Master Data Management Basics

Master Data Management (MDM)

• MDM is a combination of applications and technologies used to consolidate, cleanse, and augment corporate master data, synchronizing it with applications, business processes, and analytical tools.
• It significantly improves operational efficiency, reporting, and fact-based decision-making.
• Master Data is critical information for transactional and analytical business operations.
• Fragmented and inconsistent data creates significant issues regarding sales, marketing, supplier relationships, etc. and results in higher costs.

Introduction

• Fragmented and inconsistent data increases time-to-market, creates supply chain inefficiencies, weakens market penetration, and increases compliance costs.
• Inconsistent customer data hides revenue, introduces risks, and creates sales inefficiencies.
• Inconsistent supplier data reduces supply chain efficiency, impacts spend control initiatives, and increases the risk of supplier exceptions.

Enterprise Data

• Enterprises have three types of data: Transactional, Analytical, and Master.
• Transactional data supports applications by recording transactions. Analytical data enables decision-making.
• Master data represents business objects (e.g., Customers, Products).

Operational MDM

• Solutions focusing on managing transactional data within operational applications.
• They heavily rely on integration technologies.
• They improve operational efficiency but do not influence reporting or analytics.

Analytical Data

• Used for decision-making (e.g., customer buying patterns).
• Analyzes large datasets in data warehouses for insight.
• Includes identification of failure patterns, profitability, and marketing segments.

Analytical MDM

• Solutions focusing on managing Analytical Master Data.
• Focuses on providing high-quality dimensions with their multiple simultaneous hierarchies for data warehousing and business intelligence (BI) technologies.
• Improves reporting accuracy and fact-based decision-making, but has no influence on operational systems.

Master Data

• Represents shared business objects across multiple transactional applications.
• Creates a single version of the truth across the operational IT landscape
• Examples include Customers, Suppliers, Sites, Accounts, and Products.

Information Architecture

• Almost all companies have a heterogeneous set of applications (home-grown, purchased from vendors, or inherited).
• MDM must seamlessly integrate with modern Service-Oriented Architectures (SOAs) and Business Process Orchestration to manage master data across multiple systems.
• MDM becomes the central source for accurate, fully cross-referenced, real-time master data.

Enterprise Application Integration (EAI)

• EAI uses a metadata-driven approach to synchronize data across applications by storing details about which data to move, when to move it, what transformations to perform, and error recovery processes.
• MDM enhances and manages SOA and BI systems in addition to supporting data governance, which defines data standards, access rights, and quality rules.

Service-Oriented Architecture (SOA)

• SOA exposes application features and functions as shared services using standardized interfaces that can be combined into end-to-end business processes.

Data Warehousing

• The Enterprise Data Warehouse (EDW) stores transaction history from operational applications.
• Master Data delivers accurate dimensions, hierarchies, and cross references to the data warehouse, reducing reporting time.

Business Intelligence (BI)

• BI tools leverage a Data Warehouse solution for business users to gain report-based information.

Data Quality Issues

• EAI and SOA don't address fragmentation and poor-quality data problems that exist in pre-EAI/SOA environments.

Oracle MDM High-Level Architecture

• Oracle Fusion Middleware provides the supporting infrastructure
• The Application Layer includes pre-built MDM Hubs and shared services.
• The Governance Layer includes data governance solutions.

MDM Application Layer

• Oracle MDM includes purpose-built master data management applications (Hubs) and related data quality servers.

Oracle MDM Pillars

• Customer Hub, Product Hub, Supplier Hub, Site Hub, and Data Relationship Management.

MDM Processes

• Profile—Understanding data sources and quality.
• Consolidate—Centralizing data into a master repository.
• Govern—Establishing policies, rules, and quality standards for master data.
• Share—Distributing master data to applications and ensuring consistency across the IT landscape.
• Leverage—Using master data for enhanced reporting and decision-making.

Oracle Data Quality Services

• Addresses issues of unstructured, non-standard, and incomplete product data.
• Used for standardization, cleansing, and matching of master data.

Data Watch and Repair (DWR)

• Provides monitoring capabilities for data quality and enforcement of governance rules in MDM Hubs.
• Supports data profiling, discovery, and correction, augmenting the data quality process.

MDM Implementation Best Practices

• Strong governance, a robust data quality solution, and integration tools, are crucial for effective MDM implementations.
• Oracle’s approach for MDM implementations is designed to support large or small projects and organizations that must handle large volumes of data.

Conclusion

• MDM delivers significant business value and enhances operational efficiency.
• Utilizing Oracle MDM enables companies to govern data assets, operationalize data warehouses, consolidate systems, modernize applications, and improve reporting.
• Oracle's MDM provides a more complete set of products and capabilities than other solutions in the market.