In many cases, data integration/preparation is the most painful and time-consuming step in a data analysis process. The most commonly used tools, such as Excel, SQL, and Python/R, all have their limitations.

Excel is the most widely used tool for data analysis. The nature of the spreadsheet is in everyone’s comfort zone—it is easy to read and understand.

An Excel spreadsheet screenshot:

However, Excel is limiting for many reasons. For example, it lacks transparency, repeatability, and scalability. Excel also has limited functionality, especially when it comes to data analysis.

SQL combined with Python/R can achieve almost anything from a data analytics perspective.

A Jupyter notebook screenshot:

But it takes a lot of time and persistence to become proficient in coding.

That is where Alteryx comes to save the day! It allows you to create a data analysis workflow with a simple drag-and-drop solution.

An Alteryx workflow screenshot:

I have been using Alteryx for only two weeks, but I already feel the power of it. Unlike Excel, Alteryx does a way better job in repetitive and collaborative ETL. Besides, the workflow is auditable, which provides transparency. On the other hand, compare to SQL and Python/R, Alteryx is much easier—you only need a few days to get the hang of it. The scalability of Alteryx might be limited, but it is constantly evolving. For example, in 2020, the Alteryx Multi-threaded Processing (AMP) was released, made Alteryx possible to work with larger volumes of data at higher velocity. In 2021, Alteryx launched tools for Automated Machine Learning (or AutoML), Data Health, and Feature Engineering. Those are groundbreaking updates for data science purposes.

Overall, Alteryx is a good choice for data analysis needs. I look forward to bringing Alteryx to real projects soon.