Tableau vs Excel: Which One Should You Use? | 365 Data Science (2023)

Tableau vs Excel: Which One Should You Use? | 365 Data Science (1)

Tableau vs Excel is a hot discussion topic in the data science community. Indeed, Excel is not a top skill that employers are looking for in your resume. But it has been around for ages and you are probably familiar with tons of useful tricks for data cleaning and analysis. In comparison, Tableau is a newcomer that has many aspiring data scientists wondering what its benefits are.

In this article, we will take a deeper look at the Tableau vs Excel dilemma and whether you should be using one over the other.

You can watch our dedicated video on the topic embedded below or scroll down to keep reading.

Table of Contents

  • What is Tableau?
  • What Is Tableau Useful for Business Intelligence?
  • Tableau vs Excel: What Are the Differences?
  • What Are the Advantages of Tableau over Excel?
  • When to Use Tableau vs Excel?
  • Tableau vs Excel: Next Steps

What Is Tableau?

In a nutshell, Tableau is a BI software that allows non-technical users to visualize their data and work with it almost immediately, lowering know-how barriers dramatically.

While both Tableau and Excel are used for data analysis, it is top-notch tools such as Tableau are what distinguishes successful Business Intelligence (BI) analystsfrom average performers. According to BI Survey, it has grown to become one of the most popular business intelligence tools in the world.

If you are interested in learning more about the data science field and the career opportunities it offers, go ahead and download our free data science career guide.

(Video) Excel vs Tableau - Which Tool To Use? | Tableau & Excel Difference | Tableau vs Excel | Simplilearn

Why Is Tableau Useful for Business Intelligence?

In the past, business analysts needed the help of IT personnel who would assist them in gathering raw data and preprocessing it. Only then could they start working on its visualization.

Essentially democratizing data viz, Tableau allowed BI analysts to be independent. With its easy-to-navigate interface, non-technical people can easily load data into the program and start playing with it, extracting meaningful and intuitive insights in the forms of visualizations. This way, analysts don’t have to spend too much time on numbers which provide limited insights and instead focus on data that matters.

This is why we can confidently say that Tableau is an indispensable tool in the arsenal of most corporate business intelligence analysts, data analysts, and data scientists.

Tableau vs Excel: What Are the Differences?

Many people are uncertain about the difference between Tableau vs Excel, which is specifically designed for spreadsheets. However, it’s important to note here that both tools serve different purposes. Using Tableau doesn’t necessarily mean you can forget about Excel and vice versa.

While Excel is not as powerful or intuitive as Tableau when it comes to , the latter is not optimal as a data creation tool.

Although Tableau has several database management functionalities – such as creating duplicate sheets or extracting totals – the program isn’t the best solution when you would like to perform multiple operations with your data before you start analysing it.

Moreover, this tool isn’t great for multi-layered calculations. It is able to calculate its own custom fields, but it shouldn’t be used as a spreadsheet tool for multi-layered calculations such as the preparation of a budget in Excel.

(Video) Tableau vs Excel: When to use Tableau and when to use Excel

What Are the Advantages of Tableau over Excel?

Data Visualization

Where Tableau surpasses the competition is in data visualizations such as charts. It is a very smart program that allows you to present your work in a more powerful way compared to Excel.

For example, when you work with geographical data, there is no way Excel could interpret the cells in your spreadsheet as a geographical location. On the other hand, Tableau recognizes that and allows you to visualize such data and see how a variable is distributed geographically.

Combination of Several Types of Charts

Moreover, unlike Excel, Tableau allows you to combine several types of charts and build meaningful, interactive dashboards that facilitate additional analysis.

Once you visualize your data, you can easily dig deeper and explore its granularity, finding the reason for unusual spikes or investigating certain trends.


Even novice Tableau users would be able to save a significant amount of time if they transfer their pre-designed existing spreadsheets into the software. Uploading new data and updating visuals is more rapid in Tableau vs Excel.

SQL Integration

If you have an enormous amount of data in SQL, you may have difficulty interpreting it without some help. Integrating SQL and Tableau is one way to overcome that problem. The visual analytics program has the power to quickly deliver sophisticated visual analyses, so the tool is a perfect match for the programming language.

If you’d like to learn how to maximize your data analysis and coding abilities, then our SQL + Tableau course might be the perfect fit.

(Video) Tableau vs Excel | Difference Between Tableau vs Excel | Excel vs Tableau | Intellipaat

When to Use Tableau vs Excel?

A competent analyst needs both Excel and Tableau, given that they serve different purposes. While Tableau is superior when it comes to visuals and dashboards, Excel is a spreadsheet tool we need in order to perform multi-layered calculations. The two software programs complement each other to ensure your analysis is first-class.

In the same way a combat soldier carries a rifle and a pistol at the same time and uses them under different circumstances, a business analyst should know how to work with both Excel and Tableau and apply each of them when needed.

Tableau vs Excel: Next Steps

If you’re an aspiring data scientist or business analyst who is just now getting acquainted with Tableau, it’s a good idea to experiment with the different ways of incorporating other software such as Excel or SQL. Not only will this boost your skillset, but you have the chance to impress your future employers as well.

And if you’re looking for the right place to start learning, you’ve arrived at your destination! The 365 Data Science Program offers you self-paced courses led by renowned industry experts. Starting from the very basics all the way to advanced specialization, you will learn by doing with a myriad of practical exercises and real-world business cases. If you want to see how the training works, start with our free lessons by signing up below.


Why Do Companies Use Tableau?

Companies use Tableau because of its user-friendly interface and ability to create meaningful data visualizations. Essentially, data scientists or business analysts who are looking to drive their company’s decision-making process benefit from this tool as it is easy to load and analyze data efficiently, in addition to presenting it in a clear and concise manner for stakeholders. In this way, companies that utilize Tableau internally save time and resources while optimizing the processes that rely on business insights to produce successful outcomes.

By using Tableau, professionals don’t have to spend hours upon hours pouring over numerical data, which might lead to limited perspectives, and instead can draw their attention on the data that actually matters. Moreover, presenting their findings to the company stakeholders – who probably don’t have much technical knowledge – is simplified by the data visualization tools that the software offers. In other words, the ability to create and combine different types of charts presents valuable insights in a more accessible format for those with a limited understanding of how data works.

Is Tableau Better Than Excel?

In some ways, Tableau is better than Excel, such as data visualization, however, the opposite can also be said: the latter comes out on top in certain aspects – for example, with its database management functionalities.

(Video) Excel vs Tableau: The Comparison You Should Know

Here, it’s important to understand that the two tools serve different purposes. While Tableau offers ways to create meaningful and intuitive visualizations, Excel is best suited for work on data cleaning and multi-layered calculations. Of course, Excel can also create some visualizations of its own, but it is not as skilled as its competitor. In reverse, you can certainly manage a database in Tableau, yet its functions are quite limited. If you’re tasked with a more complicated analysis or multi-layered calculations, such as preparing a budget, you’d be better off relying on the Microsoft Office tool.

Essentially, you need both Excel and Tableau to deliver truly great results. The two software programs complement each other to ensure your analysis is first-class.

What Are the Advantages of Using Tableau over Excel?

There are several advantages of using Tableau vs Excel, including data visualization, software integration, and efficiency.

In terms of presenting data to stakeholders, Tableau offers better opportunities to showcase your work and insights in a clear, persuasive manner. While Excel can also create different types of charts, with Tableau you have the ability to combine these types in order to maximize your findings’ impact. Another benefit is its ability to present geographical data – something that Excel cannot do. Essentially, the software tool is able to recognize and present cells in your database as geographical locations and present them in a map format.

Software integration is a big pro when considering working with Tableau. The tool allows you to combine several other software packages, including Excel, as well as work in conjunction with programming languages such as SQL and Python. This optimizes your analysis and draws even more valuable insights from the data at hand.

Finally, Tableau saves you time. Because of its easy-to-navigate interface and intuitive approach, analysts don’t spend too much time on unnecessary steps that will otherwise slow them down. Instead, they can directly extract business insights and drive the decision-making process forward.


Which is better Excel or Tableau? ›

Excel works well when you have to create quick, one-off reports. Tableau is helpful when you want a more detailed analysis of your business reports. Gain expertise in the latest Business analytics tools and techniques with the Business Analyst Master's Program.

Why use Tableau vs Excel? ›

Excel is suitable for customized sequential calculations; Tableau is a powerful tool for analyzing large, complex datasets through a user-friendly interface.

Is Tableau easier than Excel? ›

Tableau is very user-friendly and it's very easy to design a dashboard. Drag and drop is the key feature where the users find it easy to use this tool. Excel can connect to different external sources using plugins. Tableau has many sources to connect and also it has the capability of a cloud.

Do I need to learn Excel for Tableau? ›

One of the core skills that can help you learn Tableau faster is a solid understanding of spreadsheets, such as those in Microsoft Excel.

What can Tableau do that Excel Cannot? ›

While Excel can also create different types of charts, with Tableau you have the ability to combine these types in order to maximize your findings' impact. Another benefit is its ability to present geographical data – something that Excel cannot do.

Is Tableau a skill in demand? ›

Not only there is a great demand for Tableau experts, but there are also huge rewards on offer as well.

Is Tableau replacing Excel? ›

No, Tableau with Excel. Tens of thousands of companies use Tableau to see and understand their data. Most companies also use Excel, which has been the preeminent spreadsheet software for what seems like forever.

Why do data scientists use Tableau? ›

A Data Scientist needs to be able to quickly visualize the data they're dealing with before creating the model, and Tableau helps with that. Tableau is a great help for summarizing success metrics.

Why should I choose Tableau? ›

People learn faster. Tableau is built on the work of scientific research to make analysis faster, easier, and more intuitive. Analyzing data in a quick, iterative way that provides immediate feedback makes our products engaging, fun, and easy to learn.

How many hours does it take to learn Tableau? ›

Tableau is considered a relatively easy-to-learn data analysis and visualization tool and can be mastered by anyone with enough time and practice. On average, it takes most people between two and six months to learn this software. This process can take even longer if you're looking to master all of Tableau's functions.

Can a non IT person learn Tableau? ›

You don't need any technical knowledge to learn Tableau. In this Tableau dashboard tutorial, you will get a step-by-step understanding of this top business intelligence and tableau data visualization tool. Using Tableau, you can answer any business question provided the data exists for it in some format.

What is the disadvantage of Tableau? ›

Tableau does not provide the feature of automatic refreshing of the reports with the help of scheduling. There is no option of scheduling in Tableau. Therefore, there is always some manual effort required when users need to update the data in the back-end. Tableau is not a complete open tool.

Which spreadsheet is best for data science? ›

Excel is everywhere. It's the go-to analysis tool and spreadsheet software for many business users. With Tableau, it's even more powerful. With a drag-and-drop approach to visual analysis, Tableau makes exploring Excel data faster and easier.

Do you need Excel for data science? ›

Over time, spreadsheet programs such as Microsoft Excel and other early data analysis tools, are becoming less commonly associated with doing data science. However, Microsoft Excel has many capabilities which make it a useful tool for data scientists.

Is learning Excel necessary for data science? ›

Modern data science platforms could learn a lot from Excel. Data science is all about learning from data and learning from data means learning from the past. Excel has a lot of past experience to share.

Why Excel is not good for data analysis? ›

Spreadsheets create ample opportunities for accidental data loss, which makes it impossible to share crucial data and information. Most importantly, the data sets can't be shared in real-time, which happen to be a severe disadvantage.

When should you not use Tableau? ›

How NOT to use Tableau
  1. Replicate a report or chart designed in another tool. ...
  2. Try to show tons of data on one screen with a dozen (or more) quick filters. ...
  3. Try to connect every table in your database using the multiple tables option. ...
  4. Spend way too much time on formatting. ...
  5. Connecting to already summarised data.
Aug 27, 2013

What are two advantages and two disadvantages of Tableau? ›

Advantages and Disadvantages of Tableau:
High PerformancePoor Versioning
Mobile-FriendlyNo automatic refreshing of reports
Extensive customer resourcesNeed manual effort
Excellent mobile supportNot a comprehensive solution
4 more rows
Apr 20, 2022

What is the salary for 2 years experience in Tableau? ›

Tableau Developer salary in India ranges between ₹ 3.5 Lakhs to ₹ 9.5 Lakhs with an average annual salary of ₹ 5.8 Lakhs.

Can I get a job in Tableau without experience? ›

Yes, you can get a job after learning Tableau. If you've already learned Tableau and want to get a job in the field, there are several steps to take. First, ensure you have experience with the product—whether that means working on projects or just using it in your free time.

Can I get a job with just Tableau? ›

A person with knowledge of Tableau can get many roles like data analyst, business analyst, and Tableau consultant. Further in Business Intelligence, one can get roles like Business Intelligence Analyst, Business Intelligence Developer, and Business Intelligence Manager all of which are highly paying roles.

Do data scientists still use Excel? ›

Over time, spreadsheet programs such as Microsoft Excel and other early data analysis tools, are becoming less commonly associated with doing data science. However, Microsoft Excel has many capabilities which make it a useful tool for data scientists.

Does Tableau have a future? ›

The Future of Tableau Developers

With so much crucial data to deal with, small and large organizations need resources who can interact and analyze data based to gain vital insights from it. You will be amazed to know that Tableau is considered as the jewel among IT working professionals.

Is Excel getting outdated? ›

Excel isn't going away anytime soon. In fact, it has become more important than ever, as it is now central to the workflow for many and used alongside other data tools such as Power BI and Tableau, and ERP systems such as SAP and Salesforce.

Does Tableau come under data science? ›

Learn about Tableau's Data Science capabilities

Tableau business science is a new class of AI-powered analytics that brings data science capabilities to business domain experts.

Do I need to learn Python for Tableau? ›

To learn tableau we need not require any programming languages, coding, technical knowledge, or basics of using any tools. There is no prerequisite. We should be good at team skills, problem-solving, etc. Then we can easily learn tableau.

Should data analyst know Tableau? ›

These analysts must be adept at the suite of data tools available today, but especially Tableau, the dominant analytics platform for enterprises. Accessing, synthesizing and sharing data as a modern data analyst specializing in Tableau requires technical, analytical and communication acumen.

What are 3 benefits of a dashboard Tableau? ›

I. Advantages of Tableau
  • a) Data Visualization: ...
  • b) Mobile Support and Responsive Dashboard: ...
  • c) Tableau can handle large amounts of data: ...
  • d) Ease of Implementation: ...
  • e) Quickly Create Interactive Visualizations: ...
  • f) Tableau Company Strategy: ...
  • g) Use of other scripting languages in Tableau: ...
  • a) Tableau Dashboard:

Which tool is best for data visualization? ›

Some of the best data visualization tools include Google Charts, Tableau, Grafana, Chartist, FusionCharts, Datawrapper, Infogram, and ChartBlocks etc. These tools support a variety of visual styles, be simple and easy to use, and be capable of handling a large volume of data.

Can I learn Tableau in 2 days? ›

In just one or two days, or even in a matter of hours, you can get acquainted with tableau and start using it efficiently and properly. However, the best answer that can be given to the above question is: Learning tableau is a simple and straightforward affair.

Is Tableau easier than Python? ›

Tableau allows for more interactivity and is easier to make plots with than coding. The most important when deciding which one to use is regarding the workflow. Python is the best when working with a variety of data that requires advanced analytics.

Is Tableau easy or hard? ›

Tableau is one of the fastest evolving Business Intelligence (BI) and data visualization tool. It is very fast to deploy, easy to learn and very intuitive to use for a customer. Here is a learning path to all those people who are new to Tableau. This path will help you to learn Tableau in a structured approach.

Do we have coding in Tableau? ›

Use the embed code that Tableau generates as the starting point for your own code, adding or editing object parameters that control the toolbar, tabs, and more. The default embed code, which relies on a Tableau JavaScript file, is also the only way to control the load order of multiple embedded views.

Can I learn Tableau without knowing SQL? ›

SQL is not required to learn Tableau and work with Tableau; however, it is essential for securing a job because once you have an offer and begin working on a task, you will need to create the Tableau dashboard by using data.

Can Tableau handle millions of data? ›

The short answer is, Tableau can handle 50 million rows. Here's a few things to watch for. Tableau normally performs better with long data as opposed to wide data. Try to limit the number of columns when connecting to data with millions of rows.

Do a lot of companies use Tableau? ›

We have data on 126,387 companies that use Tableau. The companies using Tableau are most often found in United States and in the Information Technology and Services industry. Tableau is most often used by companies with 50-200 employees and 1M-10M dollars in revenue.

Which tool is better than Tableau? ›

Power BI offers better reporting and customization options than Tableau. Obtain Personalized Updates: View what you need at a glance with custom visualizations, similar to Tableau.

Which database is best for data science? ›

Now that you have a better knowledge of NoSQL databases, let's look at a list of NoSQL databases that are popular for data science projects.
7 Best NoSQL Databases for Data Science
  1. MongoDB. ...
  2. Cassandra. ...
  3. Elasticsearch. ...
  4. Neo4J. ...
  5. HBase. ...
  6. CouchDB. ...
  7. OrientDB.

Which database is required for data science? ›

A Relational Database Model System (RDBMS) is the primary and foremost necessary concept for an aspiring Data Scientist. In order to store structured data, you must know RDBMS in-depth. You can then access, retrieve and manipulate the data through SQL.

What Excel skills are required for data science? ›

10 Essential Excel Features For Data Analysts (and How to Use Them)
  • Pivot tables and pivot charts.
  • Conditional formatting.
  • Remove duplicates.
  • MATCH.
Dec 16, 2022

Can you use Tableau like Excel? ›

Best of all, Tableau natively connects to Excel spreadsheets to make data analysis fast and simple. Tableau allows Excel users to keep their spreadsheets while greatly enhancing their ability to analyze their data, all while delivering simple to build, simple to read visualizations that convey information clearly.

What is Tableau not good for? ›

However, Tableau still has several limitations: Tableau focuses primarily on visualization and cannot work with uncleaned data. In order to efficiently use Tableau, you need to do proper data cleaning in the underlying database first. Lacks data modeling and data dictionary capabilities for Data Analysts.


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