Differences between Python and BI Tools for Visualization

As you might have experienced in the past, or about to, Python can be used for data visualization, similar to BI tools such as Power BI, Tableau, Looker Studio, among others. Their uses are contextual: depending on the business to business needs. Some prefer the use of Python visuals while others prefer the use of Business Intelligence (BI) tools. There are two major reasons for this.

The first one is that Python visualizations are static, while those of BI tools are interactive. What this means is that once a visualization, say a line plot, has been generated using a given code in Python, it cannot be altered by the user trying to find out more specific details, such as filtering for regions. However, in Business Intelligence tools, charts created are interactive and dynamic, where one can use slicers to filter for specific information in the chart or plot. This is particularly useful for people asking ‘Why’ certain things happened.

An example is where you have a line plot of sales in a country from January to December. However, you may want to understand the sales of a Northern or Southern region alone. This way, you use slicers in a BI tool. However, this can be hard in Python since you will have to generate a completely new Python code. It is time-consuming and inconvenient to generate another code, especially where executive-level decisions are being made. That is what it means to be dynamic versus static.

Still, the business may just prefer the use of one tool to the other, irrespective of data at hand.

It is important to learn the two tools. Their uses will depend on the type of reporting the client or business needs.

Key Words: Data Analysis | Scripting Language | R | Data Cleaning | Reporting | Data Wrangling

By:

Boniface Mibei | boniface@bkdataanalytics.com