As the world gets more globally connected, we see massive amounts of data generated at every turn. Companies large and small work with data to gain insight and make critical business decisions. For this data to be useful, however, it must be properly analyzed. Data analysis involves taking raw data and adding it to user-friendly data visualization dashboards.
What is Data Visualization?
Data visualization is the graphical representation of raw data using visual elements like charts, graphs, and maps. A visual depiction of this data can help analysts better understand and respond to data trends and patterns. It tells a detailed story that can be easily understood by an audience.
In the big-data ecosystem, massive amounts of information can be analyzed to make data-driven decisions that prevent losses and enhance a company's return on investment (ROI).
Why is Data Visualization Important?
The purpose of data visualization is to organize available raw data from multiple sources and formats in a manner that is easy to understand. Data is then curated and represented in a more straightforward form. Any noise or irrelevant information is removed, while useful information is highlighted.
Data visualization powers decision making by answering these key questions:
- What are the relationships and trends?
- Are there any outliers?
- Does visualization enable a comparison of multiple values over different periods?
- What trends do the datasets display?
As companies handle more data in real time, visualization becomes a key tool to make sense of the trillions of rows of data generated. While a delay can cost a company significant resources in manpower and money, practical data visualization tools can remove such losses while also increasing efficiencies.
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Caltech Post Graduate Program in Data ScienceExplore ProgramWhere is Data Visualization Used?
Weather maps, real-time air-quality indices, annual income, and expense report spreadsheets, and Facebook page statistics are examples of data visualization. It is used in many other ways to help stakeholders make quick decisions.
Some common use cases include:
Data visualization for Health Intelligence
Candlestick Visualization in Finance
Data Visualization in Sales
Tableau: The Ultimate Data Visualization Tool
There are many data visualization tools to choose from, whether you are a data scientist presenting findings to an audience or business intelligence (BI) user looking for a practical way to visualize large datasets. Tableau is one such tool. Created in 2003, the interactive data visualization software lets you create charts, graphs, maps, and graphics without using code. It can be installed as a desktop app for visual analytics or as a server solution for online and mobile reports. Tableau connects to almost any database to create compelling visualizations that can be shared with a simple click. It makes data analysis fast with its dashboards and worksheets, which has made it a popular choice in all industry sectors.
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Caltech Post Graduate Program in Data ScienceExplore ProgramWhy Learn Tableau?
The rich, intuitive BI Tableau dashboard gives senior executives and data scientists a simple way to tap into large datasets for visualization and reports within minutes. Because of this, Tableau has emerged as the preferred BI software for presentations, reports, and actionable insights. It also allows nontechnical users to publish interactive data visualizations on any topic, as well as create customized dashboards.
Tableau10 runs 100 times faster than existing solutions and can be integrated with the R programming language for a seamless visual analytics interface, making the exploration of complex data much simpler.
So what else makes Tableau the top data visualization choice for most businesses?
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Ease of Use
The intuitive drag-and-drop capability makes it simple for any users—no programming required -
Compatibility
Tableau can work with multiple sources, including Microsoft Excel, SQL servers, and cloud-based data repositories -
Power
Disparate and massive data sets can be leveraged for multiple insights via smart dashboards -
Real-time Reporting
Tableau unlocks the potential of data with reporting dashboards and custom-made reports that can be generated in seconds. Real-time tracking of resources and assets also lets analysts see both optimally performing and nonfunctioning channels -
Smart Dashboards
Multiple views can be combined for deeper insights and shared online or via mobile devices using a simple command.
10 Tips for Learning Tableau
Aside from data visualization, Tableau is useful for data mining, data cleaning, statistical computations, and graphs. But like any tool, it is crucial to learn how to use it to take advantage of its many features. Here are ten tips for learning this powerful software
1. Look at Tableau Graphs and Data Visualizations Online
See how Tableau is applied in various scenarios and industries to see how it can be used.
2. Follow the Tableau community
Subscribe to Tableau-related lists, and follow other users within your industry who are applying Tableau to get their feedback on the design and user experience.
Check out guides and resources like the Tableau Reference Guide. Follow Twitter handles like @tableaupublic and @HighVizAbility and data visualization pages like Storytelling with Data, the Tableau blog, and other similar blogs.
3. Read up on Tableau offline
Check out this list of data visualization books from Tableau or read one of these starter books to learn the software:
- Tableau Your Data! Fast and Easy Visual Analysis with Tableau Software, by Daniel G. Murray
- Communicating Data with Tableau, by Ben Jones
- Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master, by Ryan Sleeper
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Caltech Post Graduate Program in Data ScienceExplore Program4. Identify and Align Skillsets
Tableau helps piece together information and unify it into an informative, visually appealing data story. Creating this calls for skill sets that include:
- Working knowledge of data
- Experience with tools like Microsoft Excel and Google Sheets
- Basic data analysis experience
- Math and statistical knowledge
- Analytical and visualization skills
- The ability to filter, sort and group data
- Programming knowledge for advanced features like Tableau Developer and Tableau Dashboard Designer
5. Practice
The best way to learn new software is to use it. Download and install the desktop version, and start exploring its features. Use Tableau Public to publish interactive data visualizations online. Download existing workbooks as a learning resource.
6. Find free datasets
Use these free practice datasets, new contributed datasets, datasets shared on community forums, and this list of public datasets, and read these tips to look for free datasets to work within Tableau. Search Kaggle for open data to use as practice.
7. Create a Tableau portfolio
Practice your data visualization skills by building an online Tableau portfolio and starting a dashboard project. Use the active Tableau Public Gallery for inspiration.
8. Build a Tableau project footprint online
Share links to your dashboard or post them to GitHub repositories, and explain your method. Look at your project from a fresh viewpoint by getting suggestions and feedback from the active Tableau community.
9. Get involved
Engage with a Tableau user group, and meet local Tableau users. Refer to this map to find a group near you. Also, join Facebook groups for community insight.
10. Get trained
Because of its wide adoption across industries and applications, demand is high for Tableau certification. Watch some of Tableau's videos, check out available training through LinkedIn, or consider certification through Simplilearn, where you can master the desktop version and prepare for the Tableau Desktop 10 Qualified Associate Exam.
Take it one step further with Simplilearn's Data Science Certification, where you can gain hands-on experience with not only Tableau but other essential tools like Hadoop and Spark, as well as programming languages like R and Python. No matter which one you choose, start training today to take your career to the next level.
