The robotic process automation (RPA) industry is striving to bring about “the fully automated enterprise,” but even that goal might be shortsighted. As current trends indicate, RPA can be used for much more than just data entry-especially when combined with data science.
In RPA tools, computers are used to perform repetitive tasks that humans do. The robot label is vital here; it indicates that the software is not contained in a single system, but is connected to all (or many) of the information systems with which a human worker interacts.
As part of the initial RPA solution, RPA would mimic how people interact with systems, such as by routing support calls automatically to the technical team and sales call automatically to agents. Or you can also scrape information from a website, such as LinkedIn, and add it to your CRM system as needed
The first time RPA and data science were combined, the results were groundbreaking. Rather than allowing humans to brainstorm new opportunities to automate, enterprises implemented “intelligent” process automation. Through a technique known as process mining, you can now use machine learning to identify patterns in real-world processes and improve them automatically. A fully automated enterprise was at last within reach, as many of the RPA tools have been touting.
However, the second wave of convergence between RPA and data science is opening up new opportunities. This time, data science isn’t just helping RPA make human tasks more efficient-it’s assisting with the execution of some of these tasks.
Low-code tools simplify the development process:
Low-code tools are, at least in part, fueling this trend, which gives users intuitive access to complicated technical processes. Thus, more advanced versions of RPA and data science can be explained and endorsed more easily. The implementation of such systems is sometimes possible with both technical and non-technical support.
Visual platforms with low code are not new to either domain. Modules are arranged visually in a “flow,” usually from left to right. Both these visual representations are self-documenting as well as easily reusable.
Although subtle, there is a significant difference between the way visual platforms are applied to the two use cases. In RPA, the flow refers to the order in which an action is executed: a series of steps. Some of these actions may even involve human interaction, such as approving a particular transaction. This is how data is handled in data science, how it is combined from different storage facilities (e.g., Excel files, hybrid cloud databases), how it is transformed and aggregated, and how it might be fed to a machine-learning algorithm or another analysis method.
It is critical to the success of both RPA and data science that multiple technologies are integrated into one, and low-code can drastically reduce the friction involved. Manual implementations are possible, but they require a significant amount of effort both in terms of mastering the various coding languages required and sharing them with peers in the business sector.
Data Process Automation and Robotic Process Automation:
Data science still has a long way to go. Despite the sophistication of ETL and machine learning models, we still have a lot of issues applying these models in a real-life production environment. We call this the gap – taking our models and putting them in production, maintaining and adjusting them as needed.
Data science in production is, in essence, an RPA issue. How do we create a control flow between our models and the technology we have integrated?
The biggest challenge in data science has already been solved. The only thing we have to do is spread the word. Instead of calling it “data science deployment,” it should be called “data process automation.”
Bringing RPA and Data Science Together
The robotic process automation (RPA) industry is striving to bring about “the fully automated enterprise,” but even that goal might be shortsighted. As current trends indicate, RPA can be used for much more than just data entry-especially when combined with data science.
In RPA tools, computers are used to perform repetitive tasks that humans do. The robot label is vital here; it indicates that the software is not contained in a single system, but is connected to all (or many) of the information systems with which a human worker interacts.
As part of the initial RPA solution, RPA would mimic how people interact with systems, such as by routing support calls automatically to the technical team and sales call automatically to agents. Or you can also scrape information from a website, such as LinkedIn, and add it to your CRM system as needed
The first time RPA and data science were combined, the results were groundbreaking. Rather than allowing humans to brainstorm new opportunities to automate, enterprises implemented “intelligent” process automation. Through a technique known as process mining, you can now use machine learning to identify patterns in real-world processes and improve them automatically. A fully automated enterprise was at last within reach, as many of the RPA tools have been touting.
However, the second wave of convergence between RPA and data science is opening up new opportunities. This time, data science isn’t just helping RPA make human tasks more efficient-it’s assisting with the execution of some of these tasks.
Low-code tools simplify the development process:
Low-code tools are, at least in part, fueling this trend, which gives users intuitive access to complicated technical processes. Thus, more advanced versions of RPA and data science can be explained and endorsed more easily. The implementation of such systems is sometimes possible with both technical and non-technical support.
Visual platforms with low code are not new to either domain. Modules are arranged visually in a “flow,” usually from left to right. Both these visual representations are self-documenting as well as easily reusable.
Although subtle, there is a significant difference between the way visual platforms are applied to the two use cases. In RPA, the flow refers to the order in which an action is executed: a series of steps. Some of these actions may even involve human interaction, such as approving a particular transaction.
This is how data is handled in data science, how it is combined from different storage facilities (e.g., Excel files, hybrid cloud databases), how it is transformed and aggregated, and how it might be fed to a machine-learning algorithm or another analysis method.
It is critical to the success of both RPA and data science that multiple technologies are integrated into one, and low-code can drastically reduce the friction involved. Manual implementations are possible, but they require a significant amount of effort both in terms of mastering the various coding languages required and sharing them with peers in the business sector.
Data Process Automation and Robotic Process Automation:
Data science still has a long way to go. Despite the sophistication of ETL and machine learning models, we still have a lot of issues applying these models in a real-life production environment. We call this the gap – taking our models and putting them in production, maintaining and adjusting them as needed.
Data science in production is, in essence, an RPA issue. How do we create a control flow between our models and the technology we have integrated?
The biggest challenge in data science has already been solved. The only thing we have to do is spread the word. Instead of calling it “data science deployment,” it should be called “data process automation.”
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