robotic process automation in data extraction

Do you think that there is a role for robotic process automation in data extraction and migration? The answer is obviously ‘yes’.

Data Migration is the movement of data from an older legacy system to its new replacement system. The nucleus of the matter is that legacy systems will be turned off upon the data extraction and migration process. As a matter of fact, migration should be handled properly, because once it’s put out of action, there’s no turning back.

Is there a possibility of Data Extraction with RPA? 

Yes, ‘Data extraction through RPA’ is totally possible. For instance, RPA is already lending a helping hand to enterprises to surge operational efficiency.  Furthermore, it also renders assistance in cost saving and also apportions for better allocation of key resources. 

However, businesses can also cash in on RPA for automated web data extraction. This will facilitate them to increase speed. Apart from that, it can also transform unstructured data into structured and high-quality content subsequently.

Data Extraction & Their Challenges

  • You need to process the source data, and extract whatever is relevant from the main data. Later on, should map its format into the target system. 

  • You need to introduce the data into the new system via custom interfaces. 

  • You should also ensure that the migration is successful and also error-free. 

  • You should find a cost-effective method to perform all the steps that are discussed above for multiple migrations.

Taking into account all these challenges, RPA Services for data migration could prove to be very cost-effective. Let us, now, take a look at how businesses can gain from data extraction using RPA. 

This is the reason why it is recommended to lay hands on RPA for data extraction and migration: 

  • Its error-free

  • User-friendly

  • Its stability of output can mitigate the legacy decommission risks

In addition, digital technology such as RPA shares applicable properties with data migration. They are purely rule-based and highly systematic. 

All the steps demand a high level of detail and planning. The procedures, rules, and expected results for extraction, transformation, and mapping onto target structures (i.e. loading) ought to be spelled out clearly.

This rules-based approach is very identical to the requirements for a functional fulfillment of robotic process automation. In addition, the integrative capacity of RPA technology was the kindling spirit. So we described data management as one of its top use cases.

How RPA helps

We exist in the digital era and data operations are at the locus of doing business. As a matter of fact, whenever you wish to enhance or jack up your legacy systems, you come across the need to bring about data migration.

Furthermore, most interfaces do not possess a built-in procedure for this process. Hence, you will most likely have to take matters into your own hands. Despite being routine tasks, all these are complex- both functionally and structurally. Consequently, they warrant relatively high financial and time investment.

RPA for data extraction and migration is a user-friendly, straightforward, and cost-efficient solution. Hence, it can easily handle the challenges of data operations by and large. Now, let us see how exactly it can render assistance, and save you a lot of snags.


It’s always a prerequisite to focusing on data migration as a form of transfer between old and new (i.e. in tandem with a legacy system and a newer type of software). Software robots can impeccably intermediate data transfer between systems because they can function independently on APIs (Application programming interfaces) 


Bots predominantly mimic human interaction. They can do so with the front-end user interface (UIs), thereby reducing the reliance on APIs. Software robots do not demand customized UIs. They can make the best of what is already available. Evading the need to pull relevant data from APIs boils down the entire migration process and guarantees faster and also highly accurate results.

A robot’s fundamental pattern recognition ability paves them to convert digital text format into machine-encoded text. This amounts to a considerable reduction of tedious manual data entry, in particular. Furthermore, you may instruct the robots to shift formatted data into the newer system.

The flexibility of RPA technology is a bonanza as it allows the robots to handle a large variety of data formats. Apart from that, it can also create log files as required in a particular situation. In addition, software robots can also radiate the log files as sighed.

The caliber of RPA to integrate with different technologies makes it a reliable data specialist. 


Another advantage of using robotic process automation for data extraction and migration resides in the bots’ capacity to track the migration process. By doing so, you are able to find out the inconsistencies and faulty datasets. As an add-on, you can also rectify the deficiencies in real-time, before accomplishing the migration. So, RPA saves a lot of audit time that would otherwise be needed in order to go through the whole data set once again, not knowing exactly where the error lies.

Scalability is another RPA feature that adds up to performing data migration in a timely manner. Besides, this is no small thing, since migration is most often a time-critical operation. Exclusion of the necessary data from the system where you need it is like trying to build a house without having built its foundation.

Final Thoughts:

The bottom line is that RPA lies beneath the successful use of data in the digital era. By “successful” we focus on both cost and operational efficiency. More importantly, robotic process automation services are faster and cheaper than API-developed processes. It is also accurate. The return on investment is phenomenally high and the entire migration time is determined in days and weeks, not months.

The robotic process automation software companies proffer improved migration quality, faster implementation and activation of the new system(s), and less disruption across the organization, subsequently.

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