How RPA Can Help in Data Cleansing for Analysis

The task of data cleansing is boring, but very important for businesses. Can advanced technology like RPA take this up? stressful work?

Data cleansing is a tedious task for employees as it drastically slows down business processes and functions. However, this is a critical aspect that cannot be ignored in the action plans as it hinders the flow of business processes. The accuracy of the analysis results would be at risk due to their poor quality.

Can Robotic process automation (RPA) take over the monotonous data cleansing work?

While RPA is not a direct booster for big data, it can help companies prepare the data for analysis. It can absolutely replicate the human actions on routine tasks like data entry. As soon as the user enters new billing data, automation software can easily take it over.

The technology can scrape the data off the screens entered by a user and eventually transfer it to other systems that need it. This ensures the consistency of the data between different systems. Business and data manipulation policies can be encoded in RPA – which allows data to be normalized or even corrected according to the standards of the company or its systems.

Of course, process automation can be merged into an analysis toolset that uses big data. Since companies can program their data processing and normalization rules in an RPA routine, there is the possibility of automating the manual work. Users often do this to ensure high quality data for analysis.

However, there are also certain restrictions. Automation solutions, for example, can only work with structured standard transaction data. Most analyzes use data that is a mixture of structured and unstructured data.

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For example, if companies want to model the occurrence of the pandemic among residents of a particular city and map the hotspots, they need to merge the transactional data from the medical units. This could be done with the help of mapping tools for big data endpoints.

It is imperative to clean up all of this data to get the correct result. While the data team uses specialized tools to manipulate the large unstructured data, it can also combine RPA to clean up the structured transactional data – which is part of the analysis process.

Over time, new business guidelines for RPA would be created that can improve performance. Larger companies have already used machine learning to train robot logic for higher value transactional data and process improvements.

RPA can be used to aid in pre-cleaning transactional data that is then used in the analysis. As a result, IT teams and data scientists can leverage such tools to save time while focusing on other important aspects of the business.

July 2, 2021