Robotic Process Automation RPA

How to Integrate RPA in Big Data Project

How to Integrate RPA in Big Data Project

Presently, it’s so evident that robotic process automation has successfully obtained the most significant level as one of the leading process simplifying technologies in today’s market. However, another technological improvement in data processing and storage is making waves across several industries and delivers fascinating possibilities for companies when constructed against RPA. 

Discussing the arrival and proliferation of Big Data and its influence on how companies and even sectors collect, analyze, and share reporting and improved metrics to refine business processes and achieve defined standards or goals.

One of the most comfortable places to use RPA is very simple, highly repetitive business processes that depend on transactional data that comes in fixed record lengths, with data fields occasionally in the same locations.

This data is exceptionally predictable, and automation tools such as RPA that rely on recognizing repetitive data patterns are in powerful positions to succeed.

At its essence, RPA provides solutions for problems related to data-centric manual tasks. It remains perfect as a tool that effectively interacts with websites, business and desktop applications, databases, and people to perform repetitive work. 

Without a doubt, RPA is the “handwork” of processing electronic data. It strengthens human employees’ ability to move from being data gatherers to data users; they can pay more attention to more cognitive and strategic business initiatives that better serve customers and improve their job satisfaction.

Importance of Big Data in RPA

Based on the following statistics, it is estimated that:

  • By 2021, the RPA market will attain $2.9 billion.
  • Automation can reduce operating costs by up to 90%
  • RPA has become a widespread technology in 2019 and is expected to increase rapidly over the next five years
  • Banking, Insurance, Healthcare, and Retail are the top industries using the power of RPA.

Steps to Implement RPA 

To successfully implement RPA, there is a three-step tooling architecture that IT should initially ponder about, and they include: ETL, RPA, and AI.

ETL: At the front end of an RPA process that utilizes big data, it is suggested that you use an extract, transform, and load (ETL) tool. It can integrate with and collecting the incoming streams of raw and unstructured data that you gather from each of your suppliers. 

This tool is created to extract the data significant to your business process, change it into a capable format that your systems can implement, and then load the data into your operations and an RPA process.

RPA: At this juncture, the RPA process can take control because you possess clean, high-quality data coming into the RPA software. This enables the RPA software’s job of automating a business for something such as invoices straightforward.

AI: Just as the RPA software processes invoices, it invocates the business rules that experienced employees have coded into its artificial intelligence (AI) engine. For example, if the business rules planted in an RPA, see an invoice from Pearson Manufacturing with a “net ten days” note on it.

And the standard net terms for Pearson are net 30, and the RPA process may likely identify this invoice as an exception that needs a person to review and approve it.

Tips to Remember While Integrating RPA in Big Data Project 

RPA Software can’t do RPA alone

The RPA automates business processes, but ETL automates data cleaning and transfers; you need both of them to automate a business process that relies on high-quality data completely. The third piece of the puzzle is an AI engine that is added with the RPA, and that comprises the business rule-set you wish the RPA software to apply to the items and operations it processes.

Tool Integration is Paramount

In the big data environment, RPA works effectively when implemented with an ETL tool that can present it clean data. Inside the RPA software, there ought to be a table of business rules that moves the RPA software’s business process decision-making. 

Conclusion 

Well, for businesses that depend on repetitive, time-consuming tasks, Robotic Process Automation is an effective and cost-efficient method to standardize and simplify processes. RPA, when merged with cognitive technologies like Machine Learning and AI, can control tasks that require decision-making and perceptual abilities.

While the process of analyzing data and implementing these new insights to present business activities is still relying on human direction, the relationship formed by combining RPA and Big Data is hugely beneficial. 

Presently, RPA is one of the best tools that can be utilized to extract insights from Big Data to decrease process delay and enhance optimization of business results substantially.

With all the propaganda surrounding RPA increasing daily, businesses are already learning how RPA works to transform a genuine digital enterprise. 

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