SharePoint migration requires planning, governance, and user support

What the study found: The paper argues that enterprise SharePoint migration is a strategic process, not just a technical upgrade, and that successful migration depends on planning, governance, security, and user adoption measures.
Why the authors say this matters: The authors conclude that migration must protect data integrity, security, and regulatory compliance while helping organizations modernize their digital infrastructure.
What the researchers tested: The article reviews strategies and best practices for moving from legacy on-premises SharePoint environments to SharePoint Online (M365) or hybrid models, including pre-migration assessment, migration approach selection, governance frameworks, migration tools, phased rollouts, stakeholder engagement, metadata structuring, automation, and training.
What worked and what didn't: The paper says phased rollouts, stakeholder engagement, metadata structuring, automation-driven processes, encryption, access control policies, compliance alignment, and user training are important, while large-scale data handling, metadata and permission retention, customization compatibility, compliance risks, and downtime remain persistent challenges.
What to keep in mind: The abstract describes a proposed framework and identifies research gaps, but it does not report empirical test results or specific measured outcomes. It also notes that limitations are not described in the available summary.

Key points

• Enterprise SharePoint migration is described as a strategic process rather than only a technical upgrade.
• The paper emphasizes planning, governance, security, and user adoption as central to migration success.
• It highlights phased rollouts, stakeholder engagement, metadata structuring, and automation as useful practices.
• Persistent challenges include large-scale data handling, permission retention, customization compatibility, compliance risks, and downtime.
• The abstract says the paper proposes a framework and identifies research gaps, including AI-driven automation and hybrid model optimization.