applications, the audience may want to see the information in real-time to support their driving decisions. If you cannot put in a reasonable time limit, your audience will find other ways to get it.

Charts in Data Visualization

There are many different types of data visualization charts that can improve our understanding of large, complex data sets. But each data visualization chart serves a different purpose, not every chart is appropriate for every project, and choosing the wrong type of charts for data visualization can make understanding the data even more confusing.

Choosing the best data visualization chart depends on the types of data being analyzed and the types of questions being asked.

There are five main types of charts in data visualization. Below are the best types of charts for data visualization for each category:‍

Temporal — These data visualizations are linear, one-dimensional, and typically feature standalone or overlapping lines that have a start time and a stop time. Some temporal data visualization chart examples include:

• Scatter plots: this chart shows two variables in the form of data points, with the physical orientation of each point determined by the value of the variable. A popular scatter plot variant is the data visualization bubble chart, in which the area of each data point bubble represents a third value.
• Polar area diagrams: area charts are essentially data visualization line charts that fill the space between the x-axis with colors, which helps visually communicate part-to-whole relations.
• Time series sequences: using lines, steps, or column charts, time series presents data points at successive time intervals, with the horizontal axis representing time and the vertical axis representing the measured values. 
• Timelines:timelines depict the chronological sequence of events on a timescale.
• Line graphs: line chart data visualizations present data as points connected by a continuous line, measuring a variable over an interval of time. 

Hierarchical — These charts order groups within larger groups. The best data visualization chart types for hierarchical data include:

• Tree diagrams: a tree diagram depicts a hierarchy of tasks and subtasks, or parents and children. 
• Ring charts:a ring chart data visualization, or sunburst diagram, depicts hierarchy with a series of concentric rings, in which each ring corresponds to a level in the hierarchy.

Network — These charts show how datasets relate to one another within a network:

• Matrix charts:a matrix diagram depicts the relationships between two or more groups of elements in grid format. There must be at least two variables assigned to the  X- and Y-categories. Variables beyond the first two are denoted with different colors. 
• Node-link diagrams:node-link diagram is the graphical convention for grahi drawing, with vertices represented as disks, boxes, or textual labels, and edges represented as line segments, polylines, or curves.
• Word clouds:also known as a tag cloud, word clouds show the most used words in a text, or the most searched words, as physically larger or smaller depending on how often each appears. Larger words indicate higher usage. 
• Alluvial diagrams: also known as flow diagrams, alluvial charts depict changes in network structures over time.

Multidimensional: These charts depict at least two or more variables and create 3D visualizations with several concurrent layers:

• Pie charts:a pie chart data visualization uses a single circle divided into “slices,” each slice representing a numerical proportion of the whole circle’s value. 
• Venn diagrams:a venn diagram uses circles, each representing a different variable, that are overlapped to show the logical relation between sets.
• Stacked bar graphs:Data visualization bar charts use horizontal columns to show numerical comparisons between categories. A waterfall chart is a type of multidimensional bar chart that uses floating bars to illustrate how an initial value is affected positively and negatively by different factors. Bullet graphs, another type of visualization using bars, features a single primary measure, layered with different colors to indicate actual value, target value, and ranges.
• Histograms:histograms depict the distribution of data over a continuous interval or particular period of time, estimating where values are concentrated with the use of vertical bars on a horizontal line. 

Geospatial — Spatial data visualizations overlay data points onto maps. The best charts for data visualization in geospatial cases include:

• Flow map: Linear symbols depict movement of something from an origin to a destination, with the width of the line proportionate to an increase or decrease in the amount of flow. 
• Density map:density mapping indicates the concentration of a feature with an increase of decrease in the number of data points in a given area. 
• Cartogram:cartograms distort the real boundaries of a geographic region in order to convey alternate variables, which will either inflate or deflate the boundaries proportionate to its numeric value. 
• Heat map:magnitude of a phenomenon are depicted as color in two-dimension; categorized as either cluster or spatial.

The importance of visual storytelling is greater than ever as humans are confronted with the continuously expanding data deluge. Human perceptual processes are more effectively engaged with the use of interactive, visual metaphors than solely with numerical values and text. And statistically, engagement is increased significantly with the incorporation of visual elements into the content.

Charts, graphs, and visuals convey information faster than a vast spreadsheet or dense report. Visual metaphors provide a universal language that can communicate with people of any spoken or written language. Data visualization charts make data easier to consume, which helps people quickly derive valuable insights, improve decision-making, uncover hidden patterns and relationships, identify upcoming trends, facilitate visual analytics, and improve collaboration.

How Data Visualization is Transforming the Healthcare Industry

Efficient and meaningful data visualization is at the core of many medical studies. It is also important for a lot of doctors, physicians, nurses and others as they use and interact with health interfaces on an everyday basis to generate patient reports and related information.

Data visualization focuses on the health industry’s most significant takeaways, helping us recognize the latest trends and associations, making the process of data analysis more efficient. The healthcare industry is exploring several digital techniques to create a predictive model of activity in order to express a comprehensive database and to stimulate ties with the latest trends.

Almost all healthcare centers across the globe have adopted data visualization solutions to manage their routine in-house operations. From patient profiling and recording patient information to maintaining and managing satisfaction surveys and complaint registers, hospitals have started to completely rely on data visualization for all these activities and much more.

Some of the reasons why visualization techniques in the healthcare segment must be practiced are listed below.

• Minimizes Errors

In a risky, dynamic environment, the tools of visualization often help in avoiding diagnosing errors by eliminating human error and creating a protective layer for patient safety. This becomes essential for keeping sensitive patient data and information protected.

The process provides a dashboard for tracking and monitoring patient health information. This dashboard is represented in the form of e-health records and e-medical records.

Dashboards that help medical professionals analyze vast sets of information easily can save time and even save lives. It is a computer-based recording system that makes it easier to understand the ongoing medical operations and systems better.

• Brings Down The Operational Cost

Data visualization is a process that makes the data readily available for use easily. This process helps us in minimizing operating costs by providing detailed data and information in understandable forms at the convenience of the doctors, nurses, and the administration on the spot.

The systems enable healthcare professionals to procure unique knowledge and information about the demographics and lifestyle preferences of their patients. This further helps them in tracking and healing the patients accordingly.

• Interactive Widgets

When you have loads and loads of data to share, you can never choose and pick only a few key points to illustrate. This is also the case when you are looking at state-by-state health information through a number of indicators. Still, to various members of your audience, some elements of the knowledge might be more important than others. If this describes the kind of data set you are working with, the best match for your content could be an interactive widget or a dashboard.

Interactivity enables the viewer to tailor the material or dataset to his or her needs and easily discover the knowledge without having to wade through a lot of content they just do not care about. This can saves time and saves a lot of effort.

• Enables Self Service Analytics

An environment where data is secure and handled by IT allows the company to embark on self-service data analytics and data visualization because they trust the data. Similarly, IT and the leadership of the company will trust that the data governance model assures that the right people have access right to the data.

This is the cornerstone of a culture of self-service data visualization and analytics. What they see is tied to their position and who they are. By allowing self-service analytics, anyone who has access to the data can explore and know better.

People can easily answer their own questions, share observations, learn from each other, and ask questions that lead to deeper insights. Since the data is on a centrally controlled network that is regulated by IT, each physician would only be able to see and explore data that is accessible to them under the security model.

• Assisting High-Risk Patients

Healthcare Data visualization can also help in identifying and grouping patients on the basis of the treatment and attention they require. If all the medical records are operated digitally, it would be the ideal situation to access and look for the data that can be used to recognize the pattern of many patients.

It will recognize the patients visiting the hospital repeatedly and identify their chronic problems. Such awareness and analysis would aid in providing the patients with accurate treatment and provide an insight into corrective steps to minimize their repeated visits.

It is the most appropriate way to create and maintain a list and check on high-risk patients and give them the personalized treatment they deserve.

How Data Visualization Help the Business

Why should you apply data visualization techniques to the diverse kinds and vast amounts of data generated throughout your business? Simply put: every kind of organization benefits from making data easier to understand; businesses across sectors, and also the government, education, healthcare, and sports organizations.

Some of the methods used in data visualization today include area chart, bar chart, box-and-whisker plots, bubble cloud, bullet graph, cartogram, circle view, dot distribution map, Gantt chart, heat map, highlight table, histogram, matrix, network, polar area, radial tree, scatter plot (2d or 3d), streamgraph, text tables, timeline, treemap, wedge stack graph, and word cloud. You can choose the right type of visualization based on the purpose of your visualization, the nature of data, and the needs of your audience. 

Let’s look at the benefits of data visualization for business. 

Enables Swift Action

It’s common knowledge that the human brain interprets visual information far more easily than written information. Using charts or graphs transforms even the most complex data into easy to understand relationships, instead of chaotic reports and spreadsheets.

Data visualization brings about an element of simplification and transparency which allows decision makers to comprehend and take meaningful actions swiftly. Leveraging big data visualization tools ensures that business leaders are able to utilize real time information to make decisions that affect the enterprise effectively.

They’re also well-positioned to be able to adapt and respond to market changes, thereby allowing them to identify new opportunities and stay ahead of the competition in any industry.

Helps Organizations Identify Key Business Trends

There is an abundance of large and valuable databases that are made available to organizations today. However, this can quickly become pointless, if not harnessed and utilized efficiently. Sales and marketing teams are prioritizing using data to understand their customers and create campaigns that will resonate with them.

However, as these teams become more data driven, it’s also important to note that they do not have the same technical knowledge on extracting meaningful insights as a data scientist would. This is obviously an impractical expectation. It would be a terrible utilization of the sales and marketing teams if they were to expend unnecessary effort on analyzing data or scanning religiously through spreadsheets.

Data visualization provides users with a swift, effective and easily accessible way to identify important patterns. This enables your sales and marketing teams to gain quick insights and make fast decisions in a competitive environment, without having to waste time on manual analysis.

CPG businesses, in particular, can really benefit from data visualization as they’ll be able to identify trends and gauge the performance of their trade promotions. Sales and marketing teams will gain visibility into which product lines, promotions, retailers etc. are not doing well. This helps them identify and segment plans with accuracy and to deliver business outcomes they desire.

By integrating advanced analytics as well, sales executives can drastically step up their game and leverage real-time Data Visualization to forecast sales figures. This also enables them to figure out if a product is under performing, they’ll easily be able to figure out why. They can use this information to rework pricing, or gauge if demand has fallen, or if there are competitors offerings similar products at a better price.

Meaningful Interpretation of Data

Yet another benefit that big data visualization offers is a great way to interpret meaningful stories from data. For instance, you can use heat maps to follow the growth of a specific product or how it has performed over time across a variety of geographical areas.

It makes it incredibly easy for decision-makers to quickly see which products and areas are performing very well versus which ones are underperforming, so they can make the necessary changes swiftly. Big data visualization tools offer a profitable way to use data and deliver key insights that can be used to create better marketing strategies that are customized by region and help facilitate an increase in sales.

Also, they help track any changes in real time performance and make it possible to respond swiftly to market trends – an ability that is critical to organizations staying ahead of the competition.

Direct and Customized Interaction With Data

A key benefit that data visualization offers is enabling companies to generate customized reports and interact with data in a direct way. This helps ensures that decision makers are obtaining actionable insights. In a stark contrast to spreadsheets, tables and charts, data visualization tools offer not just viewing, but active interaction.

Also, users can easily manipulate the representation of data to eliminate what they do not require and drill down into important details.

Data Visualization Increases Productivity and Sales

In an Aberdeen report, it was observed that organizations that use visual data discovery tools are 28 percent more likely to find timely information than those that rely solely on managed reporting and dashboards. Needless to say, the ability to visualize data can be rather transformative for organizations in terms of real business outcomes.

Not only are you enabling your employees to spend their time being more productive and work towards real business challenges instead of getting bogged down by report generation, but you’re also giving them an intelligent toolset to make better business decisions on the go.

It isn’t just sales and marketing teams that are benefited by the use of data visualization tools, but also your IT and finance departments. The study further reports that 48 percent of business intelligence users at companies with visual data discovery are able to find the information they need without the help of IT staff all or most of the time.

Businesses that have adopted data visualization tools have seen an immense increase in their ROI (Return On Investment). In yet another study of global businesses, it was observed that only 26 percent of the respondents’ organizations used data visualization. However, most of the companies that did, lead the pack in terms of revenue growth and were looking forward to continuing investing even more in data visualization in the next year.

Where to Learn Data Visualization

If you’re comfortable working with large amounts of data and are passionate about making sure people understand the available data about a project, program, or concern, learning data visualization may be a good choice for you. You’ll likely have opportunities to help others by sharing important data in a format that’s visually appealing and easy to interpret.

Learning data visualization can add to your skillset if you’ve previously studied or worked as a graphic designer or data analyst. You may choose to learn a programming language so you can develop your own data warehouse or another visualization tool to showcase the data you analyze, or you can decide to explore the variety of methods used to create visual displays of data.‎ Below are some online courses that can help you.

Data Visualization with Tableau: University of California, Davis
Google Data Analytics: Google
Excel Skills for Data Analytics and Visualization: Macquarie University
Data Visualization with Python: IBM Skills Network
Data Visualization & Dashboarding with R: Johns Hopkins University
Data Analysis and Visualization Foundations: IBM Skills Network
Data Visualization: University of Illinois at Urbana-Champaign
Fundamentals of Data Visualization: University of Colorado Boulder
Information Visualization: New York University
Data Analysis and Presentation Skills: the PwC Approach: PwC

Where Data Visualization is Used

With so much data being acquired through data analysis in today’s corporate environment, we need a means to visualize that data so we can understand it. 

By placing data in a visual context, such as maps or graphs, data visualization helps us understand what it means. This makes the data more natural to understand for the human mind, making it simpler to see trends, patterns, and outliers in huge data sets.

1. Healthcare Industries

A dashboard that visualizes a patient’s history might aid a current or new doctor in comprehending a patient’s health. It might give faster care facilities based on illness in the event of an emergency. Instead than sifting through hundreds of pages of information, data visualization may assist in finding trends.

Health care is a time-consuming procedure, and the majority of it is spent evaluating prior reports. By boosting response time, data visualization provides a superior selling point. It gives matrices that make analysis easier, resulting in a faster reaction time.

2. Business intelligence

When compared to local options, cloud connection can provide the cost-effective “heavy lifting” of processor-intensive analytics, allowing users to see bigger volumes of data from numerous sources to help speed up decision-making.

Because such systems can be diverse, comprised of multiple components, and may use their own data storage and interfaces for access to stored data, additional integrated tools, such as those geared toward business intelligence (BI), help provide a cohesive view of an organization’s entire data system (e.g., web services, databases, historians, etc.).

Multiple datasets can be correlated using analytics/BI tools, which allow for searches using a common set of filters and/or parameters. The acquired data may then be displayed in a standardized manner using these technologies, giving logical “shaping” and better comparison grounds for end users. 

3. Military

It’s a matter of life and death for the military; having clarity of actionable data is critical, and taking the appropriate action requires having clarity of data to pull out actionable insights.

The adversary is present in the field today, as well as posing a danger through digital warfare and cybersecurity. It is critical to collect data from a variety of sources, both organized and unstructured. The volume of data is enormous, and data visualization technologies are essential for rapid delivery of accurate information in the most condensed form feasible. A greater grasp of past data allows for more accurate forecasting.

Dynamic Data Visualization aids in a better knowledge of geography and climate, resulting in a more effective approach. The cost of military equipment and tools is extremely significant; with bar and pie charts, analyzing current inventories and making purchases as needed is simple.

4. Finance Industries

For exploring/explaining data of linked customers, understanding consumer behaviour, having a clear flow of information, the efficiency of decision making, and so on, data visualization tools are becoming a requirement for financial sectors.

For associated organizations and businesses, data visualization aids in the creation of patterns, which aids in better investment strategy. For improved business prospects, data visualization emphasizes the most recent trends.

5. Data science

Data scientists generally create visualizations for their personal use or to communicate information to a small group of people. Visualization libraries for the specified programming languages and tools are used to create the visual representations.

Open source programming languages, such as Python, and proprietary tools built for complicated data analysis are commonly used by data scientists and academics. These data scientists and researchers use data visualization to better comprehend data sets and spot patterns and trends that might otherwise go undiscovered.

6. Marketing

In marketing analytics, data visualization is a boon. We may use visuals and reports to analyze various patterns and trends analysis, such as sales analysis, market research analysis, customer analysis, defect analysis, cost analysis, and forecasting. These studies serve as a foundation for marketing and sales.

Visual aids can assist your audience grasp your main message by visually engaging them and visually engaging them. The major advantage of visualizing data is that it can communicate a point faster than a boring spreadsheet. 

In b2b firms, data-driven yearly reports and presentations don’t fulfil the needs of people who are seeing the information. They are unable to grasp the art of engaging with their audience in a meaningful or memorable manner. Your audience will be more interested in your facts if you present them as visual statistics, and you will be more inclined to act on your discoveries.

7. Food delivery apps

When you place an order for food on your phone, it is given to the nearest delivery person. There is a lot of math involved here, such as the distance between the delivery executive’s present position and the restaurant, as well as the time it takes to get to the customer’s location.

Customer orders, delivery location, GPS service, tweets, social media messages, verbal comments, pictures, videos, reviews, comparative analyses, blogs, and updates have all become common ways of data transmission.

Users may obtain data on average wait times, delivery experiences, other records, customer service, meal taste, menu options, loyalty and reward point programs, and product stock and inventory data with the help of the data.

8. Real estate business

Brokers and agents seldom have the time to undertake in-depth research and analysis on their own. Showing a buyer or seller comparable home prices in their neighbourhood on a map, illustrating average time on the market, creating a sense of urgency among prospective buyers and managing sellers’ expectations, and attracting viewers to your social media sites are all examples of common data visualization applications.

If a chart is difficult to understand, it is likely to be misinterpreted or disregarded. It is also seen to be critical to offer data that is as current as feasible. The market may not alter overnight, but if the data is too old, seasonal swings and other trends may be overlooked. 

Clients will be pulled to the graphics and to you as a broker or agent if they perceive that you know the market. If you display data in a compelling and straightforward fashion, they will be drawn to the graphics and to you as a broker or agent.

9. Education 

Users may visually engage with data, answer questions quickly, make more accurate, data-informed decisions, and share their results with others using intuitive, interactive dashboards.

The ability to monitor students’ progress throughout the semester, allowing advisers to act quickly with outreach to failing students. When they provide end users access to interactive, self-service analytic visualizations as well as ad hoc visual data discovery and exploration, they make quick insights accessible to everyone – even those with little prior experience with analytics.

10. E-commerce

In e-commerce, any chance to improve the customer experience should be taken. The key to running a successful internet business is getting rapid insights. This is feasible with data visualization because crossing data shows features that would otherwise be hidden.

Your marketing team may use data visualization to produce excellent content for your audience that is rich in unique information. Data may be utilized to produce attractive narrative through the use of infographics, which can easily and quickly communicate findings.

Patterns may be seen all throughout the data. You can immediately and readily detect them if you make them visible. These behaviors indicate a variety of consumer trends, providing you with knowledge to help you attract new clients and close sales.

Why Data Visualization is Important in Machine Learning

Data visualization has become popular in recent years due to its power to display the results at the end of the machine learning process, but it is also increasingly being used as a tool for exploratory data analysis before applying machine learning models.

At the beginning of the machine learning process, data visualization is a powerful tool.

Machine learning is inherently an iterative process. Modeling can be cumbersome when you are performing the process over and over again to ensure your model is optimized and can generalize well. Add on the time you spend on model selection and model tuning, the process can easily become a frustrating one.

Good exploratory data analysis combined with relevant data visualization is essential for pinpointing the right direction to take. It both shortens the machine learning process and provides more accuracy for its outcome. Data visualization tools such as TensorFlow enable data scientists to quickly identify and focus on the most important data and the most important paths to take.

Even during the modeling process, model graphs can help to speed up the model-creation process by displaying the model maps conceptually. When evaluating the models, visualizing the results of hyperparameter tuning can help data scientists narrow down the groupings of hyperparameters that are most important.

Before popular data visualization tools for machine learning were developed, the machine learning process was much more abstract. Historically, ggplot2 in R provided much-needed visualization tools for exploratory data analysis. But today, with the suite of data visualizations that are available in Python, such as seaborn, scikit-learn, and matplotlib, exploratory data analysis that forms the initial part of the machine learning process can be done much more efficiently.

At the same time, with TensorFlow, model-building and model-tuning processes become a lot more intuitive. Rather than spending time on scrutinizing values, with the assistance of both 2-dimensional and interactive data visualizations, data scientists can pay more attention to the big picture of the data at each level of the machine learning process to focus more on the meaning of the data, the model design, and the model performance.

Why Data Visualization is Important in Data Mining

No matter what business or career you’ve chosen, data visualization can help by delivering data in the most efficient way possible. As one of the essential steps in the business intelligence process, data visualization takes the raw data, models it, and delivers the data so that conclusions can be reached. In advanced analytics, data scientists are creating machine learning algorithms to better compile essential data into visualizations that are easier to understand and interpret.

Specifically, data visualization uses visual data to communicate information in a manner that is universal, fast, and effective. This practice can help companies identify which areas need to be improved, which factors affect customer satisfaction and dissatisfaction, and what to do with specific products (where should they go and who should they be sold to). Visualized data gives stakeholders, business owners, and decision-makers a better prediction of sales volumes and future growth. 

Here are some more specific ways that data visualization can benefit an organization: 

• Correlations in Relationships: Without data visualization, it is challenging to identify the correlations between the relationship of independent variables. By making sense of those independent variables, we can make better business decisions. 
• Trends Over Time: While this seems like an obvious use of data visualization, it is also one of the most valuable applications. It’s impossible to make predictions without having the necessary information from the past and present. Trends over time tell us where we were and where we can potentially go.
• Frequency: Closely related to trends over time is frequency. By examining the rate, or how often, customers purchase and when they buy gives us a better feel for how potential new customers might act and react to different marketing and customer acquisition strategies. 
• Examining the Market: Data visualization takes the information from different markets to give you insights into which audiences to focus your attention on and which ones to stay away from. We get a clearer picture of the opportunities within those markets by displaying this data on various charts and graphs.
• Risk and Reward: Looking at value and risk metrics requires expertise because, without data visualization, we must interpret complicated spreadsheets and numbers. Once information is visualized, we can then pinpoint areas that may or may not require action.
• Reacting to the Market: The ability to obtain information quickly and easily with data displayed clearly on a functional dashboard allows businesses to act and respond to findings swiftly and helps to avoid making mistakes.

Why Data Visualization is Important in Data Science

Would you prefer to view large data tables and then make sense of that data or view a data visualization that represents that data in an easy to understand visual format? Well, most of you would prefer data visualization! And that is because data visualization is extremely useful in understanding the data and obtaining useful insights.

It can allow you to get an instant understanding of the data that is just not possible by observing rows of data in a table. That’s what makes it so important in Data Science! Below are 7 more reasons.

1. Data Visualization Discovers the Trends in Data

The most important thing that data visualization does is discover the trends in data. After all, it is much easier to observe data trends when all the data is laid out in front of you in a visual form as compared to data in a table. For example, The below screenshot on Tableau demonstrates the sum of sales made by each customer in descending order.

However, the color red denotes loss while grey denotes profits. So it is very easy to observe from this visualization that even though some customers may have huge sales, they are still in the loss. This would be very difficult to observe from a table.

2. Data Visualization is Interactive

Data Visualization can be used to interact with the users in a way that textual table-based data cannot. After all, data visualization tells a story from a particular angle to the users, and that by default makes it interactive. Users can also focus on aspects of the data visualization that they find particularly interesting and then they can learn more about that aspect of the data.

The data visualization also allows the user to obtain a holistic view of the data using different types of charts and ample usage of colors, shapes, etc. This interactivity also allows the viewers to understand the data visualization at a single glance which is rather difficult for

3. Data Visualization Provides a Perspective on the Data

Data Visualization provides a perspective on data by showing it’s meaning in the larger scheme of things. It demonstrates how particular data references stand with respect to the overall data picture. It also demonstrates that there are very few sales above 12K and higher sales do not necessarily mean a higher profit.

4. Data Visualization Explains a Data Process

Data Visualization can be used to demonstrate a data process from the beginning to the end. This can be done by using many different charts in a dashboard on a data story to convey a process. This method allows the viewers to get a lot of information in multiple smaller visualizations so that they can easily digest the data and understand the overarching data process.

This might be difficult to convey without visualizations as viewers would not be able to understand the data process by just looking at data tables.

5. Data Visualization Strokes the Imagination

There is nothing that can ignite viewers imaginations more than beautiful and well-presented data visualization! There is so much more power in the visual images to stroke imagination as compared to textual tables. This is because humans are primarily visual beings and they can understand data much faster if it is presented to them in a visual format.

This also allows them to use their own imagination and deeply analyze the visualization which may provide them more insights than just reading dry acts and analyzing a data table.

6. Data Visualization Tells a Data Story

A data visualization is also a medium to tell a data story to the viewers. The visualization can be used to present the data facts in an easy to understand form while telling a story and leading the viewers to an inevitable conclusion. This data story like any other type of story should have a good beginning, a basic plot, and an ending that it is leading towards.

For example, if a data analyst has to craft a data visualization for company executives detailing the profits in various products, then the data story can start with the profits and losses of various products and move on to recommendations on how to tackle the losses.

7. Data Visualization Puts the Data into the Correct Context

It is very difficult to understand the context of the data with data visualization. Since context provides the whole circumstances of the data, it is very difficult to grasp by just reading numbers in a table. In the below data visualization on Tableau, a TreeMap is used to demonstrate the number of sales in each region of the United States.

It is very easy to understand from this data visualization that California has the largest amount of sales out of the total since the rectangle for California is the largest. But this information is not easy to understand out of context without data visualization.

Why Data Visualization is Important in Business Analytics

Data visualization is essential to assist businesses in quickly identifying data trends, which would otherwise be a hassle. The pictorial representation of data sets allows analysts to visualize concepts and new patterns. With the increasing surge in data every day, making sense of the quintillion bytes of data is impossible without Data Proliferation, which includes data visualization.

Every professional industry benefits from understanding their data, so data visualization is branching out to all fields where data exists. For every business, information is their most significant leverage. Through visualization, one can prolifically convey their points and take advantage of that information.

A dashboard, graph, infographics, map, chart, video, slide, etc. all these mediums can be used for visualizing and understanding data. Visualizing the data enable decision-makers to interrelate the data to find better insights and reap the importance of data visualization, which are:

1. Analyzing the Data in a Better Way

Analyzing reports helps business stakeholders focus on the areas that require attention. The visual mediums help analysts understand the key points needed for their business. Whether it is a sales report or a marketing strategy, a visual representation of data helps companies increase their profits through better analysis and better business decisions.

2. Faster Decision Making

Humans process visuals better than any tedious tabular forms or reports. If the data communicates well, decision-makers can quickly take action based on the new data insights, accelerating decision-making, and business growth simultaneously.

3. Making Sense of Complicated Data

Data visualization allows business users to gain insight into their vast amounts of data. It benefits them to recognize new patterns and errors in the data. Making sense of these patterns helps the users pay attention to areas that indicate red flags or progress. This process, in turn, drives the business ahead.

Does Data Visualization Require Coding?

Data analysis requires examining data and drawing conclusions. When you see a chart, for example, you see trends and outliers. So, coding is not a necessary skill in data visualization and analysis.

You don’t need to write any code to easily create interactive data visualization.

Data visualization does not require you to have coding skills. Here is the list of best tools to create stunning data visualization and no coding is required.

1. Google Sheets
2. Microsoft Excel
3. ChartExpo

Almost all of us are familiar with the first two tools, but the third tool ChartExpo can take your analysis journey to next level by making interactive visualization that is easy to read.

ChartExpo is one of the tools you can use to create amazing visualizations. It enables you to create visuals like histograms, line charts, pie charts, bar charts, scatter plots, interactive Sankey diagrams, and many more custom charts without any coding.

If you want to visualize map data, gauges, and percentages, chart widgets on ChartExpo are the perfect feature.

You can also export the charts to impress your audience with beautifully done social media reports, goal projections, and sales reports using a variety of ready-to-go templates.

To well represent your data visually, you need an effective tool, and ChartExpo offers just that.

You can select a chart from a library of visualizations. After creating these visualizations, you can directly add them to the documents you have been working on using. They could be proposals, reports, infographics, etc. This makes your work easy and convenient.

Another advantage is that you do not have to keep switching between spreadsheets and ChartExpo when creating visualizations. You can add ChartExpo to Excel via the add-in library and integrate data you have recorded in Google Sheets into this tool as well.

How Can Data Visualization Improve Healthcare Decision Making?

Data analysis goes hand in hand with data preparation, data modeling, and data visualization. Today, healthcare analytics is becoming more advanced through the use of artificial intelligence (AI) and machine learning technologies. The adoption of visualization tools is a must-have for those organizations that want to benefit from data analysis to the full. 

So what are the benefits of using data visualization in healthcare? Here’s the rundown on five of them. 

Improved patient care

Visualization brings what matters into focus, helping medical staff identify and react faster to threats. By visualizing real-time data on patient health status, doctors can define and group patients according to the treatment and attention they need. Including all patient records in a dashboard also prevents practitioners from missing important information. Case in point, this may lead to a fewer number of drug intolerance incidents, improving the efficiency of treatment.

Trends and pattern recognition

By using data visualization software that is often paired with built-in predictive analytics tools, doctors can estimate changes in patient health status and make better diagnoses. Collecting heart rate data from fitness apps and wearable devices, for example, can reveal possible risks before the problem occurs. 

Along with pattern recognition in patient care, visualization of healthcare data also helps the government identify trends in statistical and research reports, improving their strategic decisions related to public health. For example, 90% of locations in the US had enforced mandates such as mask-wearing and social distancing when the number of deaths daily reached eight per million.

Data presentations for different audiences

Visuals can help a lot when you need to present data to business owners and other stakeholders or use it in healthcare marketing campaigns. Our brain understands visual content better than endless lines of text. With healthcare data visualization solutions, everyone can generate meaningful insight and make data-driven decisions regardless of data literacy or business analytics skills. 

It’s also good to use visualization for patient education since clear and easy-to-understand charts and infographics will help improve patient awareness and increase engagement. This, in turn, can lead to better lifestyle choices and promote a focus on what needs improvement, such as activity, body weight, or eating habits.

Accelerated performance 

Unlike lengthy, manual reporting, real-time data visualization significantly speeds up the analysis of information, helping healthcare organizations minimize process inefficiencies, accelerate the decision-making process, and optimize costs. 

Hospitals, for example, receive financial incentives or penalties from Medicare based on patient satisfaction scores. By tracking and visualizing parameters related to the patient experience, such as their feedback on communication with medical staff or wait time, hospitals can identify problems to focus on and improve their results.

Errors and fraud detection

The annual cost of fraud and abuse in Medicare alone ranges from $58.5 to $83.9 billion. Fraud schemes are committed by medical providers and patients and can include double or phantom billing, multiple bills for a single service, forged prescriptions, insurance cheating, and more. 

Data visualization improves the transparency of connections between patients, practitioners, providers, payers, and claims. Paired with machine learning and AI technologies, visualization enables users to avoid errors and mess, as well as to quickly detect anomalies and potential fraud incidents related to medical billing or prescriptions. 

Of course, this is not an exhaustive list of all benefits. Still, it gives a better understanding of how the visualization of data helps healthcare and why more and more medical organizations adopt visualization tools into their practice. And if you are about to implement it yourself, here are several visualization tools to consider.

Data Visualization Analyst Salary

The average annual pay for a Data Visualization Analyst in the United States is $87,515 a year. Just in case you need a simple salary calculator, that works out to be approximately $42.07 an hour. This is the equivalent of $1,682/week or $7,292/month.

While ZipRecruiter is seeing annual salaries as high as $150,000 and as low as $51,000, the majority of Data Visualization Analyst salaries currently range between $70,500 (25th percentile) to $101,500 (75th percentile) with top earners (90th percentile) making $121,000 annually across the United States.

The average pay range for a Data Visualization Analyst varies greatly (by as much as $31,000), which suggests there may be many opportunities for advancement and increased pay based on skill level, location and years of experience.

Data Visualization Benefits

There are ways and programs that help to visualize all the data your company has into dashboards, graphs and reports. Data visualization helps transform your numbers into an engaging story with details and patterns. Below are some of the benefits of data visualization

Easy to understand

Thanks to graphic representations, we can interpret vast quantities of data in a clear and cohesive way, which in turn allows us to understand the data and to draw conclusions and see perspectives.

Using data visualization, managers and decision-makers can easily create and rapidly consume important metrics. If any of these metrics have anomalies — ie. Sales are significantly down in one region — decision makers will easily dig into the data to find out what operating circumstances or decisions are at stake and how they respond to the factors encountered.

Spot patterns

Data visualization enables us to recognize emerging trends and respond rapidly on the grounds of what we see. Such patterns make more sense when graphically represented; because visuals and diagrams make it easier for us to identify strongly correlated parameters. Certain relations are obvious, but others need to be recognized and clarified to help us concentrate on a particular data point that can have an effect on business.

Storytelling is more engaging

Tableau is one of the leaders in helping you visualize your data. You can build dashboards and turn them into stories. The reason why this program is so successful is that storytelling is a model which enables us to easily create a narrative through graphics and diagrams through which, with the help of visual analytics, we can discover new insights and engage others. Conveying the message in a story is much more engaging and easier to understand for your employees. 

Speed up the decision-making process

Humans can process visual images 60,000 times faster than text. Therefore, seeing a graph, chart, or other visual representation of data is more comfortable for the brain to process, than for it to read and comprehend text, and then convert this into a mental visualization (that likely will not be entirely accurate) of the data. 

Thanks to our ability to easily interpret visual data , data visualization can dramatically improve the speed of decision-making processes. According to the Wharton School of Business, data visualization can shorten business meetings by up to 24 percent. 

Not just a simple pie chart

Today, we can move away from a bar graph or a pie chart, although they can still be useful at times. However, with current and more advanced data visualization capabilities, users can also make more advanced visualizations possible.

For example, a simple way to visualize any random data is to configure it in a bubble graph or a heat map. For more sophisticated research, you can choose displays such as frame diagrams (for hierarchical presentations) or Gantt charts (for project management). 

These more advanced visualizations show that for any type of data set there is a good way to transform it into an easy-to-understand image.

Data Visualization Engineer Salary

The average annual pay for a Data Visualization Engineer in the United States is $138,563 a year. Just in case you need a simple salary calculator, that works out to be approximately $66.62 an hour. This is the equivalent of $2,664/week or $11,546/month.

While ZipRecruiter is seeing annual salaries as high as $348,500 and as low as $73,500, the majority of Data Visualization Engineer salaries currently range between $100,000 (25th percentile) to $151,500 (75th percentile) with top earners (90th percentile) making $180,000 annually across the United States.

The average pay range for a Data Visualization Engineer varies greatly (by as much as $51,500), which suggests there may be many opportunities for advancement and increased pay based on skill level, location and years of experience.

Data Visualization Interview Questions

Data preparation and validation questions are used to assess your ability to work through challenges in data quality and processing.

1. What should be done with suspected or missing data?

Walk the interview through your data validation process. You might include preparing a data validation report, which reveals why the data failed. Next, you might discuss how you would analyze this data, and also strategies for working with missing data, like deletion, single imputation, mean/median/mode imputation, etc.

2. What process would you use to transform raw data into a visualization?

A question like this quickly assesses your experience working with data processing. Step 1 might be something like gathering stakeholder input and understanding the goals of the visualization. Then, you might include steps like:

• Data collection
• Data cleaning (De-dupping, missing values, standardizing)
• Choosing a chart type
• Data preparation (formatting, converting, grouping/aggregating)

3. What is an outlier? How would you address outliers?

4. Name some data validation techniques.

5. What are the characteristics of a good data model?

Data Visualization Jobs Salary UK

The average data visualization manager’s gross salary in the United Kingdom is £64,607 or an equivalent hourly rate of £31. In addition, they earn an average bonus of £6,990. Salary estimates are based on salary survey data collected directly from employers and anonymous employees in the United Kingdom.

An entry-level data visualization manager (1-3 years of experience) earns an average salary of £45,351. On the other end, a senior-level data visualization manager (8+ years of experience) earns an average salary of £80,185.

Data Visualization Objectives

Data visualization is useful for data monitoring, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting patterns, and presenting actionable results. There’re tons of good data visualization examples, such as Bar graphs and Sankey, you can use to create irresistible data stories.

Data visualization helps you to identify trends and patterns in your business data quickly.

Our brain processes visual information, such as charts, 60,000 times faster than poring over spreadsheets and data reports.

Visualization is a quick and easy way to convey insights to a broader audience. Make data visualization a habit in your organization to enjoy the benefits below:

1. Quick and Better Analysis

Visualization tools make it amazingly easy for you to extract answers from your data to create compelling stories for investors. Imagine using the tables and spreadsheets to explain emerging patterns and other significant insights to your audience.

2. Empowers Quick Action

Our brains grasp visual content, such as graphs and maps, 60,000 times faster than table reports, as we said earlier. And this means a compelling data story loaded with easy to interpret charts can empower quicker decision-making.

3. Identifying Patterns and Trends

Bulky data provides unlimited opportunities for businesses to extract actionable insights. Yes, insights that could spell the difference between you and the competition.

Visualizing data helps pinpoint relationships and patterns between metrics. Exploring these patterns enables you to save immense resources, such as time, by focusing only on areas that need urgent action.

4. Finding Errors

Data visualization helps you spot errors in the data easily. Working with error-free data validates the accuracy of the insights extracted. We hope you see the importance of data visualization.

5. Data Storytelling

The reason why we visualize data is to create data stories. Remember, poring over numbers in spreadsheets is monotonous, especially if you’re in front of an audience.

So you need to create a compelling story with insights extracted from the raw data. People love stories. Yes, and this is because they appeal to emotions. To craft a compelling data story, you need an actual story. It sounds contradictory. Yes, we know.

To create a narrative, start by asking a question or forming a hypothesis. And then, dig into your data to find answers.

Below are some of the questions you need to ask:

• What are you going to explain? 
• What are your goals? 
• Are you after a buy-in on a proposal?

Remember, visualization comes before you create a data story.

Data Visualization Software Engineer

The data visualization engineer’s salary range is $51,000 to $110,000 annually with an average salary of $72,323 / year.

Typically, data visualization engineers may have either a bachelor’s degree in computer science, computer information systems, software engineering, or any related degree.

Read Also: Content Monetization Strategies for 2023

For engineering-focused data visualization jobs, expect to need three or more years of relevant technical experience or a master’s degree with one or more years of relevant technical experience.

Responsibilities for data visualization engineers include: 

• Designing, developing and maintaining user-friendly data visualizations and dashboards using complex datasets from different sources.
• Implementing best practices to ensure dashboard design consistency for the best user experience.
• Designing, writing, and testing analytics platform code.
• Debugging applications, tracing code, and finding and fixing bugs.

Data Visualization Software Comparison

Here’s an overview of the top 7 paid-for visualization solutions, and for those on a tight budget look for my round-up of free tools coming soon.

Microsoft Power BI

PowerBI is an all-in-one BI and analytics platform provided as-a-service or as a desktop client but it is particularly highly rated for its visualization capabilities. Visualizations are created directly from reports and can be shared with users throughout your organization.

As well as a large number of inbuilt visualization styles, new ones are constantly being created by the AppSource community or if you want to get your hands dirty with coding, they can be created from scratch using the Developer Tools and shared with other users. It also includes a natural language interface allowing visualizations of varying complexity to be built from simple search terms. It is consistently rated as one of the easiest to use tools for visual data exploration.

Tableau

Tableau is often considered the gold standard of data visualization tools and as such it enjoys wide deployment with a reported 57,000 active user accounts. Much of its appeal stems from its flexibility and while it may not be as beginner-friendly as some packages, a vast support network exists in the form of its global community of users, spread across many industries.

In particular, its power means it is well suited to Big Data operations involving fast, constantly changing datasets, and as such it is designed to easily plug in to a large range of industry-standard databases such as MySQL, Amazon AWS, Hadoop, SAP and Teradata.

Three basic distributions are available – Desktop, Server and cloud-based Online. New additions to the service this year include the Hyper in-memory capabilities designed to greatly speed up analysis of large data sets.

QlickView

QlickView is another very popular option for building and sharing visualizations based on any data used by an organization. Qlik has worked hard in recent years to make its product more accessible and easy to use regardless of technical ability when it comes to working with data.

This doesn’t mean it sacrifices power or features, however, with insightful visualizations being achievable in minutes, which can be shared instantly with anyone thanks to its device-agnostic infrastructure. Often it is used alongside the provider’s QlikSense platform to provide end-to-end analytics and reporting. It also has advanced security features which allows different levels of access to data to be set according to the needs of individual users.

Sisense

Sisense is another end-to-end analytics solution with a powerful suite of visualization tools. It features an intuitive drag-and-drop method of building any sort of visualization from simple charts to complex, interactive infographics and dashboards.

This year it has added machine learning components that simplify the process of crunching through Big Data to unearth the most relevant insights, and making connections that would go unnoticed by even the most highly-trained human eyes. Its aim is to make data understandable to anyone in your organization through fully customizable, interactive visual environments.

FusionCharts Suite XP

FusionCharts enables the creation of richly interactive JavaScript-based charts, with the advantage that they can be easily and quickly embedded anywhere where JavaScript can be run. A key advantage here is that as they run in native Javascript they will look and function identically across whatever devices they are displayed on.

It offers a large number of templates which users can simply feed their own data sources into, with the company claiming that a complete beginner will be able to create their first charts and graphics within 15 minutes of sitting down with the software. 90 chart types are available from the start, from simple line and pie charts to more sophisticated heat map, zoom line and treemap charts.

Plot.ly

Another visualization-focused analytics tool that is widely popular across a large number of commercial and industrial uses. Plot.ly is known for its ability to create more technical and complex interactive charts and visualizations due to its plug-and-play relationship with analytical programming languages including R, Matlab and Python.

Plot.ly’s foundation are the open source D3.js Javascript visualization libraries but it adds an advanced and intuitive graphical user interface as well as connectivity with a number of proprietary CRM systems including the ubiquitous Salesforce.

Visualizations can be created through the online interface or with any of the supported programming languages by accessing its libraries directly. Anything you create from simple charts to fully interactive visualizations based on real-time Big Data can be simply embedded into websites or reports through HTML or iFrames.

Carto

Carto is specifically focused at creating graphics that take the form of maps, and as such has a number of features that make it a great choice for this particular form of data visualization. It uses a wizard-driven interface which means it doesn’t take long to start mapping your data thanks to a graphical, web-based drag-and-drop environment.

Its software-as-a-service (SaaS) model means that it is both affordable for small organizations but also scales as your needs for graphical reporting of location-based intelligence grows.

How to Use Data Visualization for SEO

Visualizing your data has a significant impact on interpretation. Visualization can help represent search volume for different keyword sets you want to use in your next campaign. Visualization tools can also present a detailed analysis of your site from the SEO point of view.

Data visualization can help elevate your SEO strategies in several ways. Here are the most effective areas in that visualization will help boost SEO.

Competitive Analysis

Working on your SEO strategy also means evaluating what competitors are doing. The analysis helps you understand what requires doing and areas to improve.

Visualization can help you:

• Determine the social media strength of competitors.
• Find top competitors for a keyword.
• Analyze competitor backlink profiles.

The above is an example of using a bar chart to visualize the keyword difficulty distribution of current keyword rankings.

Backlink Analysis

Visualization also aids you in creating an effective link-building campaign. Some items to analyze include:

• Backlink geographic locations.
• Quality of backlinks.
• The distribution of backlink anchor text.

Bottomline

Data visualization is a vital contribution to the success of any business practice. What makes visualization critical is its ability to convey complicated data sets visually. Anything that can condense large amounts of data into infographics, charts, and graphs is a successful recipe.

It’s clear incorporating visualization in your digital marketing operations elevates SEO capabilities too. Further, visualizing your data plays a major role in business development and SEO decisions.