Paper Number
ECIS2026-2516
Paper Type
CRP
Abstract
Organisations increasingly invest in Big Data Analytics (BDA) to enhance decision-making, yet the relationship between analytical approaches and organisational outcomes remains unclear. This systematic literature review synthesises 62 peer-reviewed studies from 2020 to 2025 to examine how descriptive, predictive, and prescriptive analytics mediate decision processes across industries. Through inductive thematic synthesis, we identify three distinct decision domains influenced by BDA: operative decision enablement, transformative decision support, and adaptive decision capability. Our findings reveal that descriptive analytics primarily supports operational monitoring and real-time responsiveness, predictive analytics drives strategic learning and organisational resilience, while prescriptive analytics enables continuous organisational recalibration. The study identifies persistent tensions between automation and intuition, algorithmic autonomy and human control, and analytical precision and interpretive transparency that organisations must navigate to realise BDA’s full potential.
Recommended Citation
Trang, Phong; Chowdhury, Soumitra; Chaudhary, Indresh; and Kaluarachchilage, Kushan Rangana Rathnayake, "Three Faces Of Analytics: How Big Data Enables Operative Decisions, Drives Transformative Change, And Builds Adaptive Capability" (2026). ECIS 2026 Proceedings. 18.
https://aisel.aisnet.org/ecis2026/litrev/litrev/18
Three Faces Of Analytics: How Big Data Enables Operative Decisions, Drives Transformative Change, And Builds Adaptive Capability
Organisations increasingly invest in Big Data Analytics (BDA) to enhance decision-making, yet the relationship between analytical approaches and organisational outcomes remains unclear. This systematic literature review synthesises 62 peer-reviewed studies from 2020 to 2025 to examine how descriptive, predictive, and prescriptive analytics mediate decision processes across industries. Through inductive thematic synthesis, we identify three distinct decision domains influenced by BDA: operative decision enablement, transformative decision support, and adaptive decision capability. Our findings reveal that descriptive analytics primarily supports operational monitoring and real-time responsiveness, predictive analytics drives strategic learning and organisational resilience, while prescriptive analytics enables continuous organisational recalibration. The study identifies persistent tensions between automation and intuition, algorithmic autonomy and human control, and analytical precision and interpretive transparency that organisations must navigate to realise BDA’s full potential.