Master data management: Transforming operations and growth

More companies are understanding the importance of quality data management in today’s ever-evolving business landscape, as high-quality data is the foundation of optimized operations, scalability and a competitive advantage. As organizations move toward increased cloud adoption, artificial intelligence integration and modern data platforms, master data management (MDM) and data governance strategies are becoming critical for success.

Implementing a hub and spoke data model creates greater organizational visibility and management capabilities to tie systems together. These efforts directly result in improved data quality, cost savings and enhanced customer experience. In addition, a hub and spoke model is presented as a practical, efficient way to implement data governance and improve analytics.

RSM US LLP principal Jason Proto and manager Liz Rizzi, along with Boomi product marketing manager Kim Kaluba, recently discussed actionable data insights and the power of data pipeline automation during the second of a two-part RSM data webinar series, titled MDM distribution hub: Benefits of implementing a data hub for master data management.

Data governance and MDM

Data governance is a set of policies that define policies, processes and roles to ensure data is trusted, available, understood and compliant, clarifying accountability and identifying essential data. It supports regulatory compliance, risk management and data-driven decision-making capabilities. MDM, on the other hand, creates a unified, consistent view of core business data such as customers, products, suppliers and locations across systems and functions. It helps reduce duplication and data inconsistencies while ensuring governance.

The two functions work together to build a strong enterprise data foundation, establishing accountability and effective use and management of data assets. Leading application integration solutions bring governance and MDM together in practice and align them with broader, strategic business and data goals.

Challenges and impact

Data quality is a key element of transforming your business operations and unlocking new opportunities for growth. Fragmented data and systems can create multiple versions of the truth and ambiguous outputs—otherwise known as garbage in, garbage out. Multiple challenges can emerge with poor data quality, including:

• Duplicate, inconsistent data records across business units and systems: Poor data quality results in inaccurate reporting, customer confusion and inefficient outcomes.
• Siloed business data across platforms: Siloed data creates challenges such as fragmented views of customers, products and other key domains.
• Manual data maintenance and standard processes: Manual processes make data hard to scale, trust and use effectively.
• Significant issues during mergers, acquisitions or system upgrades: Weaknesses in data quality can mean slow integration and increased risk and disruption.
• Lack of data governance: Insufficient governance leads to unclear ownership, redundant efforts and reduced productivity.

“The ultimate impact that stems from such data issues is all about weaker customer experience, higher operational risk and barriers to drive strategic, data-driven business outcomes,” says Rizzi. “The only reliable solution to address these challenges is to combine robust technology with strong data governance and management practices.”
 

Business outcomes and benefits

Undoubtedly, there are multiple, unparalleled benefits of decision making through the establishment and enforcement of effective data governance. The MDM layer enables better accountability and structure, allowing a company’s leadership team to gain clearer insights and act faster with confidence. Some of these benefits include:

• Data consistency: Establishes a single source of truth by eliminating data silos and inconsistencies across multiple data sources and processes
• Improved data quality: Enforces rules to keep data accurate, complete, consistent and valid across domains
• Operational efficiency: Reduces redundancy and duplication for streamlined data entry, updates and access
• Informed decisions: Strengthens accountability and governance across key domains, systems and projects
• Transparency: Increases visibility into data assets and promotes smarter, data-driven decisions

Industry use cases

Specific industries often face their own unique data challenges. However, implementing an effective MDM approach can alleviate many of those issues, delivering more accurate insights and a clearer path to success.

• Consumer products
  • Challenges: Duplicate product data, and inconsistent customer and supplier information, resulting in slow launches and poor retailer collaboration
  • MDM benefits: Accurate trade and demand planning, faster time-to-market, improved partnerships with retailers
• Financial services
  • Challenges: Fragmented customer profiles, compliance risks (for example, with anti-money laundering and know your customer laws) and siloed legal entity data
  • MDM benefits: Establishment of a single customer view, reduced compliance risk and better onboarding experiences
• Nonprofits
  • Challenges: Duplicate donor records, disconnected engagement, and weaker retention and fundraising
  • MDM benefits: Unified constituent master data, improved donor retention, clearer impact reporting and increased operational efficiency
• Manufacturing and industrials
  • Challenges: Inconsistent part, vendor and material data; poor bill of materials information; supplier redundancy
  • MDM benefits: Reduced procurement costs, fewer delays and stronger supplier performance management

“It is clear that MDM isn't necessarily a one-size-fits-all solution,” says Rizzi. “It addresses critical, tangible pain points that affect common business challenges across industries.”

Establishing an MDM program

To design and implement MDM efficiently, companies must evaluate scalability and future readiness, including AI governance. This helps businesses with cost savings and risk management. Key pillars for implementation include:

• Data transparency: Classify data, define ownership, ensure proper use, avoid risk, define attributes and track sources
• Data quality: Establish rules for accuracy, reconciliation and standardization, including data health and inconsistencies
• Data protection and privacy: Safeguard against data loss, manage identity and access, address insider threats, confirm compliance
• AI governance: Ensure scalability and compliance to mitigate risk, aligning with broader data strategy and business goals

“For MDM to be effective, it must be aligned with application roadmaps such as enterprise resource planning (ERP), product lifecycle management and customer relationship management (CRM) implementations. In addition, for business to have a 360-degree view, there must be a unified single source of truth across your CRM, ERP and data warehouse,” says Proto. “RSM’s enterprise data governance framework works as a foundation for data modernization, democratization and strategic alignment. Firms should understand that it is not just about meeting today’s needs but about setting the foundation for long-term success, optimized return on investment and supporting organizational strategies over the next three to seven years.”

In addition, for AI, a central domain prevents duplicate or incomplete records, maintaining accuracy and creating reliable customer experiences. The hub should establish data quality rules, governance processes, attributes and regulatory compliance standards such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), while supporting scalability for new sources and analytics. A 360-degree model streamlines operations like customer, product and vendor lifecycle management, reducing time spent on data wrangling.

“As we talk about the hub and spoke model, the most important point to consider is to identify who is a producer, who is a consumer, who does both and what integrations are needed to that centralized source of truth, the reporting and analytics?” says Proto. “Finally, on the advanced analytics, how this is going to support the decision making, machine learning or the agentic AI models that you are putting in place?”
 

How application integration solutions help with data governance

Application integration solutions provide a centralized, trusted location for clean, reliable and governed data. They preserve data privacy by encrypting sensitive data and offer traceability to track data evolution. With built-in quality and enrichment features, they can create complete records and reduce time and effort necessary to gather data.

In addition, they use high-quality data to accelerate AI initiatives promoting trusted AI-driven outcomes and informed decision-making capabilities.

To reduce data fragmentation across product descriptors or customer records, application integration solutions create the best golden record that is cleansed, standardized and governed by not only ingesting data but also sharing it bi-directionally with upstream and downstream systems. This creates consistency across the enterprise and fosters trust in data for analytics.

“A golden record gets created within a data governance framework, using the best data from multiple systems. For example, Salesforce may provide names, Oracle may provide emails and language, and NetSuite may provide addresses,” says Kaluba. “Application integration solutions use a flexible, open data model, allowing organizations to customize how their data is represented. AI assists in classifying data and recommending structures for the data model, speeding up the onboarding of master data for customers, products and vendors. This approach expedites MDM implementation while maintaining governance, flexibility and accuracy across the enterprise.”

Benefits of application integration solutions

Application integration solutions create the basis for reliable, governed and harmonized data, which is critical for long-term growth and a competitive advantage. Some of the key benefits of these solutions include:

• Deeper data insights: Understanding data integrity and traceability, including the evolution of your data across the organization, which is especially important for compliance with GDPR, CCPA and the Sarbanes-Oxley Act
• Data synchronization: Reducing fragmentation by providing high-quality, trusted data across systems, with bidirectional flows of accurate data
• AI-readiness: Establishing clean, trusted and AI-ready data, preventing flawed outputs and reducing time spent on data cleaning during analytical processes

The takeaway

MDM is not a one-time project but an ongoing process. Organizations are increasingly realizing its benefits, including significant ROI, as most companies find that the benefits of MDM are revealed almost immediately. Instead of business decisions made on poor data and resulting in poor outcomes and failures, operational efficiency increases, reducing data wrangling and analysis time.

Furthermore, to achieve your desired business data outcomes, it is imperative to thoroughly analyze business needs, pain points and use cases, including timelines and budgets. By leveraging data integration solutions as a part of your data strategy, you can undoubtedly obtain more trusted, high-quality data for informed and faster decisions, leading to scalable growth and positive long-term outcomes.