Your 2020 Guidebook on Data Visualization Using Tableau

Data visualization is a form of graphical information and data. It uses visual elements and tools to provide an accessible way of understanding data visualization trends, patterns, and outliers in data. Data visualization using Tableau helps businesses to perform different tasks like sorting, analyzing, and comparing data, very easily and fast.

Many businesses even consider Tableau an indispensable tool for data-science related work. Moreover, Tableau is compatible with several sources, including SQL, Excel, and cloud-based data repositories. This makes Tableau an excellent choice for most data scientists.

Basically, data visualization using Tableau, its tools, and technologies is critical in analyzing large amounts of information that allow it to make data-driven decisions.

That said, here’s a detailed overview of data visualization using Tableau, its examples, and how to apply it in real-life situations.

The Benefits of Good Data Visualization

Data visualization skills are incredibly important in today’s data-driven world. Collecting and analyzing data comes with several benefits both to the economy and real-life situations. People’s eyes are drawn to different colors and patterns, some more than others. For instance, you can quickly identify blue from red, a circle from a square.

The human culture is very visual, including everything from advertisements to art to TVs. When you see a chart, you quickly think of different trends and outliers. The human brain internalizes such kind of information quickly. Therefore, making Tableau a critical data visualization process that expresses this data in a storytelling form. Imagine staring at a large amount of data in a spreadsheet. Here’s where you can appreciate the effectiveness of data visualization using Tableau.

As the age of big data continues to kick in, visualization continues to become an incredibly important tool in helping data scientists make sense of massive rows of data generated every day. You can use several data visualization tools and techniques to tell your kind of stories by curating collected data into a form that’s easier to understand.

Besides helping to highlight trends and outliers in data, different tableau visualization types help to tell a story. It removes the noise that would, otherwise, make it more difficult to analyze and understand data.

Why Data Visualization Using Tableau Is Important for Any Career

Different fields in government, marketing, finance, consumer goods, service industries, history, sports, education, etc. benefit from understanding data. Also, there are many more practical, real-life applications where the use of Tableau in data visualization is undeniable.

Moreover, the prolific nature of visualization makes it one of the most useful skills to develop. Data scientists can now convey their points more visually, be it in a slide deck or a dashboard. The better you can also leverage that information.

It’s, therefore, increasingly valuable for professionals to use data to make critical business decisions and use visuals to tell different stories.

What Determines Data Visualization Choices?

As aforementioned, data visualization using Tableau is a critical step to making sense of data. Visuals help data scientists to transcribe and present their data and data correlations in the simplest way possible. Data analysts use different techniques, most of which include diagrams, charts, maps, etc.

This makes it even more important to choose the right technique and set up to help make your data truly understandable. In contrast, using the wrong tactics in your data presentation may prevent your information from reaching its full delivery potential.

Factors That Influence Types of Data Visualization and Choices

Audience

It’s critical to adjust all forms of data representation to target your audience. You must, however, note that simplicity is the key in this case.

Content

The type of data that you decide to use will also determine your tactics. For instance, it’s the metrics that can change over time. Most data scientists would use line charts to highlight and explain the dynamics of the data you intend to share. Also, bar charts would be the better option for use in comparative analysis.

Context

You can always use different approaches to how your graph looks. This allows your audience to read and analyze your data without stress, depending on the context. If, for instance, you want to emphasize a specific figure, you may want to use shades of one color. If possible, pick bright colors for the most significant elements on your chart. Using contrasting colors for different elements also helps to elaborate your data more.

Dynamics

There are different types of data, all of which imply a different rate of change. You can, for instance, measure financial results monthly or yearly even as time series and tracking data changes constantly. Therefore, depending on the that you want in your data, you may consider using dynamic representation or static visualization.

Purpose

Data visualization using Tableau has severe influences, depending on the way it’s implemented. As such, for you to make more complex analyses of your systems or combine different forms of data to ensure a more profound view, data scientists must compound visuals into dashboards with filters and controls. Dashboards may, however, be necessary, especially when showing single or occasional data insights.

The type of data visualization techniques that you use depends on several factors, like the ones mentioned above. Luckily, there are several statistics homework help services that you can use to ace your homework on data visualization. My Homework Writers ranks as one of the best sites to use to have your data visualization assignments done by professional content creators. myhomeworkwriters.com has a team of writers who specialize in providing over and above quality content.

Tableau Essentials: Chart Types

data visualization using Tableau is a powerful tool that creates different types of charts to display data in different forms. Basically, tableau charts are created using different measures and dimensions we choose.

It’s also critical to note that not all charts work similarly. For instance, when using geography like a country or state, then using a Map chart makes much more sense. Similarly, the Lines chart is the best consideration for data dealing with dates.

For dimension data, the Bar chart will appear by default, whereas for measure first and dimension, you’ll get a more textual representation of the data.

Below is a list of different types of tableau chart types:

• Area chart
• Bar chart
• Box and whisker plots
• Bullet chart
• Scatter plot
• Pie chart
• Bubble chart
• Line chart
• Histogram
• Highlight table
• Grant chart
• Heat map

Obviously, there’s a lot to cover when dealing with data visualization using Tableau and its different chart types. Then there is the issue of the different methodologies used in each of the above tableau charts.

Data Visualization in Action

Of course, actually seeing data visualization in action is one of the best ways you will understand it. It may look like a crazy concept, but it’s very effective. With data everywhere online and data visualization galleries, knowing where the work starts can be an overwhelming process. Tableau’s own gallery shows loads of visualizations made using Tableau public tools.

Different Types of Data Visualizations

Most people’s first thoughts of using data visualization using Tableau almost always go to simple pie charts and bar graphs. Notably, these are an integral part of visualizing data. Using Tableau also remains a common baseline for numerous data graphics. You must, however, compare the right visualizations with the right sets of information lest you end up with even harder to understand data.

Simple graphs are, however, just the tip of the iceberg as there’s a host of visualization methods that you can use to present data in more effective and interesting ways.

Categories of Big Data Visualization

tableau visualization types help data scientists to be more highly effective in their data presentation techniques. You must, however, identify and use the right types of visualizations to ensure that the data you use enables you to intercept and make critical decisions based on what they observe. You can do this by creating the proper visualizations and understanding the different forms of visualizations too.

Temporal

Data visualizations belong in this category only if they satisfy two conditions. These conditions include:

• One-dimensional
• Linear

Temporal visualizations feature lines that either overlap each other or stand alone, with specific times.

The plus? These are some of the most commonly used charts in data presentation. You can recognize them in school and in the workplace. They, therefore, enable the audience to understand what the data represent:

Data visualization examples for temporal data include:

• Timeliness
• Scatter plots
• Line graphs
• Time series sequences
• Polar area diagrams

Hierarchical

Visualizations that belong in this category are those known to order groups within larger groups. This makes hierarchical visualizations best suited for data scientists looking to display groups of information. This is especially true is they flow from one origin point.

On the downside, these graphs tend to be more complex and extremely difficult to read.

Data visualization examples for hierarchical data include:

• Ring charts
• Sunburst diagrams
• Tree diagrams

Network

Network data visualization is another critical type of visualization that connects deeply with other datasets. This type of data visualizations also shows how datasets relate to one another in a network. This visualization demonstrates relationships between datasets minus all the wordy explanations.

Data visualization examples for networks data:

• Word clouds
• Matrix charts
• Alluvial diagrams
• Node-link diagrams

Multidimensional

These data visualizations do exactly what their names suggest. They have multiple dimensions, meaning they always have two or more variables in any mix to create more of a 3D data visualization. As such, these types of visualizations rank as the most vibrant due to their many concurrent datasets and layers.

Multidimensional visualizations are very eye-catching, making them easier for the brain to break down and understand compared to the more complex visualizations.

Data visualization examples for multidimensional visualizations include:

• Histograms
• Pie charts
• Scatter plots
• Stacked bar graphs
• Venn diagrams

Geospatial

Also known as spatial data visualizations. This type of data visualization relates more to real-life physical locations. Geospatial visualizations help to overlay familiar maps with data points that make it easier for the audience to understand. These types of visualizations are, therefore, more commonly used in displaying sales or acquisitions over time. This type of data visualization is most recognizable in its use in political campaigns and to display market penetrations like those of multinational corporations.

Data visualization examples for geospatial data:

• Cartogram
• Density map
• Heat map
• Flow map

Data Visualizations and Big Data

Data visualization using Tableau has helped make the use of big data more critical than ever before. Several companies now use machine learning and other forms of data collection to gather massive amounts of data in very short timeframes. Tableau public, for instance, has helped make data more available, easy to comprehend and even explain to the general population. Data visualization provides a more effective means to speed things up, especially when presenting information to business owners.

Business owners and stakeholders can now receive easy-to-understand data. It goes well beyond the typical techniques that companies use in normal visualizations like pie charts, corporate graphs, and histograms.

Instead, data visualization uses more complex representations like fever charts and heat maps to make data easily understandable.

Big data visualization also requires the use of powerful computer systems that collect raw data, analyze and process it into graphical representations that people can use to draw insights more quickly.

While big data visualizations can be beneficial, they sometimes also pose several disadvantages to organizations that use them. They can:

• Require involvement from IT and management. Big data visualization requires the use of powerful computer hardware, more efficient storage systems, and a move to the cloud.
• To get the most out of using big data visualization tools, you must hire a visualization specialist who will identify the best datasets and visualization styles that guarantee the optimization of data.
• The insights that big data visualization provide are only always as accurate as the information that they visualize. It’s, therefore, essential to have the right people and processes in place to monitor and control the quality of data, metadata, and data sources of the organization.

Different Uses of Data Visualization

data visualization using Tableau has many uses that corporations can benefit from. Consequently, each data visualization type has different uses and processes. Below is a detailed overview of some of the common ways you can use data visualization:

Changes Over Time

Perhaps one of the most common and basic uses of data visualization is its versatility. This, however, doesn’t make it any less valuable. In any case, most data have an element of time involved. Thus, the first step in most data analyses is to monitor how the collected data trends over time.

Determining Relationships (Correlations)

Identifying relationships is one of the most valuable uses of data visualization. It can be extremely difficult to determine the relationships between two variables without the right visualization. Still, you must know and be aware of the relationships between data. Generally, this illustrates the value of visualization in data analysis.

Determining Frequency

Frequency is another fairly basic use of visualization. It also applies to the data that involves time. In cases where time is involved, it’s only logical that you determine how often different relevant events occur over time.

Examining A Network

Marketing research is perhaps the best example of examining a network using data visualization. Marketing professionals must always be in the loop regarding their target audience and how they can deliver the most impactful message to them. They, therefore, study and analyze the whole market to identify the best audience clusters, influencers within clusters, bridges between clusters, and outliers.

Analyzing Value and Risk

Analyzing and determining complex metrics like value and risk requires the use of several different variables, making it almost impossible to analyze data accurately, especially within a plain spreadsheet. Generally, data visualization can be using color to code formula to showing which business opportunities are valuable and which ones are risky.

Scheduling

Data visualization using Tableau also helps in planning out a timeline or schedule for complex projects to make data less confusing. Take the Gantt chart, for example. This type of chart solves the scheduling issue by illustrating different tasks within each project clearly and how long each project should take to complete.

Reasons to Use Tableau in Data Visualizations

Tableau generally empowers everyone, data analysts and audiences, to see and understand data. It’s typically a business intelligence platform that allows organizations to explore and analyze data quickly. Tableau’s drag and drop feature also helps in discovering different trends and outliers. Data scientists can also use it to connect to the data that audiences care about.

Data Manipulation

Concerning data manipulation, understanding the collected data, gathering reports from different databases, consolidating the collected data, and difficulties in deploying different software are among the major problems. Tableau ranks as the best analytics tool for its usefulness in implementing data analytics.

Big Data Revolution

Big intelligence platforms help in resolving issues, especially ones related to big data. This makes it a key market gamechanger in the big data revolution. Business intelligence software is critical in turning data into insights to make it more profitable.

Visualizing data in excel sheets has, however, been terminated and isn’t manageable because of the large exchange of big data from databases.

Data Integration

Compatibility issues don’t often occur as Tableau software helps businesses to build new integrations with different programming languages. Connecting data has never been easier with the ever-increasing demand for data entry. All this is now possible with Tableau.

Essential Tableau Learning Resources in Visualization

There are many training resources available that you can use with Tableau that make it a rich recourse for gaining knowledge. Here are some of the most useful resources that you can use to help you master the toolset of Tableau:

Tableau Tutorials

tableau data visualization tutorials offer several training resources to gain basic knowledge. You can now find video tutorials that go for more than 5+ hours and other options within the tableau environment.

Tableau Desktop Free Trial

You probably already have Tableau installed on your computer but maybe haven’t started exploring the software or using its features. The best part about using the Tableau free desktop free trial version is that it’s fully functional throughout its trial period, which runs 14 days. This should be enough time for you to try things out and get a feel of the program and its many features.

Tableau Training

This is perhaps one of the best data visualization projects in Tableau. This process provides you with the basics of Tableau and eventually provide a deeper understanding of all of its concepts. You can also benefit from its many live classes and other training sources to help you record and review classes later.

Tableau Community

The tableau community is also one of the most passionate and engages user bases you’ll find on the internet. What with all the buzz that data visualization using Tableau has created, especially in the democratization of big data, which has enabled it to spread even more widely.

The community has users proficient in various levels, always looking to discuss Tableau and its many processes. You will also find a YouTube channel to help you implement different tableau methods.

Tableau Public

This is a more advanced version of using the program to interact with community members of Tableau. Tableau public provides individuals with a forum to create and share their reports and visualizations with the tableau community.

Additionally, joining the tableau community also comes with other benefits. First, it allows you to analyze what other members of your team are doing, like how dashboards and visualizations are being created.

Of course, having the best data presentations will attract more views, shares, and comments. This makes using the tableau community even more useful in collecting and implementing different ideas into your work.

Conclusion

Effective data visualization using Tableau is one of the most critical and final steps of data analysis. Without it, you would miss out on valuable insights and messages that can help make your business more productive. It’s, therefore, vital that you understand data visualization and how you can apply it in your business.