A Systemic Approach to Decision Support and Automation: The Role of Big Data Analytics and Real-Time Processing in Management Information Systems

ÖNDEN A.

Systems, cilt.14, sa.2, 2026 (SSCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 2
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/systems14020216
  • Dergi Adı: Systems
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, Compendex, Directory of Open Access Journals
  • Anahtar Kelimeler: big data analytics, data infrastructure, decision support, management information systems, mixed-methods research, real-time processing, regression analysis, workflow automation
  • İstanbul Üniversitesi Adresli: Evet

Özet

Management Information Systems (MIS) are increasingly expected to support real-time, evidence-based decision-making and to automate routine workflows. Nevertheless, many organizations still struggle to transform heterogeneous, high-velocity data into trustworthy decision support and process execution at scale. Adopting a socio-technical systems perspective, this study explores the interplay between data infrastructure, analytics capabilities, and decision-making processes. We adopted a mixed-methods design, which incorporated (i) a cross-sectional survey of MIS professionals (n = 150) from organizations across three industries (retail, healthcare, and financial services) and (ii) 12 semi-structured stakeholder interviews. The survey data show that the performance outcomes of the organizations reporting a higher level of BDA and maturity in real-time processing are stronger, characterized by self-reported average revenue growth of 12% among retailers, a material decrease in operational costs, and improvements in overall system efficiency. These figures reflect respondents’ estimates rather than audited financial statements. BDA, real-time processing, and data infrastructure readiness were statistically significant predictors in an OLS regression model of perceived organizational performance, explaining a substantial percentage of variance (R2 = 0.72). The insights provided by the interviews explain how these effects were achieved: performance improvements materialized through real-time feedback loops where streaming and batch pipelines were integrated, data-quality controls were embedded in ingestion, and decision outputs were linked to workflow automation. The research contributes a holistic view to the MIS capability framework, linking data infrastructure decisions to the timeliness of decisions and automation preparedness, while contributing to the theoretical refinement of MIS capability frameworks and offering practical guidance for governance and technology selection.