CRM Data Quality and Governance Framework for Predictive Engagement in Life Sciences

Abstract

The life sciences industry encounters an increasing problem in the need to balance between predictive analytics and the ability to comply with strict regulatory requirements in Customer Relationship Management systems. The credible quality of data and effective governance frameworks become the background conditions of credible predictive involvement in the work of healthcare providers, insurers, and patients. This theoretical framework balances Information Quality Theory, IT Governance Models, and principles of data stewardship to fill the essential gap, in which organizations can implement elaborate predictive algorithms whilst governance mechanisms are fragmented and reactive. The proposed architecture includes four layers that interact with each other: the Input Layer, which deals with the integration of heterogeneous data sources;, the Governance Layer, which defines the structure of stewardship and accountability, the AI Support Layer, which places algorithmic capabilities as a human augmentation, and the Outcome Layer, which describes data states that are sufficient compliant predictive engagement. Three hypothetical hypotheses formulate empirically testable propositions between governance maturity and predictive model performance, the AI oversight needs in regulated settings, and ethical stewardship as an intermediary between trust in algorithmic engagement choices. The framework contributes to the theoretical knowledge by placing data governance maturity as an enabler of analytical capacity, not a necessity of compliance, but offers practical advice to organizations balancing commercial analytics and regulatory limitations, and ethical stakeholder involvement in pharmaceutical operational environments.