Abstract:
This study investigates the key role of vendors in overcoming challenges within data
management systems to improve data-driven organizational processes. Addressing
cybersecurity vulnerabilities, scalability issues, interoperability challenges, and nonstandardised
data formats, this research delves into how vendors tackle these
impediments and optimize data-driven decision-making within organizations. Through a
purpose-sampling technique involving ten participants from a population of sixty-five,
thematic analysis was employed to obtain insights.
The finding and conclusion of the study show that to ensure scalability, vendors should
employ key strategies such as robust infrastructure, robust architecture, and cloud-based
solutions with dynamic scalability features to address scalability issues effectively.
Furthermore, the technical requirements to achieve interoperability found included
adherence to standards, secure data exchange mechanisms, and seamless integration
with third-party applications. The study also outlines the overall net benefits attributed to
data management systems, emphasizing increased productivity, reduced costs, and
improved decision-making processes. Furthermore, the study identifies critical focal
points for vendors aiming to enhance data management systems, including adaptability
to various data load patterns, data consistency across varied applications, improved
integration within cloud environments, and robust data privacy measures. Embracing
emerging technologies such as AI, machine learning and block chain, along with continual
investment in research and up-skilling, were recommended for sustainable system
innovation.
This study provides comprehensive information on how vendors address the challenges
in data management systems, offering practical recommendations for managers to
enhance scalability, standardize protocols, ensure data security, facilitate interoperability,
prioritize adaptability, embrace emerging technologies, and continuously improve
systems. Suggestions for future research include longitudinal studies tracking system
evolution and cultural impact analysis on data management practices, as well as
exploring ethical considerations in data system designs and vendor practices.
