What is Tableau: Overview
Tableau is a data visualization software that is used for data science and business intelligence. Tableau can create a wide range of different visualization to interactively present the data and showcase insights. It comes with tools that allow to drill down data and see the impact in a visual format that can be easily understood by any individual. Tableau also comes with real-time data analytics capabilities and cloud support. Here we will discuss the different versions of Tableau, it benefits and implementation. We will see how Tableau is different from Microsoft Excel and other spreadsheet tools.
An Overview of Different Tableau Products:
- Tableau Desktop:
Tableau Desktop is a business intelligence and data visualization tool that can be used by anyone. It specializes in transforming boring tabulated data into eye-candy graphs and representations. With tableau desktop, you can enjoy real-time data analytics by directly connecting to data from your data warehouse. You can easily import your data into Tableau’s data engine from multiple sources and integrate them by combining multiple views in an interactive dashboard.
- Tableau Server:
Tableau server comes with all the features of Tableau Desktop along with networking capabilities. With Tableau Server you can share dashboards created in Tableau Desktop. This makes it an ideal choice for enterprise-level projects and reporting. When leveraged with real-time data processing this can become a very dynamic and powerful tool for ensuring instant communication of data and insights.
- Tableau Online:
This is a hosted version of Tableau server. It is usually powered with the help of cloud computing to make the software available to everyone. This enables faster and easier access to business intelligence on the go. You can publish dashboards created in Tableau Desktop and share them with colleagues.
- Tableau Reader:
This is the free desktop version of Tableau. Its features are limited to only viewing the visualizations created in Tableau. This means that you can filter and drill down the data but cannot edit or perform any kind of interactions or edits.
- Tableau Public:
This is a free version of Tableau software which can be used to make visualizations. The downside is that you need to save your workbook and visualizations in the Tableau Server which can be accessed by anyone.
Tableau provides solutions for all kinds of industries, departments, and data environments. Following are some unique features which enable Tableau to handle diverse scenarios.
- Speed of Analysis − As it does not require high level of programming expertise, any user with access to data can start using it to derive value from the data.
- Self-Reliant − Tableau does not need a complex software setup. The desktop version which is used by most users is easily installed and contains all the features needed to start and complete data analysis.
- Visual Discovery − The user explores and analyzes the data by using visual tools like colors, trend lines, charts, and graphs. There is very little script to be written as nearly everything is done by drag and drop.
- Blend Diverse Data Sets − Tableau allows you to blend different relational, semistructured and raw data sources in real time, without expensive up-front integration costs. The users don’t need to know the details of how data is stored.
- Architecture Agnostic − Tableau works in all kinds of devices where data flows. Hence, the user need not worry about specific hardware or software requirements to use Tableau.
- Real-Time Collaboration − Tableau can filter, sort, and discuss data on the fly and embed a live dashboard in portals like SharePoint site or Salesforce. You can save your view of data and allow colleagues to subscribe to your interactive dashboards so they see the very latest data just by refreshing their web browser.
- Centralized Data − Tableau server provides a centralized location to manage all of the organization’s published data sources. You can delete, change permissions, add tags, and manage schedules in one convenient location. It’s easy to schedule extract refreshes and manage them in the data server. Administrators can centrally define a schedule for extracts on the server for both incremental and full refreshes.
Tableau vs. Excel:
People are easily confused between Tableau and Microsoft Excel. For a person who has never used these tools in depth, they appear to be similar to each other. Both these tools can be used to create interactive visualizations and have the tools to analyze data. But the approach each of these tools uses to reach the insights is very different.
Tableau is a data visualization tool, meaning that it formats data in the initial stage into pictorial representations. As and when users drill down the data, the representations change accordingly. Excel, on the other hand, needs the user to first analyze data in tabular format and then opt for visualizations for better understanding and insights. Here are two key differences between the Excel and Tableau:
- In Excel, you need to know the insights you are looking for and accordingly place the formulas and arrange tabulation. While Tableau can take you to insights you never thought would exist. Using interactive visualizations and data drilling tools you can freely explore data without any specifics in mind.
- While both Excel and Tableau support real-time data visualizations, Excel needs programming to enable such processing while Tableau uses an easy and interactive approach to the same. Overall Tableau is designed for business executives enabling them to find correlations in data without any need for specialized knowledge of data science.
3 Benefits of Using Tableau:
1. Awe-inspiring Visualizations:
Tableau provides great data visualizations at scale. It takes unorganized data and provides a range of visualizations for a deeper understanding of trends. It makes it easy for users to analyze data by using differentiating factors like colors, labels, and shapes. By allowing for easy switching between different visualizations it brings in greater context as we drill down the data and explore on a granular level.
2. Greater Insights:
Tableau allows the user to analyze data without any specific goals in mind. You can freely explore the visualizations and look for different insights. By using “what if” queries you can adjust data hypothetically and visualize data components dynamically for comparisons. When combined with real-time data these capabilities enhance dramatically.
3. Ease of Use:
Tableau is a highly interactive solution for business intelligence. It is designed for people who don’t have coding skills. With Tableau, anyone can visualize and understand data without the need for any advanced skills in data science. As compared other tools Tableau showcases visuals in a presentable way hence, they can be used in presentations and reports. All of this makes Tableau a great tool not only for data scientists but also for business executives.
Implementation of Tableau:
Tableau comes with a variety of implementation and consulting options. It comes with quick-start options for small-scale deployments which can complete the setup in just a few hours. While for complicated enterprise-level deployment it comes with the following four-step process:
- The first deployment phase involves IT planning, architecture consulting, pre-install check-up, server set-up and verification, and security configuration.
- In the next step involves working on data and its migration – this includes data modeling, data mining, data extraction, data sources and business workflow.
- With this step, the company ensures that employees are actually able to use the tool. A two-day classroom training is provided for Tableau fundamentals, hands-on advanced coaching, and for building and formatting visualizations.
- The final step helps companies expand Tableau usage across their business. Implementation workshops are conducted where topics like the evaluation of action plans and the process of defining measurable outcomes are discussed.
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Tableau Desktop was one of the furst players to focus on self-service business intelligence (BI) tools and it’s this maturity that helps it remain one of our three Editor’s Choice winners in this category. In the last few years, the fast rise of Big Data, Internet of Things (IoT), and their associated analytics burden has Tableau feeling pressure from a slew of new and increasingly innovative competitors. Tools such as our other Editors’ Choice winners IBM Watsonwith its intuitive, semantic language interface and Microsoft Power BI with its very familiar user interface (UI), and free starter price tag, are giving Tableau a serious run for its money, perhaps for the first time.
This is likely part of the reason why Tableau moved to a subscription model in 2017, now starting at $35 per user per month for the Desktop version and $42 per user per month for the Online version. Yes, subscription models are the trend in recent years, but that represents a significant slash from Tableau’s $999 per user per year—a price cut the company felt no need to make in years past.
The reduced cost makes it easier for individuals and companies alike to opt for Tableau, and those that do will have little cause to complain. It’s a mature product and very stable, and being able to add the phrase “Tableau-proficient” on your resume can be a big plus with many employers. However, that’s not to say that Tableau is resting on its laurels because, faced with the advanced capabilities of its new competition, it can’t. If it does, the company may not hold on to its market perception, hence, the price cut.
If performance is a concern for your business, Tableau has attempted to answer with Hyper. Introduced in January, 2018, Hyper is a new data engine that Tableau claims will provide its customers with up to 5x faster querying speed over previous iterations and up to 3x faster extract creation speed. It also added Tableau Server on Linux and embedded tooltip data visualizatins.
The Learning Curve
Tableau Desktop—like Chartio$2,000.00 at Chartio—still assumes too high a level of sophistication in its users if it hopes to progress further in a market that’s swiftly moving towards general users rather than data specialists. Tableau easily found footholds to sprint to the top earlier because experienced business and data analysts were desperately seeking better tools and a way around IT bottlenecks. But that market is now largely saturated. The challenge today is to grow the market through distributed BI and data democratization—meaning, tools must appeal to and be usable by nearly anyone in a given organization.
This is why IBM Watson Analytics$360.00 at IBM and Microsoft Power BIFree at Microsoft are such serious threats to Tableau. IBM Watson Analytics, for example, has found a stronghold in healthcare where doctors, nurses, and other medical professionals understand data but not the language of data science. The highly intuitive, semantic language in the UI enables them to work with data with little hassle or learning curve. Ditto for Microsoft Power BI, which has found a stronghold in organizations that tend to have few data-trained people yet significant need for data analysis and a familiarity with everything Microsoft.
Still, Tableau is a great product with a feature set that easily rivals that of either of the competitors just mentioned. If customers are willing to eat its learning curve, then Tableau can almost certainly fulfill any data analytics need. And, if the company evolves its UI in the future, then there’s every chance it might regain its solo position as king of self-serve BI.
The UI aside, loading and extracting data in Tableau is a breeze—arguably the easiest of the systems I tested. It has plenty of connectors, and users can choose to work with the data live or extract and load it to Tableau. It’s just a matter of clicks starting at Data Source and then choosing your setup by clicking the boxes appropriate to your needs or preferences.
When I connected to CSV files, it instantly connected to all of them in the same group rather than waiting on me to select each file. That was much faster and easier than in much of the competition, even IBM Watson or Google AnalyticsFree at Google. Color me impressed, as establishing data connectivity is the part of data analysis I find most annoying.
Once you’ve established a connection to your data sources, however, there’s the data preparation task, which means cleaning the data and making nonconforming entries adhere to your established fields. This is a little trickier as you must hunt your way around the page until you find the right pull-down menus and/or spilt commands for sorting and data manipulation. Even so, I moved through the entire process in a matter of minutes once I got the hang of it.
The Discovery Process
To an experienced data analyst, using Tableau is fairly straightforward or, at least, relatively easy to figure out. But the absence of prompts, popups, and quick help links in the presence of esoteric terms and configurations means that many new users will require substantial training before the tool proves its full worth. In short, Tableau is not a tool that inexperienced or low-skilled users can poke around and easily figure out. This means it’ll present some hurdles to organizations that are looking to become fully data democratized.
From the Data Source page, I merely clicked on the Sheet 1 tab to go to a worksheet. My data’s dimensions were automatically displayed and I needed only to drag and drop the relevant data sets and then choose a visualization with which to explore my results. Click the Show Me button to find visualization and other options. Tableau presented me with a wealth of visualization options, but, again, a user with little experience or understanding of data science concepts isn’t likely to know what to drag to where, much less how to form a sophisticated query.
For experienced data analysts, it’s easy to pause for automatic data refreshes, sort records, save, share, and other functions by clicking on familiar icons at the top of the screen. I quickly moved through the data and built a dashboard to share in a matter of minutes. Tableau works so fast and so flawlessly that a user can be forgiven for thinking the tasks simplistic. But they aren’t; it’s just that the processing engine and analytics are that efficient and powerful. That is to say that, while Tableau needs to further simplify its UI to fully capitalize on the distributed BI movement, I fully appreciate how far Tableau has come in reshaping the BI industry to date.
Tableau offers a variety of visualizations including the old familiar standbys. It also offers guidance on how to best use each visualization when you click on them, which is particularly helpful when the analysis kicks out a weird-looking visual you want to fix.
The potential downside to Tableau is that users need some training on it to get the full benefits of all the functionality built into this tool. The danger is that users may try to skip the training and just feel their way around the UI. That will work for more basic queries but it will eventually hold the organization back—not because the tool can’t do the work, but because users don’t know how to make it work at harder or more nuanced tasks.