Tableau is the fastly growing and powerful data visualization tool. Tableau is a business intelligence tool which helps us to analyze the raw data in the form of the visual manner; it may be a graph, report, etc.
Example: – If you have any data like Big Data, Hadoop, SQL, or any cloud data and if you want to analyze that given data in the form of pictorial representation of data, you can use Tableau.
Data analysis is very fast with Tableau, and the visualizations created are in the form of worksheets and dashboards. Any professional can understand the data created using Tableau.
Tableau software doesn’t require any technical or any programming skills to operate. Tableau is easy and fast for creating visual dashboards.

Why use Tableau?

Here are some reasons to use Tableau:
• Ultimate skill for Data Science
• User-Friendly
• Apply to any Business
• Fast and Easy
• You don’t need to do any Coding
• Community is Huge
• Hold the power of data
• It makes it easier to understand and explain the Data Reports

Features of Tableau

• Data Blending: Data blending is the most important feature in Tableau. It is used when we combine related data from multiple data sources, which you want to analyze together in a single view, and represent in the form of a graph.
Example: Assume, we have Sales data in relational database and Sales Target data in an Excel sheet. Now, we have to compare actual sales with target sales, and blend the data based on common dimensions to get access. The two sources which are involved in data blending referred to as primary data and secondary data sources. A left join will be created between the primary data source and the secondary data source with all the data rows from primary and matching data rows from secondary data source to blend the data.
• Real-time analysis: Real-Time Analysis makes users able to quickly understand and analyze dynamic data, when the Velocity is high, and real-time analysis of data is complicated. Tableau can help extract valuable information from fast moving data with interactive analytics.
• The Collaboration of data: Data analysis is not isolating task. That’s why Tableau is built for collaboration. Team members can share data, make follow up queries, and forward easy-to-digest visualizations to others who could gain value from the data. Making sure everyone understands the data and can make informed decisions is critical to success.

What is Data Visualization?

Data visualization is a graphical representation of quantitative information and data by using visual elements like graphs, charts, and maps.
Data visualization convert large and small data sets into visuals, which is easy to understand and process for humans.
Data visualization tools provide accessible ways to understand outliers, patterns, and trends in the data.
In the world of Big Data, the data visualization tools and technologies are required to analyze vast amounts of information.
Data visualizations are common in your everyday life, but they always appear in the form of graphs and charts. The combination of multiple visualizations and bits of information are still referred to as Infographics.
Data visualizations are used to discover unknown facts and trends. You can see visualizations in the form of line charts to display change over time. Bar and column charts are useful for observing relationships and making comparisons. A pie chart is a great way to show parts-of-a-whole. And maps are the best way to share geographical data visually.
Today’s data visualization tools go beyond the charts and graphs used in the Microsoft Excel spreadsheet, which displays the data in more sophisticated ways such as dials and gauges, geographic maps, heat maps, pie chart, and fever chart.

What makes Data Visualization Effective?

Effective data visualization is created by communication, data science, and design collide. Data visualizations did right key insights into complicated data sets into meaningful and natural.
American statistician and Yale professor Edward Tufte believe useful data visualizations consist of? complex ideas communicated with clarity, precision, and efficiency.

To craft an effective data visualization, you need to start with clean data that is well-sourced and complete. After the data is ready to visualize, you need to pick the right chart.
After you have decided the chart type, you need to design and customize your visualization to your liking. Simplicity is essential – you don’t want to add any elements that distract from the data.

History of Data Visualization

The concept of using picture was launched in the 17th century to understand the data from the maps and graphs, and then in the early 1800s, it was reinvented to the pie chart.
Several decades later, one of the most advanced examples of statistical graphics occurred when Charles Minard mapped Napoleon’s invasion of Russia. The map represents the size of the army and the path of Napoleon’s retreat from Moscow – and that information tied to temperature and time scales for a more in-depth understanding of the event.
Computers made it possible to process a large amount of data at lightning-fast speeds. Nowadays, data visualization becomes a fast-evolving blend of art and science that certain to change the corporate landscape over the next few years.

Importance of Data Visualization

Data visualization is important because of the processing of information in human brains. Using graphs and charts to visualize a large amount of the complex data sets is more comfortable in comparison to studying the spreadsheet and reports.
Data visualization is an easy and quick way to convey concepts universally. You can experiment with a different outline by making a slight adjustment.
Data visualization have some more specialties such as:
• Data visualization can identify areas that need improvement or modifications.
• Data visualization can clarify which factor influence customer behavior.
• Data visualization helps you to understand which products to place where.
• Data visualization can predict sales volumes.
Data visualization tools have been necessary for democratizing data, analytics, and making data-driven perception available to workers throughout an organization. They are easy to operate in comparison to earlier versions of BI software or traditional statistical analysis software. This guide to a rise in lines of business implementing data visualization tools on their own, without support from IT.

Why Use Data Visualization?

1. To make easier in understand and remember.
2. To discover unknown facts, outliers, and trends.
3. To visualize relationships and patterns quickly.
4. To ask a better question and make better decisions.
5. To competitive analyze.
6. To improve insights.

History of Tableau

Until the early 21st century, the Database were used to produce numbers and data. It’s the job of IT professionals to analyze the data and create reports.
Tableau was founded by Pat Hanrahan, Christian Chabot, and Chris Stolte from Stanford University in 2003. The main idea behind its creation is to make the database industry interactive and comprehensive.
Tableau appears in the era when there were already established companies like Cognos, Microsoft Excel, Business Objects, etc. It managed to climb the success chart with $3.8 billion of current market value.
Since then, the company is growing day by day.
In August 2016, Tableau announced and appointed Adam Selipsky as president and CEO of the company.

What made Tableau Popular?

The main logic behind creating this tool was developing a simple and user-friendly tool that can help you in creating graphs, charts, maps, reports as well as assist you in the next-gen concepts like the predictive and prescriptive analysis.

The worldwide business analytics market grew from $37.7 billion in 2013 to $59.2 billion in 2018, which translates to 9.4% compounded annual growth rate for the forecast period.
The main features that led Tableau Software to achieve success are-
• Powered by VizQL language, which makes it more flexible to pull data from any source.
• Provide Facility to the user with n number of visualization tools to customize the Tableau reports.
• All the complicated graphs and maps can be prepared with drags and drops method.
• Tableau data visualizations can be inserted with multiple platforms.
• It can analyze and display the data in real-time.
Some recently introduced versions of Tableau have the following features:

Tableau 9.0

• Smart maps
• Instant visual feedback
• Cashing and consolidation
• Scalable and faster tableau server

Tableau 10.0

• Cluster analysis
• Cross-database join
• Self-service at scale
• Multiple device support

Tableau has seen a considerable growth of 82% in its annual sales over the past seven years from $18 million in 2009 to $654 million in 2015, making it to obtain the highest position in the ranking chart. This company now ranks under top 10 BI tools giving competition to other old tools like IBM, Microsoft, Qlik, Oracle, etc.
A report by Forbes in 2016 shows that the total income of Tableau grew 32% in the first quarter to $172 million, with foreign income up to 52%. The company closed 268 transactions greater than $100,000, up to 8% per year. If Tableau continues to perform with the same speed, its net worth will be in the $3 billion counted as one of the top three BI companies in the world.

Advantages of Tableau

• Data Visualization:- Tableau is a data visualization tool, and provides complex computation, data blending, and dashboarding for creating beautiful data visualizations.
• Quickly Create Interactive Visualization:- Users can create a very interactive visual by using drag n drop functionalities of Tableau.
• Comfortable in Implementation:- Many types of visualization options are available in Tableau, which enhances the user experience. Tableau is very easy to learn in comparison to Python. Who don’t have any idea about coding, they also can quickly learn Tableau.
• Tableau can Handle Large Amounts of Data:- Tableau can easily handle millions of rows of data. A large amount of data can create different types of visualization without disturbing the performance of the dashboards. As well as, there is an option in Tableau where the user can make ‘live’ to connect different data sources like SQL, etc.
• Use of other Scripting Language in Tableau:- To avoid the performance issues and to do complex table calculations in Tableau, users can include Python or R. Using Python Script, user can remove the load of the software by performing data cleansing tasks with packages. However, Python is not a native scripting language accepted by Tableau. So you can import some of the packages or visuals.
• Mobile Support and Responsive Dashboard:- Tableau Dashboard has an excellent reporting feature that allows you to customize dashboard specifically for devices like a mobile or laptops. Tableau automatically understands which device is viewing the report by the user and make adjustments to ensure that accurate report is delivered to the right device.

Disadvantages of Tableau

Page 10 Image 6 — Disadvantages of Tableau

• Scheduling of Reports: – Tableau does not provide the automatic schedule of reports. That’s why there is always some manual effort required when the user needs to update the data in the back end.
• No Custom Visual Imports: – Other tools like Power BI, a developer can create custom visual that can be easily imported in Tableau, so any new visuals can recreate before imported, but Tableau is not a complete open tool.
• Custom Formatting in Tableau: – Tableau’s conditional formatting, and limited 16 column table that is very inconvenient for users. Also, to implement the same format in multiple fields, there is no way for the user that they can do it for all fields directly. Users have to do that manually for each, so it is a very time-consuming.
• Static and Single Value Parameter: – Tableau parameters are static, and it always select a single value as a parameter. Whenever the data gets changed, these parameters also have to be updated manually every time. There is no other option for users that can automate the updating of parameters.
• Screen Resolution on Tableau Dashboards:- The layout of the dashboards is distributed if the Tableau developer screen resolution is different from users screen resolution.
Example:- If the dashboard is created on the screen resolution of 1920 X 1080 and it viewed on 2560 X 1440, then the layout of the dashboard will be destroyed a little bit, their dashboard is not responsive. So, you will need to create a dashboard for desktop and mobile differently.

So, this brings us to the end of blog. This Tecklearn ‘Overview of Tableau and Data Visualization’ blog helps you with commonly asked questions if you are looking out for a job in Tableau Desktop. If you wish to learn Tableau Desktop and build a career in Business Intelligence domain, then check out our interactive, Tableau Desktop Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

Tableau Desktop

Tableau Desktop Training

About the Course

Tecklearn’s Tableau Desktop Training teach you how to transform raw data into interactive and shareable dashboards using Tableau. Our Tableau Course covers the necessary analytical skills to Advanced data visualizations by incorporating real-world use-case scenarios, labs, and exercises. Some of the topics included are Data Blending, Data Mapping, Graphs, creation of charts, and LOD expression by using different versions of Tableau, such as Tableau Desktop, Tableau Reader, and Tableau Public.

Why Should you take Tableau Desktop Training?

• The average salary of a Tableau Professional ranges between $108,697 to $158,000 per annum -Indeed.com.
• Tableau has been positioned as a Leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms – Gartner.com.
• Fidelity Investments, Capgemini, EY, Deloitte, EY, JP Morgan, Verizon, Facebook, Dell, General Motors, KPMG, Bank of America and 40,000 other MNCs worldwide across industries use Tableau.

What you will Learn in this Course?

Understanding Tableau

• Why Tableau
• Tableau Product Platforms
• Tableau Architecture
• Tableau Interface

Data Connection with Tableau Desktop

• Features of Tableau Desktop
• Connect to data from File and Database
• Types of Connections
• Data Blending
• Joins and Unions
• Tableau Desktop User Interface
• Basic project: Create a workbook and publish it on Tableau Online

Basic Visual Analytics

• Visual Analytics
• Basic Charts: Bar Chart, Line Chart, and Pie Chart
• Hierarchies
• Data Granularity
• Highlighting
• Sorting
• Filtering
• Grouping
• Sets

Advanced Visual Analytics

• Parameters
• Tool tips
• Trend lines
• Reference lines
• Forecasting
• Clustering

Calculations in Tableau

• Calculated Fields
• Numeric, String, Data, Logical, Addressing and Partitioning
• Table Calculations

Level of Detail (LOD’s) Calculations

Advanced Charts in Tableau

• Box and Whisker’s Plot
• Bullet Chart
• Bar in Bar Chart
• Gantt Chart
• Waterfall Chart
• Pareto Chart
• Control Chart
• Funnel Chart
• Bump Chart

Parameters

• What If Scenarios
• Parameter in CF (KPI control)
• Parameter in Filter (Top N)
• Parameter in reference line

Dashboards and Stories

• Introduction to Dashboards
• The Dashboard Interface
• Dashboard Objects
• Building a Dashboard
• Dashboard Layouts and Formatting
• Interactive Dashboards with actions
• Designing Dashboards for devices
• Story Points

Data Security in Tableau

Optimizations tips and tricks

Interacting with Tableau Server

Working with Tableau JavaScript API

Got a question for us? Please mention it in the comments section and we will get back to you.

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