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June 24th, 2020
RPA and Data: How Robotics Is Facilitating Innovation By Automating Data Science
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Robotic Process Automation (RPA) can be programmed to do various tasks across multiple applications with one primary purpose of reducing strains on redundant tasks and reducing the time to market. As enterprises lookout for newer ways for improving operational excellence and upgrading legacy systems with new applications, RPA continues to find its application.

The global RPA market is estimated to reach US$ 7.2 billion by 2025, growing at a CAGR (Compound Annual Growth Rate) of 32.6%, driven on the back of varied applications across varied industry verticals, according to Reportlinker.com. The main growth drivers would be AI and the extensive use of software robotics.

Expanding Beyond Data Science – the Next Level

Now RPA is expanding much beyond the realms of data science. The world is producing an unprecedented amount of data. Enterprises are taking the support of RPA to handle and process such humongous data, and RPA to improve data quality/accuracy.

RPA is revolutionizing data science along with SaaS, AI/ML, self-service analytics, and predictive analytics. RPA solutions enable enterprises for end-to-end monitoring of their business processes and thus accelerates their digital transformation.

Advantages of Using RPA for Data Analysis

Enterprises use RPA solutions for IT, HR, Finance, and RPA-as-a-service solutions. They use RPA services to provide end-to-end automation services

There are numerous benefits associated with using RPA for data analysis:

  1. The probability of errors is reduced – Use of robotics for data analysis reduces errors to a large extent as compared to the manual process opted for data analysis. By using RPA, enterprises can be assured of the highest quality of data with the least error percentage. While RPA is also not perfect, this process is much more reliable than most of the manual processes.
  2. Cost-effectiveness – Employing RPA for analyzing data is much more cost-efficient than having an entire dedicated team for the purpose. Employing a workforce not only increases costs but also leads to more chances of making errors by humans while analyzing. Enterprises could use the money saved from this in improving other business processes.
  3. High efficiency and reliability – RPA facilitated data analytics offers improved efficiency as it enables enterprises to get great insights into big data. This could lead to automation taking over manual processes and mundane tasks. The RPA software is highly reliable and can operate 24*7 relentlessly with the same efficiency.

How to Integrate RPA in Data Analytics

RPA is based on various rules of business to execute processes in autonomous ways. These processes could be fitted into any solution without human intervention.

As enterprises move along the spectrum, there are technical developments, infrastructure, and expertise required for integrating RPA. 

There are several ways to integrate RPA effectively in data automation:

  • Customer service – New-age customers expect quick response time with effective solutions and automation is the key to delivering desired results. Automated systems are best able to respond to customers’ queries in the best possible manner as the questions are segregated into various categories and reach to the right customer care agent
  • Invoice automation – Many times, manual invoicing leads to delayed and wrong payments. Timely payments are crucial for any enterprise as it ensures high-quality services. RPA automatically processes invoices and removes or cuts down steps like data input and reconciliation errors that involve human interference.
  • Purchase orders – Maintaining consistent data is a very tedious task for enterprise systems. First sales representatives enter data to the CRM (Customer Relationship Management) and ERP (Enterprise Resource Planning) systems, and then financial analysts have to enter the same data into another system. This might lead to duplications, errors, and lower productivity. RPA is very well equipped with performing end-to-end sales activities by automating these tasks and helping maintain a proficient database. RPA removes duplicate data, improves customer experience, and removes all the time-taking jobs like data entry. Sales representatives can put their focus on the main tasks of prospecting and business development.
  • Payroll processing – If enterprises have to process payroll every month, then it consumes lots of time as it is a repetitive task for Human Resources and takes lots of effort for entering data. The result could be payment delays and dissatisfaction among employees. RPA helps by verifying employee data across systems, validating their timesheets, and input their earnings and deductions. Ultimately, automated paychecks are created that render benefits and reimbursements. RPA thus performs end-to-end payroll-related jobs.
  • Comparing prices – Manufacturers often purchase raw materials in bulk to produce products or solutions. Their cost is crucial for these companies as it can have a long-term impact on the company’s revenue. Therefore, they conduct a lot of research for making an informed purchase decision and usually online. The whole process could be very exhaustive and time-consuming. This has made companies use RPA for price comparison. The software compares prices from various vendors along with the product specifications and quality. This enables businesses to make the best possible purchase decisions.
  • Processing customer data – RPA can help your company to store and organize customer data to ensure that the data is easily accessible. The system automatically segregates data like contact information, purchase record, customer preferences, and even their personal information. The RPA system displays each information to customer care, sales personnel, and similar other employees. Enterprises do not need to enter the information manually and take pain about its accuracy. RPA is much more precise than the human process and has a lower error probability. Storing information requires a lot of labour and could lead to a whole lot of stress. Adopting RPA can reduce these redundant tasks by nearly 80%.
  • Recruiting workforce – RPA assisted recruitment process can help to streamline the whole process significantly. The RPA software can source candidates’ resumes from various platforms, sort them through spam or unwanted applications. This can rationalize the process by a significant margin. A recruiter could be allowed to access and evaluate, measure, and onboard every applicant more methodically.
  • Data extraction from various formats – The whole data extraction process can be automated with the help of RPA with nearly 100% accuracy.

Conclusion

RPA has lots of applications when it comes to data automation. It is a novel approach to automate tasks and is gradually innovating data science to emerge as revolutionized business process automation.

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