The thing that is loved about Data Science is the options of multiple applications and libraries within programming languages like Python and R to accomplish the same task in a project. So, you can never be sure about usage of a particular medium, and it makes the Data enthusiasts for craving for more easier to use applications for developing projects, scraping data, and analyzing patterns effectively and more efficiently like ever before.
Most of the Data Analysts did not know much about how Tableau is so effective and how it has changed the visual side of data analysis, until they create projects themselves on it. It has opened a world of possibilities for them, and many have started using Tableau much more than python libraries like matplotlib for their generation of data related visualizations.
Many of the Data Scientists happily share the main points for which Tableau is an amazing tool that will make the life of every analyst easier and way more efficient.
Neuroscience teaches us that our human brains can focus on a limited amount of information at a time. So, it is a no surprise when we are favoring our brain by limiting data within a Dashboard to enable us to concentrate on the data much more confidently and clearly via a limited number of Visualizations.
Tableau has been designed while keeping that in mind, and it has various interactivity options that enables us to choose the KPIs to display, type of visualization, date granularity, etc. with a simple click. Beyond that, Tableau has some nifty features under the tooltip which lets the user to have extra information when they hover of click a visualization, or even the smallest part within any visualization. There are some great tools to display way more information that you could ever do with a static graph, while keeping the workspace simple and clear.
This is small GIF that I have created, highlighting how a user can interact with a visualization to find detailed information within a Dashboard without writing even a single line of code.
Data Connections and Relative Speed
When you compare Tableau with similar tools like Power BI, Tableau has a huge advantage of connecting to everything. The list of available connection is exceptionally long, even longer than one could think of. This major feature enables the organization to not only extract, wrangle and clean, but also to visualize data from many diverse sources.
Tableau is well-known to do this at an incredible speed, optimizing the time necessary to analyze massive amounts of data. Joining various sources is quick and easy, but if you wish to e.g., prepare your data in Alteryx and send it to Tableau, this is also possible, quick, and easy.
Besides this, Tableau developers can customize the colors, labels, and shapes much easier within the tool, resulting in a significant amount of time being saved. If we look back at all the time, I spent doing EDA (Exploratory Data Analysis) in R, saving plots one by one, fighting for the right label… sigh, many developers will wish they got to know Tableau before.
Forget about compiling, installing Tableau on every computer within the organization for enabling yourself to send a file to be viewable by the respective stakeholders. Tableau (within the server environment) lets developers to share just a single web-link on any machine with an internet connection to view any kind of visualization created in Tableau.
Tableau Server allows you to schedule automatic data refreshes, so you can wake up every morning with updated reports with no effort nor click.
Last few versions of Tableau released have emphasized on security implementation much more effectively and it allows you to control who can open/create/modify dashboards, projects and even restrict a portion of rows within data to specific users.
This can be achieved with three options:
- Licenses & Site Roles
- Permissions → both for individual users and groups (e.g., Sales team, HR,)
- Row-Level Security → same dashboard, different people see various parts of the data based on privacy rules.
In short, you do not need to worry about who can control, view, or even edit the data before sharing to the relevant stakeholders if you know the security policies and sharing options in Tableau.
There are far more benefits, features and revolutionary advancements being made as versions of Tableau gets released every month, that I can describe here.
All these benefits of article cover a part of the data analysis process, so the point I would like to emphasize is not to switch to Tableau forgetting about all the other tools, but to understand at which step of the process switching, for example, from Python to Tableau, will save you tons of time and make your results more understandable, clear, and. pretty!