Data scientists and RPA developers should collaborate to make a perfect team.

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If  RPA developers work with data scientists, it will facilitate more creative solutions to complex business problems; than working separately. Robotic process automation (RPA) is a cost-effective way of automating basic tasks as humans do, with the help of various hardware and software systems that can perform on different applications. RPA also focuses on the manual processing of data to gather more information for the company. Applying data analysis to this RPA-generated data can help the businesses gain a deeper understanding of the improvement opportunities, different business structures, and models, and help meet customer demands faster. RPA and data science have always shared a mutually beneficial relationship. RPA tools integrated on the insights drawn from the data analysis, and the predictive models of data science were programmed to enhance the capability of these tools. The further advancement of robotic process automation into the realm of data science will prove a remarkable transformation for business enterprises since they will gather more data in a cost-effective and non-invasive manner. The skills RPA developers and data scientists possess are different but they complement each other. To understand why they should collaborate, let us look at the roles and responsibilities of data scientists and RPA developers.  


Role of an RPA developer


The primary responsibility of an RPA developer is designing, innovating, and implementing new RPA systems. Other responsibilities include:

  • Enabling high-quality automation using quality assurance (QA) processes and preventing potential complexities.
  • Design business processes for automation.
  • Develop process documentation to refine business processes by highlighting mistakes and successes simultaneously.
  • Provide instructions and guidance for process designing.

Role of a Data Scientist


A data scientist analyzes and handles vast amounts of information to find patterns, customer behavior, trends, and potential risks in the market. Other responsibilities are:

  • To implement data science techniques like machine learning, artificial intelligence, and statistical models to gain data for the company.
  • Understand and select correct potential models and algorithms for different business tasks.
  • Cooperate with engineering and product development teams to produce solutions and strategies for complex business problems.
  • Develop predictive models and machine learning algorithms.

How the two teams complement each other?


The skill-set that a data scientist possesses differs from an RPA developer. They have different temperaments since their workflow and timelines are very different. When the workflow divulges, so do the mindsets and it affects the communication between the two teams. But RPA developers can generate more complex processes working with the data science team than working alone. Business organization leaders should understand the potential outcomes and encourage RPA developers to communicate with data scientists. A forward-thinking business organization will not compromise between two valuable teams, instead align them. RPA’s automation of data science allows the generation of models and selecting the most suitable model for unique business tasks. On the other side, these features enable data scientists to invest more time in other important tasks and develop creative models to provide analytical solutions for critical business problems. Bottom line, combining these two teams will not only enhance productivity but also amplify business growth.