Short answer

Adopt a modular and layered approach to data governance, ensuring clear definitions of roles, responsibilities, and data standards that can be applied consistently across all cloud platforms within an organization's analytics ecosystem.

Field: Commercial Production
Source: International Journal of Advanced Multidisciplinary Research and Studies (2023)
Method: Conceptual Framework Development and Expert Validation
Evidence: Moderate effect

Implementing a structured, multi-layered governance framework with standardized metadata and defined roles is crucial for ensuring consistent data quality and assurance across diverse cloud analytics environments. This commercial production research insight is drawn from a 2023 study published in International Journal of Advanced Multidisciplinary Research and Studies. Using Conceptual framework development and expert validation, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Adopt a modular and layered approach to data governance, ensuring clear definitions of roles, responsibilities, and data standards that can be applied consistently across all cloud platforms within an organization's analytics ecosystem.

Study

Commercial ProductionRecentModerate effect

A Layered Governance Framework for Robust Multi-Cloud Data Quality

International Journal of Advanced Multidisciplinary Research and Studies · 2023

Key Findings

01A layered governance structure is effective for managing data across heterogeneous cloud platforms.
02Standardized metadata management and defined stakeholder roles are essential for consistent oversight.
03Interoperability models and continuous quality assurance processes are key to maintaining data reliability.

Application

Design takeaway

How to apply

When designing or implementing data analytics solutions that span multiple cloud providers, integrate a governance model that includes distinct layers for policy, operations, and quality assurance, supported by standardized metadata and automated monitoring.

Project actions

01When designing a system that uses multiple cloud services, think about how you'll manage and check the data's quality across all of them.
02Consider creating a 'data dictionary' or metadata standard that works for all the cloud platforms you're using.

Method & Evidence

AimHow can a conceptual framework be designed to ensure robust data governance and quality assurance in multi-cloud analytics ecosystems?

MethodConceptual Framework Development and Expert Validation

ProcedureThe research developed a conceptual framework for multi-cloud data governance and quality assurance, incorporating layered governance structures, metadata management, stakeholder roles, performance metrics, interoperability models, and a continuous quality assurance lifecycle. This framework was then refined through expert validation.

ContextMulti-cloud analytics ecosystems

Variables

IVImplementation of a layered governance framework, standardized metadata management, defined stakeholder roles, interoperability models, and continuous quality assurance lifecycle.

DVData governance robustness, data quality assurance effectiveness, data consistency across platforms, and overall data reliability.

CVSpecific cloud providers used, types of analytics performed, organizational size and structure, existing data governance policies.

Strengths & Limitations

Strengths

+Addresses a timely and relevant problem in modern IT architectures.
+Proposes a comprehensive and structured conceptual solution.

Limitations

The conceptual nature of the framework means its practical implementation details and effectiveness in diverse organizational contexts are not yet proven.

Reliability & validity

The reliability and validity of the framework would need to be established through empirical testing and comparison with existing data governance models in real-world multi-cloud deployments.

Think critically

How might the proposed layered governance framework be adapted to address the unique security and compliance requirements of different industries within a multi-cloud environment?

Design Principles

"Design for interoperability and layered governance to ensure data integrity and compliance in distributed systems."

As organizations increasingly adopt multi-cloud strategies for analytics, maintaining data integrity and compliance becomes a significant challenge. This framework provides a systematic approach to manage data assets across these distributed platforms, mitigating risks associated with data silos and inconsistencies.

What This Means for Your Design

To keep data good and safe when using multiple cloud services for analysis, you need a clear plan with different levels of rules, consistent ways to describe data, and defined jobs for people, plus ongoing checks.

How to use in your project

1.Reference this framework when discussing the challenges of data management in distributed systems and proposing solutions for data governance and quality assurance in your design project.

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Quick Cite

Paragraph starter

The challenges of maintaining data governance and quality assurance in multi-cloud analytics ecosystems necessitate a structured approach. This research proposes a conceptual framework that addresses these complexities through layered governance, standardized metadata, and continuous quality assurance, offering a valuable model for ensuring data integrity across distributed platforms.

Source

International Journal of Advanced Multidisciplinary Research and Studies

A Conceptual Framework for Building Robust Data Governance and Quality Assurance Models in Multi-Cloud Analytics Ecosystems

journal · 2023

View source

Questions About This Research

What does the research say about a layered governance framework for robust multi-cloud data quality?: Adopt a modular and layered approach to data governance, ensuring clear definitions of roles, responsibilities, and data standards that can be applied consistently across all cloud platforms within an organization's analytics ecosystem. Evidence: International Journal of Advanced Multidisciplinary Research and Studies (2023).
Why does "A Layered Governance Framework for Robust Multi-Cloud Data Quality" matter for design?: As organizations increasingly adopt multi-cloud strategies for analytics, maintaining data integrity and compliance becomes a significant challenge. This framework provides a systematic approach to manage data assets across these distributed platforms, mitigating risks associated with data silos and inconsistencies.
How can designers apply this research?: Adopt a modular and layered approach to data governance, ensuring clear definitions of roles, responsibilities, and data standards that can be applied consistently across all cloud platforms within an organization's analytics ecosystem.
What were the main findings?: A layered governance structure is effective for managing data across heterogeneous cloud platforms.. Standardized metadata management and defined stakeholder roles are essential for consistent oversight.. Interoperability models and continuous quality assurance processes are key to maintaining data reliability.
What research method was used?: Conceptual Framework Development and Expert Validation.
How strong is the evidence?: Evidence strength is rated Moderate effect, based on a 2023 journal from International Journal of Advanced Multidisciplinary Research and Studies.
What should I do differently in my next project?: When designing or implementing data analytics solutions that span multiple cloud providers, integrate a governance model that includes distinct layers for policy, operations, and quality assurance, supported by standardized metadata and automated monitoring.
What are the limitations?: The framework is conceptual and requires empirical validation to assess its practical effectiveness and scalability in real-world scenarios.