3 min Jan 28, 2021 Sandip Singh RPA Views : 1847

You might have already heard and read about several benefits of RPA ( Robotic Process Automation) and how it helps increasing the operational efficiency of different organizations. 

Data Extraction is a long and tedious process that requires utmost precision. When it comes to repetitive and tedious back-office work, we have RPA that automates all the boring work. However, this leads us to the following questions: 

  1. Is there a possibility of using RPA with data extraction?  

  2. If yes, then how?  

  3. What is OCR, and how can it help?  

  4. What are the benefits?  

If you are also looking for answers to all these questions, this article is totally for you.  

To begin with, let us have a clear perspective of RPA and data extraction. 

What is RPA?

RPA or Robotic Process Automation is the process of using software robots to automate repetitive business operations. This helps in reducing business costs, workforce, and thus, errors. RPA increases the speed of several business operations and allows for a better allocation of resources. 

What Is Data Extraction?

According to UiPath robot service, “the movement of data from an older legacy system to its new replacement system” is known as data extraction or data migration. The point to be noted is that once all the data is extracted from the legacy systems, they will be turned off.  

As it happens, there will be no turning back. Therefore, it should be done with utmost precision to ensure that the extracted data is 100% correct. 

Is There A Possibility Of Data Extraction With RPA?

Yes, data extraction through RPA is totally possible. As discussed earlier, RPA is already helping enterprises to increase operational efficiency by speeding up the process and reducing errors. Additionally, it also helps in saving costs and allows for better allocation of key resources. 

However, businesses can also leverage RPA for automated web data extraction. This will help them increase speed and efficiency to transform unstructured data into structured and high-quality content.  

Also, Read: The Ultimate Guide to Hyperautomation

It is to be noted that when we perform data extraction, there are four main challenges: 

  1. We have to process the source data, extract whatever we need from the main data, and map its format into the target system. 

  2. We need to introduce the data into the new system through custom interfaces. 

  3. We also need to that the migration is successful and error-free. 

  4. Last but not least, we have to find a cost-effective method to perform all the steps discussed above for multiple migrations. 

Considering all these challenges, RPA Services for data migration could prove to be a very cost-effective and user-friendly solution. Let us, now, have a look at how businesses can benefit from data extraction using RPA. 

How To Automate Data Extraction With RPA ?

Manual data extraction means that the companies employ a lot of humans and several resources for the task, which is rather boring in nature. So, there are high chances of errors, and the implications of these errors can be substantial. 
Moreover, it takes a lot of time to manually extract the data and introduce it to the new system. Due to these factors, the operational costs of manual data migration is very high. This is where process automation comes in to boost the extraction process. The best part is that it helps in saving a lot of time and resources spent on the manual extraction process.  

Here is an example to give you a better understanding: 

A company receives a lot of emails on a daily basis. While some mails are sent to raise urgent requests, there are others that contain praise for the company and services. Similarly, some emails contain job applications, while others are just spam.  

Segregating such data and forwarding it to the concerned team manually turns out to be a big headache for the employees. However, with RPA in place, the robots analyse the data and say if the email contains a job application, it is forwarded to the HR department automatically.  

Emails are just an example. All other forms of unstructured data are analysed, classified, and then transferred to the relevant departments accordingly. 

Data extraction, for RPA, becomes even more profound when OCR enters the equation. Documents scans, email threads, and the images that are part of the loan and mortgage application, etc., are unstructured data.  

Normal computers can’t process this type of data to source any relevant or useful information from these. Therefore, even the RPA couldn’t process such data, and the need for OCR becomes evident. 

OCR ( Optical Character Recognition) solutions convert such data into text so the bots can understand it. So, instead of employing manual force for such tasks, businesses can supplement their RPA with OCR. 

Benefits Of RPA In Data Extraction

Apart from all the advantages discussed throughout the blog, other benefits of web automation data extraction are: 



The software bots use the existing technology, and they don’t even need APIs to pull relevant information, which makes the process much faster and more accurate. Not to mention, the need for manual data entry reduces further. 


Another best part of RPA technology is its flexibility as the robots can smoothly handle multiple data formats. Additionally, the bots can also create and circulate log files as and when required. 

RPA can easily integrate with different technologies, making it a data specialist you can rely on. This gives businesses a huge advantage of holistic data analysis, which may further improve their competitive abilities. 

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The bots can easily track the migration process, making data migration in RPA even more beneficial. The tracking allows the bots to identify inconsistencies, faults, low-quality data, etc. and can rectify these issues while the migration is happening. In short, tracking will enable businesses to avoid auditing the whole data to find the error. 


Data is the most crucial element for any business, and if you can migrate the data faster, you will have data when you need it. Thus, the chances for growth increase automatically.