Data Governance for Big Data Business Intelligence: A Conceptual Framework and Research Agenda

Abstract

Organizations increasingly depend on big data business intelligence (BI) to support operational and strategic decisions. Yet the realized value of BI remains uneven. Practitioners frequently attribute shortfalls not to data scarcity or analytical capability but to governance failures: unclear data ownership, inconsistent metric definitions, weak lineage, slow access approvals, and uncontrolled proliferation of shadow analytics. Big data amplifies these problems through continuous pipeline change, semantic heterogeneity, decentralized ownership, and the embedding of algorithmic outputs into decision workflows. Prior research offers governance principles and reference models, yet the field lacks an outcome-centered conceptual framework that specifies how governance mechanisms translate into BI performance under big data conditions. This paper addresses that gap. We propose a mechanism-based conceptual framework linking governance mechanisms (structural, process, technical) to BI outcomes (trustworthiness, decision quality, agility, risk/compliance) through four intermediate governance capabilities: data quality assurance, lineage and traceability, access agility with control, and analytical accountability. We develop six testable research propositions (P1–P6) specifying directional relationships and theorized boundary conditions including decentralization and regulatory intensity. We conclude with a focused research agenda comprising operationalization guidance, study designs, and units of analysis to support cumulative empirical inquiry. The framework contributes a diagnostic lens for practitioners and a theoretically grounded foundation for hypothesis testing in big data BI governance research.

Introduction

Business Intelligence (BI) has evolved from descriptive reporting to real-time, data-driven decision support, integrating heterogeneous and streaming data, machine learning, and self-service platforms. Paradoxically, this abundance of data makes governance more complex rather than simpler. BI shortfalls often result from governance failures—unclear ownership, weak lineage, manual approvals, shadow datasets, and fragmented accountability—rather than technological deficits. Big data amplifies these challenges due to high velocity, variety, volume, and distributed ownership.

Conceptual Foundation

Data Governance Definition:

• Governance = organizational structures, decision rights, processes, and technical controls guiding how data and analytics artifacts are created, accessed, used, and retired.

• Distinct from data management (execution) and IT governance (overall IT oversight).

Key Shifts with Big Data:

1. Velocity: Continuous pipeline evolution, schema drift.

2. Variety/Semantics: Diverse data types and inconsistent meanings.

3. Volume & Reuse: Large datasets reused in unintended contexts, increasing risk.

4. Distributed Ownership: Fragmented responsibility; cross-team accountability needed.

Conceptual Framework

Four-layer model:

1. Governance Mechanisms (Inputs):

   • Structural (SM): Roles, councils, stewards—“Who decides?”

   • Process (PM): Workflows, policies, SLAs—“How are decisions enforced?”

   • Technical (TM): Metadata catalogs, automated lineage, access controls—“How is governance implemented?”

2. Intermediate Governance Capabilities (Abilities):

   • Data Quality (DQ): Accuracy, completeness, consistency.

   • Lineage & Traceability (LT): Trace outputs to sources.

   • Access Agility with Control (AA): Fast, compliant provisioning.

   • Analytical Accountability (AC): Clear ownership and oversight of BI outputs.

3. BI Outcomes (Performance):

   • Trustworthiness (TR)

   • Decision quality (DQl)

   • Agility (AG)

   • Risk/compliance (RC)

4. Boundary Conditions (Context Moderators):

   • Decentralization: Distributed authority increases reliance on structural mechanisms.

   • Regulatory Intensity: High regulations amplify the effect of technical mechanisms.

   • Platform Maturity: Immature platforms limit the effectiveness of structural and process mechanisms.

Research Propositions (P1–P6)

1. Mechanisms → Capabilities: SM, PM, TM positively impact DQ, LT, AA, AC.

2. DQ & LT → Trustworthiness: Higher data quality and traceability increase BI trust.

3. AA → Agility: Access agility enhances BI agility, strengthened by technical automation.

4. AC → Decision Quality: Analytical accountability improves decision-making, especially with algorithmic outputs.

5. Decentralization Moderation: Structural mechanisms are more effective in highly decentralized environments.

6. Regulatory Intensity Moderation: Technical mechanisms yield greater risk/compliance benefits under high regulation.

Research Agenda & Methods

• Construct Operationalization: Measure mechanisms, capabilities, and outcomes via perceptual and objective metrics (e.g., certified datasets, access provisioning times, incident rates).

• Study Designs: Comparative case studies, surveys, quasi-experiments, mixed methods combining telemetry and interviews.

• Units of Analysis: Enterprise BI programs, domain teams, or recurring decision workflows.

Implications

Theoretical:

• Governance framed as a capability-building system rather than compliance overlay.

• Identifies intermediate capabilities, clarifying which functions are underperforming.

• Introduces boundary conditions for context-sensitive governance design.

Managerial:

• Offers a diagnostic lens: identify weak capabilities to improve trust, speed, or risk control.

• Guides investment decisions based on decentralization, regulation, and platform maturity.

Limitations:

• Conceptual, not empirical; needs validation.

• Focuses on enterprise BI; does not fully address public-sector or LLM-enabled BI.

• Mechanisms co-evolve in practice; interdependencies require further study.

This framework helps organizations translate governance mechanisms into measurable BI outcomes, balancing trust, agility, decision quality, and compliance under big data conditions.

If you want, I can also make a visual diagram summarizing the four-layer governance framework with mechanisms, capabilities, outcomes, and moderators—it would make this dense information much easier to grasp.

Conclusion

Big data BI elevates both the opportunity and the risk of data-driven decision-making. Governance is increasingly a prerequisite for scalable, trusted BI, yet governance designs can also impede agility if misaligned with organizational structure and technical maturity. This paper has proposed a mechanism-based conceptual framework that links governance mechanisms (structural, process, technical) to BI outcomes (trust, decision quality, agility, risk/compliance) through four intermediate governance capabilities: data quality assurance, lineage/traceability, access agility with control, and analytical accountability. Six testable propositions specify directional relationships and theorized moderators including decentralization, regulatory intensity, and platform maturity. The accompanying research agenda outlines measurement approaches and study designs to support cumulative empirical inquiry. The framework is intended to move governance research toward outcome-centered, empirically testable theory and to help organizations govern big data BI in ways that preserve both control and speed.

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Copyright

Copyright © 2026 Zeyad Faieq Suleiman Abuhuweidi, Ibrahim Khaled Qasem Farea. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.