RPA takes us on board the express train to big data analytics

18 Nov 2021
RPA takes us on board the express train to big data analyticsRPA is a must-have ticket to get on board the express train of big data analytics. Adopting it may seem complicated, but businesses can start from small by applying RPA to simple tasks to boost productivity.

Robotic Process Automation, or RPA, is a hot topic today as companies undergo the process of digital transformation. According to findings of a Gartner market research, global sales of RPA may reach US$1.89 billion in 2021, a 19.5% increase from 2020. This indicates many enterprises are seeking to use RPA to boost their competitiveness within a short period of time. Those that take a wait-and-see approach will be the laggards. Adopting RPA may seem complicated, but businesses can start from small by applying RPA to simple tasks to boost productivity. RPA is a must-have ticket to get on board the express train of big data analytics.

  1. What is RPA?
  2. Applicable to different fields
  3. Integration of data for accurate BI analyses
  4. Advantages of combining RPA with BI
  5. Want to use RPA wisely? Just ask the expert
 

 

What is RPA?

RPA is a software technology that automates repetitive tasks on behalf of human. It can operate in the IT domain of a workplace without using specific hardware. The difference between RPA and traditional desktop automation is that the latter can only handle simple, individual tasks, such as the record and playback tool of Excel Macro, and automatically generate solutions in a document based on specific instructions, whereas RPA can automatically search different items in a table and automatically complete the processes of categorisation and filing based on pre-set rules. It is also capable of disseminating different kinds of information to related individuals and completing a file management procedure without human intervention. Take automobile assembly as an example, traditional desktop automation involves the automation of individual assembling tasks, whereas RPA involves overseeing and completing the whole assembly procedure. In terms of the scope of usage, there is a big discrepancy between traditional desktop automation and RPA.

 

 

Applicable to different fields

RPA is now considered an indispensable digital workforce. Some years ago we already predicted that this tool would bring huge benefits to commercial enterprises, such as replacing humans to carry out repetitive and mundane tasks, eliminating the possibility of manual input of incorrect data, ensuring around-the-clock operations, and preventing company operations from being interrupted by personnel changes. As the RPA technology is becoming increasingly sophisticated, it is being used by a wide variety of industries, including logistics, accounting and insurance. It is a practical technology that can help different sectors move forward at a steady pace on their digital transformation journey, and also enable companies to focus on growing their businesses and optimise customer experiences.

 

 

 

Integration of data for accurate BI analyses

Big data analytics is a must-have for companies undergoing digital transformation. With business intelligence (BI), companies can better grasp market trends, improve workflows, or formulate apt marketing strategies. As for RPA, it can provide accurate and high-quality data to support big data analytics. Nevertheless, while RPA can collect hundreds of times more data than humans and is able to provide accurate and high-quality data for big data analytics, if unsupported by artificial intelligence (AI) and machine learning, RPA can only handle structured data and is unable to deal with unstructured data from different segments within a company. For instance, online shopping platforms in general can only use RPA to collect structured data such as daily sales, product clicks, and chosen payment methods. However, unstructured data, such as customer comments, browsing activities and the most clicked pages, are also important references for the improvement of website design and business turnover.

 

RPA Machine LearningRPA equipped with AI and machine learning functions can mimic the cognitive functions of humans, instantly combining complex unstructured data with structured data.
 
On the other hand, RPA equipped with AI and machine learning functions can mimic the cognitive functions of humans, instantly combining complex unstructured data with structured data. With BI, operators of online shopping platforms need not spend time to create post-sale evaluation benchmarks (such as the benchmark for measuring the impact of price reductions on sales) when they launch new promotional offers. Instead, they are able to grasp the impact of a promotion initiative on the sales volume, click-through rates of other products, website traffic and logistic load in a more macroscopic and timely manner. With data visualisation, company management can promptly adjust a sales strategy and optimise other supporting measures. By upgrading RPA to the more advanced level of process automation or workflow automation, companies can eventually achieve the objective of digital transformation, which is to improve customer experiences.
 

 

Advantages of combining RPA with BI

Traditionally, the writing of market analysis reports must be preceded by setting the research objectives and the data collection methods. The process inevitably involves the subjective views of the researchers. Besides, it is not easy to apply individual reports to other fields. But when integrated with BI, RPA can enable big data analytics to play a more effective role in three areas.

Process mining Using data captured by RPA, BI can identify bottlenecks in the various workflows of a company, e.g. checking whether the approval procedure related to report submission contains any duplicated or redundant steps.
Process simulation             Using data captured by RPA, BI can make comparisons between the datasets of a related industry and stimulate the impact of specific procedural changes. This way, the company can predict the efficiency of certain changes and to what extent they can help mitigate risks.
Machine learning
To a certain extent, both process mining and process simulation require a company to provide specific issues for the BI-integrated RPA system to tackle. When the system is combined with different machine learning algorithms, it will be able to do calculations and proffer suggestions automatically. For example, it can work out which months price reductions can generate the best response, and which logistics companies do best in terms of punctual delivery of goods. This way, companies can eventually enhance customer experiences.
 
RPA combined with BIRPA combined with BI can enable big data analytics to play a more effective role in three areas: process mining, process simulation, and machine learning. 
 

 

Want to use RPA wisely? Just ask the expert 

Despite the aforementioned advantages, it should be pointed out that RPA is not applicable to all procedures. There are indeed some tasks that do not require the use of RPA. In other cases, RPA is not that useful for promoting digital transformation. Pushing the use of RPA could therefore delay the return on investment (ROI) that a company can get. Therefore, before you decide whether to adopt RPA technology, it is best to consult an expert for a detailed analysis based on the salient features of your business, needs, workflows and future expansion plans. If you want to make sure you use RPA wisely, simply contact Ricoh’s experts.

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