Robotic process automation (or RPA) is transforming the way many businesses handle their repetitious, labour intensive tasks such as reporting, making basic decisions, and providing services. Using software these tasks can be automated; reducing the time to complete tasks while also improving their accuracy and consistency. If you want to get started down the RPA path without incurring licensing costs, there are free tools you can start using today.

RPA, like most trends, is really the repackaging of an older concept. Prior to RPA there was scripting, which was the term for automating tasks that would have otherwise been done by a person. There are many free scripting languages that will allow you to accomplish the same results as RPA software, generally with slightly longer development times to implement solutions.

Now, there are always some tradeoffs when using free software. The primary ones deal with the fact that organizations tend to be trading financial expenditure on licensing for investments (of either time or money) in training for their staff. This isn’t a bad trade to make since the knowledge that is being gained is of great value both to staff and organizations, but it doesn’t come without some manner of opportunity cost.

The tools

All these tools share a few common attributes. They are open source, meaning that they are free to use and supported by a large community of volunteers. They are also extensible, meaning that the users of these tools share their work and their improvements, which you can incorporate and use in your work via packages or libraries.  Here are the most common and widely known:


Python is a programming language which is seen a large surge in popularity in recent years. While it started as a general purpose scripting language, it has moved into heavy usage in the web development and data science communities. Python is primarily intended for software development so it follows conventions from the programming world and the core audience is coders.


Perl is a programming language with a similar origin story and evolution as Python. Like Python, Perl has a large collection of user-contributed libraries and which extend it’s capabilities to cover machine learning and analysis tasks. While Perl is less popular than Python, it’s supporters are every bit as enthusiastic.


R is the inverse of Python in many ways. It started as a statistical analysis package and has been extended to cover scripting tasks over time. It was created by staticians, and uses their best practices and terminology, so it is most commonly used within academia and for research and development. R is the best choice for data intensive applications or when you need the latest, cutting edge analysis techniques.

Which tool to use is dependent on several factors. Primarily, you’ll want to consider the task at hand and which tool is best suited for the work.  Secondarily, you’ll also want to consider your knowledge and comfort with the tools. Personally, I tend to lean towards R since I tend to be developing data intensive reports and conducting exploratory analysis which R is uniquely suited for. That having been said, the same work could also be accomplished using either Python or Perl, if you add some packages or libraries.

One example of a task that you can use these tools to automate is corporate reporting. A lot of corporate reporting is consistent and repetitive in nature, making it an ideal candidate for automation. I’ve used R to develop reports which automatically refresh on a weekly basis without user intervention, saving time for tasks which add more value to the organization.

Want to learn more about process automation, or want to discuss which tools are right for you? Contact me and I will be happy to help you work through the considerations and implementation.

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