The Estimated Age Limit of Storing Data and its Impact on Data Sustainability

Abstract

In the context of information technology and data management, the term "age limit of data" is not commonly used. You might, however, be referring to the idea of data lifecycle management or data retention. Policies and procedures for maintaining and handling data for a predetermined amount of time are referred to as data retention. Companies frequently create data retention policies to make sure they are in compliance with legal and regulatory obligations and to effectively manage their storage resources. particular types of data must be preserved for a particular amount of time according to regulatory or legal obligations in some situations. For instance, legislation or industry rules may specify the length of time that financial records or healthcare data must be kept on file. The management of data from the time it is created or acquired until it is used, shared, stored, and finally deleted or archived is known as data lifecycle management. The complex realm of data sustainability is explored in this study, along with the implications of calculating the age of data storage and the complex linkages between technology accessibility and adaptation. Data were collected using a multistage sampling technique that incorporated non-probability and probability methods, together with a research design that is cross sectional. The study reveals unexpected results, which verifies and enhance the Technology Acceptance Model (TAM).  TAM framework has been used because there is not much proof that data accessibility and sustainability are related. Unexpectedly, there was a negative correlation found between the predicted age limit of data storage and technology adaption, highlighting the need for a careful integration strategy. The study also highlights the significance of predicted data storage age limits for data sustainability, bolstering the argument for a comprehensive TAM framework that accounts for legislative dynamics. The results emphasize various adoption characteristics beyond accessibility and have practical consequences for quality policymakers, stakeholders, and institutions. This research contributes to data sustainability in addition to creating theoretical frameworks for integrating technology in organizational contexts. The methodical approach design concerns the accuracy of the study and assures the authenticity of the conclusions drawn from the research results.