Master data management is having a moment, and not because it’s a new discipline. If anything, the growth and evolution of MDM reflects how quickly organizations across industries are adapting their strategies for better operations, more advanced analytics and smarter compliance. The faster businesses push to modernize, the more obvious it becomes that without trusted, connected master data, even the most transformative initiatives struggle to achieve meaningful outcomes.
To understand how this plays out in different sectors, Profisee recently published three industry-specific research reports:
- The State of Master Data Management in Financial Services
- The State of Master Data Management in Healthcare
- The State of Master Data Management in Manufacturing
Taken together, these reports provide a wide-angle view of where organizations stand today, what challenges continue to hold them back and how they’re approaching MDM as part of their broader digital strategies.
After more than 25 years working with organizations navigating the complexities of enterprise information management, these findings reflect a reality I recognize clearly: despite differences in market pressures, regulatory environments and operating models, financial services firms, healthcare organizations and manufacturers are struggling with master data challenges. And across all three, the push for integrating more data domains across the enterprise to power more efficient operations and advanced analytics with trusted data is emerging as a defining theme.
Cross-Industry Themes: Shared Challenges and New Momentum
Operational Efficiency, AI and Analytics are Accelerating Demand for MDM
Across every sector surveyed, organizations point to more efficient operations, AI adoption and advanced analytics as primary reasons to invest in better master data. Many have already discovered the hard way that analytics about customers, patients, providers and products are only as good as the data that fuels them, and the same is certainly true of AI. When data from any of these domains is fragmented or inaccurate, analytics initiatives like customer 360 tend to produce unreliable results, eroding confidence and potentially leading to negative business outcomes.
What’s changed is that many organizations no longer view MDM as a way to manage data from one or two systems. Rather, MDM is central to near-term strategy, elevating it from a back-office data program to a strategic business enabler.
Data Quality Issues Continue to Undermine Strategic Priorities
Despite years of investment in data platforms, data quality remains a foundational problem across industries. Respondents report persistent duplication, inconsistent attributes and conflicting versions of critical business entities. These issues don’t stay confined to IT; they ripple into customer experience, risk modeling, care delivery, supply chain efficiency and revenue integrity.
Among these challenges, the most complex is entity data that varies across organizational functions and supporting systems. While correcting a missing product attribute is straightforward, reconciling mismatched values between ERP and material management systems requires deeper harmonization — underscoring the unique value of MDM.
Organizations increasingly understand that without accurate, complete and consistent master data, digital transformation itself slows down.
Governance and Stewardship Gaps Remain Difficult to Close
Many organizations acknowledge that governance is not keeping pace with the volume and complexity of their data. Clear ownership is often missing. Stewardship responsibilities are inconsistently defined. Processes break down when data changes frequently or spans multiple business units.
These gaps limit the value of MDM and create uncertainty about how data should be maintained as business conditions evolve.
Integration, Speed and Agility Are Becoming Non-Negotiable
Enterprises are pushing for faster integration cycles, greater API enablement and more flexibility in how they manage new data domains. Legacy architectures and highly manual processes to share and harmonize master data across systems are unable to keep pace and are therefore falling out of favor.
The shift toward cloud-based and domain-centric MDM reflects a desire for agility: organizations want solutions that can start small, scale incrementally and expand as maturity increases.
MDM Maturity Still Lags Behind Ambition
Even as respondents identify MDM as mission-critical, many remain early in their maturity journey. This gap speaks to a growing realization across industries: a successful MDM program does not materialize from a single large-scale implementation. Instead, it emerges from practical, domain-driven steps that deliver tangible value quickly and incrementally.
Financial Services: Navigating Regulation and Complexity
Few industries feel data complexity more acutely than financial services. Financial nstitutions operate within dense regulatory frameworks, manage vast customer and counterparty networks and rely on data precision to inform risk models, compliance processes and customer interactions.
Key Insights from the Report
Financial organizations report a high prevalence of siloed data across core systems, risk, compliance, onboarding and customer service channels. This fragmentation directly impacts their ability to meet regulatory obligations such as KYC, AML and ESG reporting.
Increasingly, these teams are naming strategic business initiatives as a core reason for investing in MDM. They recognize that inconsistent customer and product data erodes operational performance — loan approvals, trade executions, householding — and makes risk scoring or fraud detection less effective.
The industry also shows some of the highest spending on data initiatives, yet maturity levels are uneven. Many institutions still struggle to reconcile legacy systems with emerging analytics and customer engagement needs.
What Leaders are Prioritizing Next
Financial services organizations are focusing on strengthening governed data management, modernizing integration architectures and expanding MDM beyond traditional customer domains. They’re aiming to unify master data across risk, product and compliance functions, and they’re increasingly using MDM to support real-time insights and regulatory transparency.
→ Download the report: https://profisee.com/resources/state-of-mdm-fin-serv/
Healthcare: Confronting Fragmentation Across the Care Ecosystem
Healthcare organizations face an especially thorny challenge: clinical, administrative and operational data spread across EHRs, claims systems, provider networks and external partners. Fragmentation disrupts care coordination and introduces administrative burden at every level.
Key Insights from the Report
Provider data quality is emerging as one of the most urgent gaps in the sector. Inaccurate provider directories, outdated credentialing information and inconsistent location and facility data create downstream issues ranging from claim denials to patient frustration. Health systems also report significant operational impacts stemming from poor data quality, including delays in care, errors in billing and inefficiencies across care teams.
As consolidation accelerates and value-based care expands, MDM adoption is climbing. Organizations recognize that unified patient, provider, plan and facility data is essential to executing new care and reimbursement models.
What Leaders are Prioritizing Next
Healthcare leaders are prioritizing provider data management and patient matching capabilities, both of which are central to compliance and care quality. They’re also aligning master data across multiple domains to streamline prior authorization, accelerate provider credentialing, support analytics, manage population health and better coordinate care. The goal is to reduce administrative friction and empower providers with more accurate and complete information.
Healthcare leaders also see AI adoption accelerating, from administrative support like clinical note generation to emerging clinical decision support. Consistent patient data across EHR, imaging, labs and pharmacy systems is critical for enabling these AI-driven use cases.
→ Download the report: https://profisee.com/resources/state-of-mdm-healthcare/
Manufacturing: Building a Foundation for Digital Transformation
Manufacturers operate at a scale and complexity that makes master data both critical and notoriously difficult. Global supply chains, distributed plants and fragmented product hierarchies introduce constant risk of inconsistency or duplication. Further complicating the data landscape, many manufacturers are highly acquisitive, which not only replicates data silos, but introduces different data standards, different material and product taxonomies, and potentially conflicting supplier agreements.
Key Insights from the Report
The research highlights persistent issues around product, material and supplier data. Many manufacturers still rely on legacy systems that do not communicate well with modern ERP, PLM or MES platforms. As companies pursue digital transformation initiatives such as smart factories, predictive maintenance and real-time supply chain visibility, these data gaps become more consequential.
Interest in MDM is growing as manufacturers recognize that these initiatives depend on harmonized, trusted master data.
What Leaders are Prioritizing Next
Manufacturers are focusing on harmonizing product and supplier data to improve operational efficiency and reliability. They’re also laying the groundwork for analytics-driven use cases like digital twins, automation and predictive maintenance. Again, incremental, domain-focused MDM strategies are gaining traction as organizations look for practical ways to improve data quality without disrupting production environments.
→ Download the report: https://profisee.com/resources/state-of-mdm-manufacturing/
What These Findings Signal About the Future of MDM
Industry-Specific Use Cases are Sharpening
While the core challenges with fragmented data are similar, the use cases diverge:
- Financial services: Unifying customer and product data for risk and compliance
- Healthcare: Improving provider and patient data for care coordination
- Manufacturing: Harmonizing product and supplier data for operational excellence
Pragmatic, Iterative MDM Strategies Will Dominate
Organizations are moving away from monolithic programs and toward purposeful, domain-by-domain approaches. They want rapid time-to-value, the ability to implement process changes incrementally, and flexible architectures that grow with them.
Data Management Is Entering a New Era
Modern data management is expanding beyond stewardship into organizational alignment and data congruency. This maturation is essential for scaling MDM and embedding trustworthy data into the business.
Closing Perspective
Across all three research studies, one truth stands out: organizations know that trusted data is foundational, and they are increasingly treating it as such. But they also know that success requires more than technology. It demands clarity about data ownership, alignment across business functions and a commitment to improving data one domain at a time.
After decades of working alongside organizations on their MDM journeys, I see this moment as an inflection point. The pressures of increased productivity, regulatory expectations and digital transformation continue to be core drivers for investing in proactive data management. The need to move beyond AI experimentation into operational AI adoption is forcing organizations to confront longstanding data challenges with new urgency.
The good news is that the path forward is clearer than ever: start where the business impact is greatest, build momentum through early wins and expand MDM as maturity grows. To explore how these trends are unfolding in detail, I encourage you to read the full industry reports and benchmark your organization’s MDM journey against your peers.
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