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How is RPA used in Big Data?

  • Mallika Rangaiah
  • Aug 13, 2021
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There’s no limit to the numerous steps and strategies that contribute towards the growth of a business. While the prerequisites for any business like a capable workforce, marketing, and exceptional customer service are of course integral for the success of any business, what is even more crucial in the present scenario would be data. 

 

Data aids firms in lucrative decision making, in detecting trends, and paving the way for enhanced customer service. Every day, a massive section of data is assembled by businesses. Being a paramount asset for companies in today’s date, data is being leveraged by platforms in multiple ways to collect, cultivate and benefit from the same. 

 

Robotic Process Automation (RPA) in particular, is a technological trend that is already being applied to data by platforms across the globe. As per a survey by The Economist “Advance of Automation”, most organizations believe that automation kickstarts digital transformation.

 

Let’s take a look at how. 

 

 

What is RPA?

 

Robotic process automation (RPA) is a software technology that facilitates easy development, deployment, and management of software robots that simulate human actions for capturing and interpreting applications to process a transaction, manipulate data, trigger responses and also interact with other digital systems and software. 

 

Just like humans, software robots are able to comprehend the items on a screen, perform accurate keystrokes, navigate systems, detect and derive data, and execute a wide set of actions. The same tasks performed by humans are able to be implemented in a swift and more consistent manner,  with there being no risk of overexertion or hindrance caused by breaks.

 

You can take a look at the video below to get a better understanding of this technology : 


 


RPA in Big Data

 

From automating repetitive procedures to examining massive data volumes to detect suitable information for the human controllers, RPA is adopted by companies for a variety of purposes.

 

Imagine a scenario without the adoption of RPA. The user would have to key documents such as invoices into a single system and then the same information would be rekeyed into another system since there wouldn’t be a simple way for automating integration among the systems. Being a highly strenuous work for the users, it considerably decelerates business processes and becomes quite an inconvenience. 

 

Meanwhile, when RPA takes over, the user is only required to key in the new invoice data once. Following this, the automation software will take over by scraping the data the user has entered off the screens and then shifting this data into other systems that are in need of it. 

 

In this blog, we will be taking a look at the benefits offered by RPA in Big Data and how RPA is used in the field of data analytics

 

 

Benefits of RPA in Big Data

 


The image shows 3 benefits of RPA in Big data namely reduction of errors, decreasing costs and enhancing efficiency

Benefits of RPA in Big Data



There’s definitely plenty of perks RPA offers that make it an asset for companies carrying out their data analytics. We’ve listed three of them below : 

 

 

Reduction of Errors

 

In the case of manual analysis of data, there is a high possibility of frequent errors which often renders the data unusable for the company.

 

Robots and automated software on the other hand don’t have the disadvantage of getting sidetracked or confused, which ensures high-quality data with minimal errors through the power of automation. 

 

Alongside reducing errors, RPA also helps in enhancing customer satisfaction, with lesser errors allowing for there to be minimal things to sort out. Although there is always the possibility of malfunction since the technology is not exactly impeccable as of yet, it offers a more dependable and precise result in comparison to most humans. 


 

Decreasing Costs

 

It's definitely not a piece of cake to staff full departments for analyzing data and an extensive amount of cost can be involved, which might still result in inaccuracy or lack of efficiency in comparison to the use of RPA. 

 

Being affordable and simple to execute, RPA is actually pretty easy to carry out. Its use also allows for overtime minimization of data analysis staff, paving the way for more saved expenses. The staff can then be removed or equipped for operating on more integral and complex tasks

 

( Recommended blog - RPA in Manufacturing )

 

The saved money can then also be used for enhancing other parts of the company.

 

 

Enhancing Efficiency

 

With the automation of the process of data analysis, the users are able to get the insights they require at a faster rate. With automation having a massive capacity and operating faster than humans, this results in enhanced efficiency. 

 

This allows staff members to invest their time and energy into more crucial matters. With automation taking charge of the routine tasks, the staff will inadvertently be more engaged by being involved in more intricate operations.  


 

How is RPA used in Big Data?

 

Let’s take a look at how RPA is adopted in Big Data. 

 

Data Entry, Integration, and Migration

 

Owing to the lack of integration of systems, the business users generally have to enter the data derived from documents such as invoices manually into a single system and then rekey that data into another system. 

 

Analytics programs have compelled users to manually swift through data for locating address fields, ZIP codes, or replicated through other submissions. The manual data cleansing and deduplication of records procedure can prove to be highly taxing and prone to errors.  

 

Business operations are considerably delayed owing to the exhausting administrative labor involved in the data cleansing tasks, which inevitably leads to hindrances and delays in business operations. 

 

In case the analysis does not conclude, the operations of the enterprise will be hindered and the accuracy of the outcome of this analysis will be at risk owing to a lack of adequacy in the consistency of data. 

 

RPA can be made use of, for generating and maintaining well-managed and suitably classified data across enterprise systems and generating data lakes to develop advanced Machine Learning models for data scientists. One or more software applications can be handled by RPA software robots. For instance, if we take into account data cleansing and analytics, RPA can collaborate with Big Data analytics toolkits and aid businesses by a couple of approaches mentioned below :

 

  • Instead of manually keying or sending files, the Data entry can be completely automated.

 

  • RPA allows for automated data monitoring, by constantly inspecting to detect any data anomalies and enhance data consistency in the absence of any human engagement.

 

  • The technology allows for automated deduplication of data and revival of fresh data streams like IoT computer logs as well as other system-generated data. 

 

Alongside automating the procedure used for transactional data entry and preserving time for the end-users, RPA also aims to aid in everyday IT operations, like the initial data cleansing prior to it being used in analytics.

 

 

Using RPA for Data Analytics

 

RPA is known to serve as an effective data aggregation tool for supplying data sources to be used in advanced processing algorithms. The technology allows better digitization of business operations.

 

It is enhancing data analytics and making use of machine learning which can be incorporated for further digitization of business operations. A comprehension of the organization’s processes and its workflows, the enhancements in its model process and an idea of the accurate process enhancement opportunities can be gained by making use of advanced data analytics software for analyzing the data.

 

Alongside automating business processes, RPA also digitizes them. This allows more data to be gained following the automation of the process rather than when it was carried out manually. The approach minimizes subjectivity via manual process assessment, enabling organizations to undertake transformational decisions for meeting business goals and augmenting their competitive edge.

 

The data generated through RPA can later be imposed upon varying types of data analysis for optimizing it further. We’ve listed some use cases obtained through RPA generated data:

 

  • Machine Learning: Machine Learning models allow us to decipher the elements that can considerably impact a process and to what extent. If we feed RPA process audit trials into varying machine learning algorithms, it will allow us to obtain the prescriptive strategies for optimizing the processes. 

 

  • Process Mining: The Process Mining technologies can be adopted for visualizing the entire process of making use of the data offered by RPA, paving the way for a more comprehensive insight into the process, and also, Process Mining applications can produce the data to pinpoint the best suitable processes when it comes to RPA development.

 

  • Process Simulation: It might prove to be difficult to point out the effects of even slight enhancements in complex and redundant business procedures. Operating a simulation program is easy with the utilization of process data for defining process requirements and simulating the realistic influence of different scenarios in automated systems. 

 

 

Algorithm Selection and AutoML

 

It is pretty integral to decide which predictive model and its basic algorithm operate most effectively in a particular use scenario. Multiple models are often generated by data scientists for the same purpose and these models may sometimes even be combined. The modern-day solutions offered by RPA can automate this data science component by making use of AutoML in which a number of varying algorithms are applied among which the one that predicts the most suitable human results is determined as an effective model.

 

The option becomes a possibility in scenarios like that of loan approval where the data is derived through a myriad of sources for forming a lone decision (especially in case of binary decisions like the approval of loans or their denial). In such cases, the varying algorithms and models of AutoML are required to determine the most lucrative one, that is capable of automating an array of data science levels. The algorithms can shift and vary on the basis of the data observed by the bot.

 

 

Bot Learning

 

Production data becomes an excellent source to train machine learning models, especially in the case of big organizations. These machine learning models are mainly trained through the decision of a business user for approving or denying a particular application, yet the stage is also impacted by other learning types which discursively impact a model training process. The following abilities are adopted by bots for making a loan decision through suitable sources :

 

  • Computer Vision: The technology allows bots to observe a screen in a similar manner to a human. Bots can detect varying data sources and websites from where they can gain data backing business use cases.

 

  • Natural Language Processing: The NLP technologies operate in collaboration with deep learning ones so that the bots can detect each of the screen app controls, just as a human would and operate them. This gives bots the capacity to comprehend applications written in varying application frameworks.

 

  • Connectors: These mechanisms enable bots to carry out a variety of steps like data retrieval, which are vital for their processes to operate through operating systems.

 

Although the procedure of interpreting the data and putting the fresh insights into action for the prevailing business activities, still highly relies upon human direction, the combination of Big Data with RPA has proven to be highly advantageous. RPA is presently one of the leading tools for extracting insights through Big Data to considerably minimize process bottlenecks and enhance the optimization of business results.

 

It will be interesting to observe how this technology influences the field ahead in the future.

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