Data Warehouse Integration in Business Intelligence for Sustainable Competitive Advantage
Keywords:
Sustainability, Competitive Advantage, Sustainable Development, Data Warehousing, Business IntelligenceAbstract
Background: In the digital era, Data Warehousing (DW) and Business Intelligence (BI) are pivotal for organizations aiming to enhance operational efficiency and competitiveness. Integrating these technologies with sustainability practices can offer organizations a sustainable competitive advantage (SCA). However, there is a research gap in exploring how BI and DW can be aligned with sustainability objectives to promote long-term success.
Methods: This study employs a mixed-method approach, combining a comprehensive literature review and case studies of companies integrating sustainability into their BI and DW systems. The literature review identifies challenges and opportunities in the integration, while the case studies provide real-world insights through semi-structured interviews with key stakeholders in various industries.
Results: The study finds that integrating sustainability metrics, such as carbon footprint and social impact, into BI and DW systems enables data-driven decision-making that aligns with both business goals and sustainable development. Companies that have successfully integrated sustainability into their data systems report improved operational performance and a better market position.
Conclusions: Integrating sustainability with BI and DW systems can enhance operational efficiency, strengthen stakeholder relationships, and foster innovation. A conceptual framework is proposed to guide businesses in embedding sustainability into their data practices, ensuring long-term success.
Implications: This research offers a framework for organizations seeking to align their BI and DW systems with sustainability goals. It also highlights the importance of adopting flexible infrastructure and continuous adaptation to evolving sustainability standards.
Limitations: The study’s findings are based on selected case studies, which may limit generalizability across industries
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