Helping organizations define and build a trusted data foundation that supports AI-driven decision-making, efficiency, and regulatory expectations, so you can modernize data capabilities and scale analytics-led use cases with confidence.
Data strategy, master data management, and governance
Overview
Organizations need data they can rely on to power analytics, support regulatory expectations, and enable AI at scale. PwC’s Data Strategy, Master Data Management (MDM), and Governance offerings help organizations define a clear data vision, strategy and roadmap, align operating models and technology, and establish decision rights that improve data quality and consistency across the enterprise, so teams can turn data into insight and action.
Capabilities
- Data strategy and roadmap
- Master Data Management
- Data governance and operating model design
- Data quality and standards
- AI-ready data foundations
- Data migration and modernization support
Data strategy and roadmap
Advise executive leaders on shaping an enterprise Data and AI strategy by assessing current maturity, aligning a future-state vision to business priorities, and defining the operating model, investment plan, and phased roadmap required to scale analytics and AI with measurable value.
Master Data Management
Design and implement MDM for critical domains (e.g., customer, product, supplier, asset) including golden record strategy, survivorship rules, hierarchies, and stewardship workflows—so downstream reporting, operations, and AI models consume consistent master and reference data.
Data governance and operating model design
Define decision rights, roles, processes, and controls (policies, lineage, issue management) that balance enablement with risk. Establish governance routines that fit how the business operates—so accountability is clear and adoption is sustained.
Data quality and standards
Implement practical DQ controls (profiling, rules, monitoring, exception workflows) focused on critical data elements and decision points—improving reliability without slowing delivery.
AI-ready data foundations
Prepare data environments for AI use (structured + unstructured), with governance and responsible AI considerations embedded. Support feature-ready datasets, metadata, and access patterns that enable scaling GenAI and ML safely.
Data migration and modernization support
Support ERP and cloud migrations with data readiness, reconciliation, and cutover controls. Reduce rework by enabling governance and MDM patterns are built into modernization from day one.
Use cases
PwC helped a leading healthcare services organization design and implement an AI-enabled oncology data platform in collaboration with Google Cloud. By centralizing clinical data, strengthening governance and embedding AI-driven extraction and analytics, the solution enhanced data consistency and provided clinical teams with more timely insights to support informed decision-making.
PwC helped a leading health insurer establish a hub-and-spoke AI center of excellence and deploy predictive models across clinical, member and operational functions. Over a multi-year engagement, AI capabilities were embedded into core decision-making processes, contributing to cost savings, improved member engagement, and stronger workforce planning.
PwC helped a healthcare organization define and implement a Responsible AI vision to support secure and effective AI adoption. By establishing governance principles, a risk-tiering framework and a structured intake process, the organization built a balanced approach that can help manage bias, privacy and security risks while supporting responsible AI adoption across the organization.
Data strategy, master data, and governance are no longer back-office concerns. They shape how organizations operate, how decisions get made, and how AI delivers value. Our focus is helping clients align data foundations to real business priorities, so data becomes an asset teams can rely on every day.
Rajeev KrishnanManaging Director and Offering Leader, PwC USCase studies
