The role of vendors in addressing data management systems challenges towards improving data-driven organizational processes

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.