How RPA Can Make Data Extraction and Migration Easier

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Is there a role for robotic process automation (RPA) in data extraction and migration? A contextual definition of data migration provided by UiPath is “the movement of data from an older legacy system to its new replacement system”. The crux of the matter is that legacy systems will be switched off upon data extraction and migration. So, migration should be handled properly, because once it’s switched off, there’s no turning back.

This is why it is recommended to use RPA for data extraction and migration: its error-free, high precision and stability of output mitigate the legacy decommission risks.

Furthermore, digital technology such as RPA shares relevant properties with data migration, such as being rule based and highly systematic. According to Data Migration Info, a commonplace approach to data migration is ETL, which holds that there are three steps inherently involved in migration: Extract, Transform and Load.

All steps require 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 clearly spelled out. This rules-based approach is very similar with the requirements for a functional implementation of robotic process automation. Additionally, the integrative capacity of RPA technology was the main reason why we described data management as one of its top use cases.

How RPA helps

We live in the digital era and data operations are at the core of doing business. Whenever you want to upgrade your legacy systems, for example the addition of cloud apps/services, merging unconnected databases by linking data providers, or simply consolidating your knowledge infrastructure, you come across the need to perform data migration.

This raises three kinds of challenges:

  • You need to process the source data, extract what you need out of it, and map its format to the target system.
  • You must introduce the data into the new system, using custom interfaces.
  • Finally, you need to ensure that the migration has been successful and complete.

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

RPA for data extraction and migration is a user friendly, simple and cost-efficient solution to handle such challenges of data operations. So, let us see how exactly it can assist you, and save you a lot of headaches.

As we defined above, we focus on data migration as a form of transfer between old and new, i.e. between a legacy system and a newer type of software. Software robots can ideally intermediate data transfer between systems because they can function independently of application programming interfaces (APIs).


Bots mimic human interaction, and they need to do so only with the front end user interface (UIs), thereby mitigating the reliance on APIs. Software robots do not require customised UIs, they can make the best of what is already there. Avoiding the need to pull relevant data from APIs simplifies the entire migration process and promises faster and more accurate results.

All you need to do is to build the automation workflow to extract data from the legacy systems. A robots’ basic pattern recognition ability allows them to convert pretty much any digital text format into machine-encoded text, which can be easily edited and searched. This amounts to a substantial reduction of tedious manual data entry. Furthermore, you may instruct the robots to move formatted data into the newer system.

The flexibility of RPA technology is a goldmine, since it allows the robots to handle a large variety of data formats, and to create log files as required in a particular situation. In addition, software robots can also circulate the log files as desired, e.g., stored on a drive, sent by email, etc.

The capacity of RPA to integrate with different technologies makes it a reliable data specialist, which can thereby afford the ‘luxury’ of a holistic analysis of the transferred data. What does this mean in the larger scheme of things? A more competitive business that can better handle the fierce market rivalry.

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