By Sudipta Choudhury - November 30, 2020 3 Mins Read
The task of data cleansing is dull but highly essential for businesses. Can advanced technology like RPA take up this strenuous work?
Data cleansing is a painstaking task for workers as it dramatically slows down the business processes and functionalities. However, it is a critical aspect that can’t be left out of the action plans, because it will hamper the flow of business processes. The accuracy of analytics results would be at risk due to its low quality.
While RPA is not a direct enhancer for big data, it can help businesses get the data ready to be analyzed. It can absolutely replicate the human actions in rote tasks such as data entry. Once the user keys in new invoice data, automation software can easily take it over.
The technology can scrape off the data from the screens entered by a user and ultimately move them into other systems that need it. This ensures the uniformity of data among different systems. Business and data edit policies can be coded into RPA – which can normalize or even correct data to the standards that the company or its systems are set.
Clearly, process automation can be merged into an analytics toolset that uses big data. As businesses can program their data edit and normalization regulation in an RPA routine, there is a scope to automate the manual work. Often users do it to ensure high-quality data for analytics purposes.
However, there are certain limitations too. For instance, automation solutions can only operate on structured, standard, transactional data. Most analytics make use of data that are a blend of structured and unstructured data.
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For instance, if businesses want to model the incidence of the pandemic among residents in a particular city and map the hot spots, they will need to unite the transactional data from the medical units. This could be done with the help of mapping tools towards big data endpoints.
It is mandatory to cleanse all such data to achieve the proper outcome. While the data team will use specialized tools to edit the big unstructured data, they can combine RPA as well to cleanse the structured, transactional data – which is a part of the analytics process.
With time, new business policies for RPA that can enhance performance would be created. Bigger organizations have already used machine learning to train the robotic logic for higher-quality transactional data and process improvement.
RPA can be used to assist in the upfront cleansing of transactional data that is subsequently used in analytics. As a result, IT teams and data scientists can leverage such tools to save time while focusing on other crucial aspects of the organization.
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