ABSTRACT
With the rapidly changing technological landscape, automating daily routine tasks for accurate, reliable, and consistent outputs has become a priority for many companies.
Robotic process automation (RPA) can be understood as a software bot used to automate the daily and repetitive processes performed by humans. Its applications include information technology (IT), human resource, and finance.
Robotic process automation can successfully handle processes that have high volumes, are structured, and are repetitive. Robotic process automation differs from IT projects in the sense that it is a more business-centric and tailored solution. Process automation requires a mature process with a standard process in its built.
Cognitive automation has emerged as an effective tool for optimizing and reconfiguring service delivery models in human-centric processes. It has also become a useful tool for decision-making, whereas RPA is used to enter, capture, create, and update the data.
Purpose: This chapter explores RPA applications in data management.
The remainder of this chapter emphasizes and explains the different RPA tools and technologies used for effective and efficient data management. This chapter also discusses use cases from the industry as practical application examples of RPA.
Methodology: Robotic process automation is currently understood to be a very effective tool for managing numerous repetitive tasks in the aggregation and curation of data. Robotic process automation is generally applied to large repositories for tasks, such as data cleansing, normalization, data wrangling, and creation and updating of metadata, more efficiently and effectively.
A systematic explanation of these concepts and how RPA can be used to effectively manage data can provide clear insights to numerous RPA users. Therefore, researchers have attempted to bridge this research gap.
Outcomes: Robotic process automation is an effective technology that has a tremendous impact on industries and society, and it intends to cut short daily and repetitive tasks, thereby helping people to focus more on further productive occupations.
The model proposed in this chapter will assist learners working with structured or unstructured data and handling RPA processes to increase their productivity, as it will not only provide essential concepts but also practical illustrations of the uses of RPA in data management.
Originality/Value: Insightful knowledge on the use of RPA tools and techniques for data management, along with a discussion of use cases, is not readily available. We intend to provide the same, which can be used as a reference for professionals.
Moreover, as we use an inquiry-based learning approach with ample illustrations for presentation, it would help readers understand the fundamentals of RPA applications in data management more easily. Industry use cases are also discussed for better understanding.
