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Everything You Need to Know about Robotic Process Automation (RPA) in Data Analytics

What is RPA (Robotic Process Automation)?

RPA is a technology that uses software robots or 'bots' to automate routine and repetitive tasks that would normally be performed by humans. These bots can interact with systems and applications just as a human would, enabling the automation of a wide range of business processes.

Why is RPA important?

RPA can bring many benefits to organizations, including increased efficiency, reduced errors, cost savings, and freeing up employees to focus on more strategic, higher-value tasks.


But how does this translate into a business context and what are the implications for data analysis?


First, let's look at the RPA software market. It grew an impressive 31% to $2.4 billion in 2021, far outpacing the average global software market growth rate of 16%. This indicates widespread acceptance of RPA, with the top five vendors accounting for nearly 66% of the global RPA market.


The projection for the future of RPA is even more exciting. By 2023, more than a third of large organizations have analysts practicing the discipline of decision intelligence, including decision modeling. Furthermore, data fabric deployments are expected to quadruple data utilization efficiency by 2024, halving human-driven data management tasks.


By 2025, it is predicted that 95% of decisions that currently use data will be at least partially automated. This move towards automation will transform the way companies make decisions, making processes more efficient and data-driven.


Another important point is the influence of Chief Data Officers (CDOs) in this scenario. Those who establish value stream-based collaboration will significantly outperform their peers in driving cross-functional collaboration and value creation by 2025.


What tasks can be automated with RPA?

According to Gartner, a leading authority on IT research, companies should leverage RPA to "move data into or out of application systems without human interaction" or "automate tasks with a human in the loop."


That is: RPA is best suited for tasks that are highly repetitive, rules-based, and involve data manipulation. This includes things like data entry, transaction processing, responding to emails, and systems integration.


What are the challenges of implementing RPA?

Implementing RPA can be challenging. It may require a cultural shift within the organization, as employees may see technology as a threat to their jobs. Furthermore, implementing RPA can be complex and requires careful planning and change management.


To remain competitive in this rapidly evolving landscape, organizations will need to develop and look for analytical and interpersonal skills in the data and analytics talent market. After all, robotic process automation is here to stay, and companies that can adapt to this new reality will reap the benefits.


Conclusion

It is evident that RPA is becoming an integral part of data analytics. With its power to automate tasks and increase efficiency, RPA promises to transform the data analytics landscape by making it more efficient, data-driven and AI-driven. Organizations that understand and embrace this trend will be well positioned to thrive in tomorrow's digital economy.



By Félix Muniz, CEO - Falqon Specialist in Strategy and Management for Technology Businesses; Startup Maker and Entrepreneur.

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