Optimizing Information Management for Decision Support: Insights from Big Data Analytics
TL;DR
Whether big-data analysis optimizes organisational Information Management Practice to improve the Quality of Decision Support under Organisational Context is examined to improve the Quality of Decision Support under Organisational Context.
Abstract
Background: With the explosion of data in our time, organisational information management and decisions have undergone a complete transformation. Larger scale Data Analysis may help detect numerous hidden Relationships in vast volumes Unstructured Information; yet this field also fails to produce concrete business guidance for all companies. Objective: To examine whether big-data analysis optimizes organisational Information Management Practice to improve the Quality of Decision Support under Organisational Context. Method: Based on publicly available data from international organizations such as IDC's global data sphere, the World Economic Forum's Global Competitiveness Report, and relevant empirical research to synthesise evidence about the trend of BDA application, information management problems, and the results of decision-making support. A theoretical Framework combining the DIKW hierarchy, Decision-support System (DSS), Theory and the Resource-based view (RBV) is proposed and tested. Results: Global Data Creation will be approximately 175 ZB by 2025. Organizations with mature BDA capabilities demonstrate 5-6% higher productivity and 4-6% higher profitability than competitors. Optimisation Strategies for Key Points: Real-time Data Integration; Predictive Analytics Deployment; Governance Framework Building. To sum up, a good use of big data analysis to manage needs more effective decision-making support; However, all these changes are also related to strengthening the company's governance capability and achieving strategic alignment well.