Leveraging Data Analytics to Improve User Satisfaction for Key Personas: The Impact of Feedback Loops

Ranjit Kumar Gupta; Sagar Shukla; Anaswara Thekkan Rajan; Sneha Aravind

doi:10.36676/jrps.v11.i4.1489

Authors

Ranjit Kumar Gupta Independent Researcher, USA
Sagar Shukla Independent Researcher, USA
Anaswara Thekkan Rajan Independent Researcher, USA
Sneha Aravind Independent Researcher, USA

DOI:

https://doi.org/10.36676/jrps.v11.i4.1489

Keywords:

Human Computer Interaction, Analytic Tools, Adaptive Case Management (ACM), Leveraging, Single Application, Big Data Analysis, User-Manufacturer Interaction, Agile Embraces

Abstract

A well-known method in human-computer interaction is the use of personas. Nevertheless, robust empirical study comparing persons to alternative approaches is lacking. Agile techniques' theoretical underpinnings are based on a number of important frameworks of theory. According to complexity theory, emergent features are present in complex systems, such as digital financial ecosystems. These are difficult for conventional linear algorithms to forecast. Agile recognises this complexity and supports continuous improvement cycles and flexibility to changing requirements. Lean thinking, which is drawn from manufacturing, places a higher priority on maximising customer value and removing waste. A promising market for IT-driven products, or Smart, Connected Products (SCPs), is made possible in this day and age by the rapid growth of Information and Communication Technology (ICT) (such as wireless sensor networks and cyber-physical systems). This also modifies the way that manufacturers and users interact during the product's process of development. Big data analysis allows businesses to base judgements on facts instead of gut feeling or internal knowledge that may be disguised. Business organisations gain from better informed choices and consistent results than ever before when knowledge workers and those making decisions have access to reliable sources of information that are constantly updated and accurate, as well as the analytical tools needed to make sense of it. The manner in which knowledge workers carry out their duties and the company's overall operations may be impacted by this method of decision support. Large data sets provided insights for analytical tools. They contribute to the expansion of the context and backgrounds that are available for rational, precise, and coherent decision-making. This study illustrates how Adaptive Case Management (ACM) systems' DSS component can be expanded through Big Data analysis. The article's authors explore the idea that businesses might use big data to enhance their operations by combining analytical tools, adaptable process management, and easy access to all the necessary data into a single application.

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