Before joining Biztory, I was a fresh graduate in statistics/data science with 5 years of coding on my young shoulders.
R, Python, and in-depth data analysis were my daily cup of tea, and I was delivering my work (and my Ggplot/Matplotlib visualizations) with great enjoyment.
I have never thought of learning Tableau, as I gave for granted that a drag-and-drop tool would not boost the work you can deliver through ‘hardcore’ programming languages, where you can code basically whatever you want.
Little did I know about the other side of the visual BI deal, little did I know about the next level possibilities that this BI tool will open to my work.
After a bit more than a year having my hands on Tableau every day, I can look back and 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 the human brain is able to focus on a limited amount of information per time, so it’s no surprise that, when looking for an answer from our data, we make our braincells a favor by limiting the information we display and the number of visualizations to look at.
And here Tableau comes as a magic hand, saving you a bunch of work: not only you can switch the KPIs to display, type of visualization, date granularity, etc., all with a simple click. Beyond that, the chance to add other data and other visualizations in the tooltip allows you to have extra information popping up when hovering on a graph, with Tableau filtering the view on the current selection in an automatic way. These are all great tools to display way more information that you could ever do with a static graph, while keeping the space simple and clear.
This is a short and simple snapshot of how interactive a single dashboard can be, and if you start adding Tableau actions to connect different dashboards, the possibilities are endless.
Data Connections & relative speed
Tableau can connect basically to everything.
The list of available database connections is veeery long, allowing your organization to analyze data stored all over the places (e.g., Microsoft SQL Server, Google Analytics, Salesforce, ...) in the same dashboard, without having to set up API calls. Tableau is well-known to do all this at an incredible speed, optimizing the time necessary to analyze huge amounts of data. Joining different 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.
Beside this, creating data visualizations is very quick and intuitive in Tableau, and so it is to customize them (colors, labels, axis, ..). If I 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, I wish I got to know Tableau before.
a.k.a. share your work with a link
Forget about saving all kinds of different files to send to your colleagues, with a Tableau Server environment you can share your results with a single URL. You can have an URL for Projects, Workbooks, Dashboards, individual visualizations, and even for visualizations already filtered on specific fields as you wish the user to display them.
Tableau Server allows you to schedule automatic data refreshes, so you can wake up every morning with updated reports with no effort nor click.
Security implementation in Tableau is an easy win that allows you to control who can open/create/modify dashboards, projects, and even restrict the views to specific rows in your data for different users.
This can be achieved with:
Licenses & Site Roles
Permissions → both for individual users or groups (e.g. Sales team, HR, ..)
Row-Level Security → same dashboard, different people see different parts of the data based on privacy rules
In short, you don’t have to worry about hiding specific data to specific people, Tableau makes it easy to control who can do what.
I’d like to write a whole chapter about many other functionalities, for example about how easy it is to create custom Maps in Tableau thanks to the automatically generated Lat/Long fields and so on, but this post is already long enough, so yes.. I’ll stop here 😀
All these benefits of course cover a part of the data analysis process, so my point 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!
“Ask a silly question and you'll get a silly answer” still holds true, and this is to me one of the top priorities every analyst should have. Tableau is amazing but it will not transform rubbish in roses, always good to keep in mind.
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