Top 3 Reasons Why Tableau Is Better Then MS Excel for Creating Dashboards
As the analytics industry has grown over the years, it has given opportunities to companies to create tools which specifically cater to specific aspects of the data analytics procedures like data transformation, data formatting, visualisation etc. As a result some of the older tools have obviously faced the brunt of this gap created with the evolution of the industry. MS Excel used to be a standard tool used by most organisations all over the world to create dashboards. In the current state of the industry Tableau has turned out to be the preferred choice for dashboards and data visualisation tasks to replace the MS Excel’s capabilities. Let us explore some reasons for this shift in choice.
1. Computed Fields – A very basic feature that the tool provides us is that it allows the end-users to create custom fields on the fly in order to facilitate the required values in a new column apart from the existing data in the source files. Needless to say it adds a level flexibility while creating dashboards in Tableau. In a way, it covers the need to create new fields from existing fields in required formats.
2. Dynamic Dashboards – Tableau allows the end-users to create dynamic dashboards. This means that the final dashboards respond to certain filters that can be applied to the final dashboard. This allows the end-client to look at their data at many levels from the one dashboard. Since dashboards and visualizations refresh in realtime, it definitely makes it a worthy choice.
3. Connects to Multiple data sources – Tableau is a very robust tool when it comes to connecting to data sources. It is able to connect to a variety of data sources like SQL databases, Excel files, delimited text files etc. Not only that, it can access these sources simultaneously and also create joined tables from the tables as necessary for the end-user. This is a capability that is a huge step ahead of anything MS Excel provides end-user.
However, as powerful as Tableau can be, it is only as capable as the data that it is accessing. Therefore, in order to get the best results for data visualisation, the should preferably be prepared appropriately in a data transformation tool like SAS or ACL Audit Command Language. The data analytics industry has evolved to this point where it is clear that all these phases are executed sequentially by dedicated teams all over the world. So that means the expectations are also higher for each of these aspects to work flawlessly. This trend doesn’t seem like it is going to die down, so all young data analysts should starting building there skills accordingly to future proof their careers.