A collaborative model for data governance: enhancing integration across multi-line businesses

Abstract

In today's increasingly data-driven business environment, organizations with multiple lines of business face significant challenges in managing data effectively. Fragmented and siloed data governance models can hinder decision-making, reduce data quality, and create inefficiencies across business units. This review explores the development of a collaborative data governance model designed to enhance integration across multi-line businesses. By unifying data governance frameworks, fostering cross-functional collaboration, and standardizing data policies, the proposed model aims to break down silos and create a more cohesive approach to data management. Key components include the establishment of data governance councils, the appointment of data stewards in each business unit, and the adoption of advanced data technologies that facilitate seamless integration. The collaborative model encourages interdepartmental communication and shared objectives, ensuring that data governance aligns with broader organizational goals. It also emphasizes the importance of maintaining data security and privacy while enabling data sharing across departments. Case studies of successful implementations in various industries are presented, highlighting best practices and lessons learned. Additionally, the review identifies potential challenges, such as cultural resistance, technical barriers, and resource allocation issues, offering strategies for mitigation. By adopting a collaborative data governance approach, multi-line businesses can improve data quality, enhance operational efficiency, and ensure better regulatory compliance. The review concludes with a forward-looking view on the scalability of this model and the role of emerging technologies, such as artificial intelligence, in automating and enhancing data governance processes in the future.

Keywords: Collaborative model, Data governance, Multi-line, Review.