Dears, Over the last several decades, IT landscapes have grown into complex arrays of different systems, applications, and technologies. This fragmented environment has created significant data problems. These data problems are breaking business processes; impeding Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), and Supply Chain Management (SCM) initiatives; corrupting analytics; and costing corporations billions of dollars a year. MDM attacks the enterprise data quality problem at its source on the operational side of the business. This is done in a coordinated fashion with the data warehousing / analytical side of the business. This combined approach is proving itself to be very successful in leading companies around the world.
MDM has become hot due to the perfect storm of:
· The continued struggle organizations face in managing their master data,
· The need to master the data in the operational environment, and
· Momentum with service-oriented architecture (SOA)-based software designed to systemize the rough spots.
MDM primarily refers to the 15 percent volume of organizational data that supports the organization's transactional data. In data warehouse vernacular, it is what we've come to call dimensional data. Master data management is the organization, management and distribution of corporately adjudicated information with widespread use in the company. It is part of the IT response to modern business requirements, along with right-time data warehousing, data mining, metadata and a data quality program
The problem of the existing IT infrastructures is the half-baked solutions that lack the power to combine operational and analytical data into a single view of the master data entities. This paper will discuss what it means to ‘manage’ master data and outlines Oracle’s MDM solution.
The N² problem
It starts with the N² (n square) problem that occurs when data synchronization between transactional applications is accomplished with code. IT managers all over the globe experience this on a daily basis.
Enterprise Application Integration
Of course there is a more elegant solution established over the last decade which is usually described as an information bus or hub-and-spoke topology, supported by Enterprise Aplication Integration (EAI) technology.
Service Oriented Architecture
EAI has recently evolved into Service Oriented Architecture (SOA) where interfaces can be called from an application's service layer independently of the programming language or software platform. SOA is more than just synchronizing data; it also supports business process orchestration across separate systems. Data Quality
So when your IT infrastructure has evolved to SOA, you will find that the old saying "garbage in, garbage out" is more true than ever before, because data quality is crucial to successful business process integration. So the paper concludes that an MDM solution that really deserves this name must also embed data quality solutions.
Data Warehousing, Business Intelligence and Reporting
Most companies today have established data warehouses and ETL processing that pulls data from the transactional systems into the DW. And most of them have invested in Business Intelligence tools to analyze the billions of records that they store in the DW in order to drive business decisions. So far so good.
But the following should make us think twice:
Company A rolls out the new CRM system. A few years later, the analytical CRM project is started Shortly after the first pilots, the data quality project is launched. So the BI system suffers from the poor data quality of the transactional systems. “Garbage in, garbage out again”
Ideal information architecture
The paper boldly announces the ideal information architecture which brings the MDM system into play as a central instance which enables metadata and data cleansing and forms the base source system for ETL.
Master Data Management Process
Now that we have identified the nature of master data and its place in Information architecture, we need to identify the key processes that MDM solutions need to support.
These are the key processes for any MDM system.
• Profile the master data. Understand all possible sources and the current state of data quality in each source.
• Consolidate the master data into a central repository and link it to all participating applications.
• Govern the master data. Clean it up, deduplicate it, and enrich it with information from 3rd party systems. Manage it according to business rules.
• Share it. Synchronize the central master data with the connected applications. Insure that data stays in sync across the IT landscape.
• Leverage the fact that a single version of the truth exists for all master data objects.
MDM Foundation Pillars
The MDM Applications are organized around five key pillars.
Pillar 1 : Trusted Master Data is held in a central MDM schema.
Pillar 2: Consolidation services manage the movement of master data into the central store.
Pillar 3: Cleansing services include deduplication, standardize and augment the master data.
Pillar 4: Sharing services include integration, web services, event propagation, and global standards based synchronization.
Pillar 5: Governance services control access, retrieval, privacy, auditing and change management rules.
Oracle Prebuilt MDM Data Hubs
It has been said that data outlasts applications. This means that an organization’s business data survives the changing application landscape. Technology advancements drive periodic application re-engineering, but the business products, suppliers, assets and customers remain. Oracle’s Master Data Management (MDM) solution is a set of applications (MDM Data Hubs) designed to consolidate, cleanse, enrich, and synchronize these key business data objects across the enterprise and across time. It includes pre-defined extensible data models and access methods with powerful applications to centrally manage the quality and lifecycle of master business data. Oracle offers a variety of products to build a solution and also offers pre-built MDM applications like Oracle Customer Hub, Oracle Product Hub & Oracle Site Hub.
Customer Hub is represented by the Siebel Customer Hub (UCM) application and Oracle Customer Data Hub ( Built on Oracle EBS TCA )
Oracle Product Information Management Data Hub (PIM Data Hub) is the Product MDM Data Hub
Site Hub is represented by Oracle Site Hub
Financial Hub is represented by Oracle Hyperion Data Relationship Management
As the holder of trusted master data, Oracle Master Data Hubs are integrated with the enterprise analytical systems. Oracle optionally provides pre-built integration between Oracle MDM Hubs and OBIEE. Prebuilt ETL processes extract information from the Hubs and loads it into the OBI EE Data Warehouse. OBIEE provides a number of Information Dashboards for the Data Steward to monitor the quality of master information. These dashboards ensure the Data Steward has all the information necessary to optimize and improve data quality.
Summary
MDM Data Hubs deliver a single, well defined, accurate, relevant, complete, and consistent view of master data across channels, departments, and geographies. The results for companies who implement MDM solutions are dramatic. Over 5000 companies and organizations are managing billions of master data records with MDM Data Hub solutions. Companies such as Cisco, Telecom Italia, Home Depot, Supermarchés Match, Toyota, and 7/11 are realizing the promise of consolidated, clean, consistent master data feeding their operational and analytical systems. Companies are achieving that elusive goal: a single version of the truth about their business across the enterprise.
All the Best for your Golden Record Expedition
Your Partner and Companion
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