A Guide To Using RPA For Data Analytics

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7th December 2022
6 Minute Read

What is RPA?


Robotic process automation (RPA) is one of the most widely used digital technologies that mimics human interaction with software to carry out mostly straightforward, recurring, and repeatable tasks.

RPA bots are programmed to execute a variety of non-invasive actions such as data entry, copying and pasting data between applications, logging in and out of applications, and many others.


What is data analytics?


Data analytics (DA) is the examination of different data sets to identify patterns and trends and then draw interpretations and make conclusions about the information they contain.

As data analytics becomes more specialized so do the systems and software utilized so that companies can make better-informed decisions from the interpretations.

In science and research, data analytics is a vital tool that acts as a mainstay for testing theories and scientific models. In combining RPA for data analytics, companies have discovered a powerful combination, which is quick, cost-effective, and produces valuable insights.


Using RPA for integration, data entry, and migration


RPA for Data Integration

When a company’s data systems haven’t been integrated, entering new data or the retrieval or analysis of existing data is severely compromised. RPA is an ideal tool to bring structure and classification, which makes the data that much more accessible and insightful.

Previously, data cleansing was a tedious and time-consuming task that could cause long delays in business operations but at the same time couldn’t be avoided. Without cleansing the poor quality of data could significantly affect analytics results.

RPA can also take over the task of the preliminary data cleansing of databases to ensure they are in the best condition prior to data analytics.


RPA for Data Entry

Usually, data entry is performed manually, which is a laborious and repetitive task that keeps recurring. Being mundane the process is particularly prone to human error, as attentions wander with the lack of stimulation.

RPA is a potent solution where simple rule-based tasks are programmed, which operate accurately, quickly, and 24 hours a day if necessary.

In automating data entry, the software is designed to streamline the data flow that is normally handled by humans. More complex RPA packages can recognize and understand data from several sources, such as online forms, printed reports, emails, and PDFs.

Once scanned the information is automatically entered into databases or spreadsheets, with none of the human error the task was once associated with.


  •  Enhanced accuracy in calculations, more streamlined workflows, and better visibility
  •  Significant savings in time and money through less paperwork management
  •  Data is effectively handled whether in entry, extract, processing, or cleansing
  •  Data collation is more effective and comprehensive, as data from other sources can be directed into the analysis.
  •  The improved transparency of analysis highlights any fraudulent activity


RPA for Data Migration

In medium and large size companies it’s not unusual for data migration to be needed to merge the business information from two separate sources. This could occur through a merger, an acquisition, modernization, the finalization of a legacy system, or a transfer to the Cloud. 

Migration sounds simple in theory but in practice, it is complicated, expensive, time-consuming, and risky. The process is broken down into a sequence, which includes the extraction of the information from the old database, its preparation, and finally its transfer to the new database.

Again, RPA is ideally designed to work under these constraints where the 4 different steps are highly structured and rules-based:

The Extract Design: The scope of how the data will be extracted, prepared, and verified

Transformation: The rules are designed for the transformation of the data to the new database

Loading: The extracted and transformed data is transferred into the new database

Testing and Recovery: Test plans are created for reports on the migration, as well as rollback and recovery procedures for each step, in case of errors.


  •  Significant improvement in migration speed. RPA bots don’t need a break.
  •  With little human intervention throughout the process, errors are nearly eliminated.
  •  Using RPA for data migration is a cost-effective solution, as there is no requirement for coding.
  •  The migration process is fully tracked at each step, so any inaccuracies can be easily identified and rectified
  •  RPA provides a more flexible and scalable solution, so the company can react to change more effectively. RPA can also cope with different data formats.


Using RPA for data analytics


Use case: Machine Learning


In recording RPA process test trials, these can be applied to different machine learning (ML) algorithms, which in turn will produce optimum strategies for managing processes. At the same time, ML models can be used to show which factors affect a process and by how much.


Use case: Process Mining


Applying process mining technology to the data created from RPA can generate much more insightful information about the process. Applied in reverse, process mining apps can create data, which suggests the ideal process for RPA development.


The benefits of using RPA for data analytics 


Decreased costs

As RPA technology becomes ever more widespread through industry, the capital cost of implementation naturally falls. The technology isn’t expensive and reasonably easy to implement, so a healthy ROI is often the dividend.

Employing staff to analyze data is expensive and their accuracy in comparison to RPA is markedly lower. Many of the tasks of analysis are mundane and repetitive, which leaves employees feeling undervalued and bored and, consequently, even more prone to making mistakes.

With the workload of menial tasks transferred to RPA, employees can be redirected to more fulfilling work. This can be anything from more customer contact, which needs a personal touch, through to analysis that can’t be conducted by technology and needs human reasoning to get it right.


Reduction of errors

So long as RPA is coded correctly and checked, then the input data will produce accurate output. Unfortunately, human counterparts are lacking the same efficiency. Prone to tiredness, boredom, and plain just having a bad day can take their toll on the accuracy of input data. Input errors can be difficult to detect and even harder to rectify.

The approach of RPA is much more clinical, with no off days or distractions to muddy the data. This leads to more accurate analysis and interpretation, which in turn creates better business decisions from which the company and customers can benefit.


Improved efficiency

In the past, the processes of data analytics were involved and time-consuming. Each step of the analysis was distinct, which allowed the possibility of error to creep in at different points. In automating data analysis, once the system is set up, it can run independently without the need for human intervention.

This creates an efficiency win-win, where the analysis is fast and accurate but at the same time less troubled by errors. Data analytics staff can also benefit from working in a more interesting department, where they spend less time on tedious but necessary tasks and more time on creating value for the company, with improving the department and innovation.

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