Oracle主数据管理：企业解决方案与关键架构

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Oracle主数据管理技术方案是一份2007年的白皮书，深入探讨了在企业环境中如何有效地管理和优化主数据的过程。主数据管理（Master Data Management，MDM）是企业级数据管理的关键组成部分，它关注于确保组织内部对关键实体如客户、供应商、产品等的一致性和准确性。该方案提供了以下关键知识点： 1. **主数据管理概述**：主数据管理旨在解决企业数据的混乱和冗余问题，通过集中管理核心数据，提高数据质量，支持业务决策和跨系统一致性。 2. **企业数据分类**：分为事务性数据（Transactional Data）、操作型MDM（Operational MDM）和分析型数据（Analytical MDM）。事务性数据处理日常业务活动，操作型MDM则侧重实时更新和同步，而分析型数据用于支持深度分析和报告。 3. **信息架构**：强调企业应用程序集成（EAI）和服务导向架构（SOA），确保不同系统间的无缝通信。数据质量问题被着重指出，因为这直接影响到数据价值的实现。 4. **数据质量管理与标准化**：在Oracle Fusion Architecture中，数据质量服务和标准化服务是关键组件，它们通过数据清洗、转换和标准化来提升数据一致性。 5. **Oracle Fusion Middleware**：作为实施MDM的基础平台，Oracle Fusion Middleware提供了一系列服务，如应用集成服务、业务流程编排服务、数据质量及元数据管理，以及业务规则引擎和业务事件服务。 6. **身份管理与Web服务管理**：身份管理确保数据安全，而Web服务管理则促进了异构系统间的交互。 7. **MDM过程**：包括主数据的整合、治理、共享和利用四个阶段。整合是从各个源系统收集数据，治理则是确保数据质量和遵守法规要求，共享使数据在整个组织内可用，而利用则是通过数据分析驱动业务决策。 8. **Oracle MDM高级架构**：展示了如何通过Oracle提供的工具和技术实现全面的MDM解决方案，涵盖了数据整合、元数据管理以及与业务流程和分析系统的紧密集成。这份方案为Oracle用户提供了实施主数据管理的具体步骤和工具支持，帮助企业优化数据资产，提升数据驱动决策的能力。

Master Data Management

Types of Data in the Enterprise

Enterprise

Data

• Describes an Enterprise’s

Performance

Analytical

Data

• Describes an Enterprise’s

Business Entities

Master

Data

Transactional

Data

• Describes an Enterprise’s

Operational State

As data is moved and manipulated, information about where it came from, what

changes it went through, etc. represents a fourth kind of enterprise data called

metadata (data about the data). Though not a prime focus of this paper, the key

role metadata plays in the broader Enterprise Information Management (EIM)

space and how it relates directly to MDM is described.

Transactional Data

A company’s operations are supported by applications that automate key business

processes. These include areas such as sales, service, order management,

manufacturing, purchasing, billing, accounts receivable and accounts payable.

These applications require significant amounts of data to function correctly. This

includes data about the objects that are involved in transactions, as well as the

transaction data itself. For example, when a customer buys a product, the

transaction is managed by a sales application. The objects of the transaction are

the Customer and the Product. The transactional data is the time, place, price,

discount, payment methods, etc. used at the point of sale. The transactional data is

stored in OnLine Transaction Processing (OLTP) tables that are designed to

support high volume low latency access and update.

Operational MDM

Solutions that focus on managing transactional data under operational applications

are called Operational MDM. They relay heavily on integration technologies. They

bring real value to the enterprise, but lack the ability to influence reporting and

analytics.

Analytical Data

Analytical data is used to support the company’s decision making. Customer

buying patterns are analyzed to identify churn, profitability and marketing

segmentation. Suppliers are categorized, based on performance characteristics over

time, for better supply chain decisions. Product behavior is scrutinized over long

periods to identify failure patterns. This data is stored in large Data Warehouses

and possibly smaller data marts with table structures designed to support heavy

aggregation, ad hoc queries, and data mining. Typically the data is stored in large

fact tables surrounded by key dimensions such as customer, product, account,

location, and time.

Master Data Management

Analytical MDM

Solutions that focus on managing analytical data are called Analytical MDM. They

relay heavily on data warehousing and BI technologies. They also bring real value

to the enterprise, but lack the ability to influence operational systems. Any data

cleansing done inside an Analytical MDM solution is invisible to the transactional

applications and transactional applications knowledge is not available to the

cleansing process.

Master Data

Master Data represents the business objects that are shared across more than one

transactional application. This data represents the business objects around which

the transactions are executed. This data also represents the key dimensions around

which analytics are done. Master data creates a single version of the truth about

these objects across the operational IT landscape.

An MDM solution should to be able to manage all master data objects. These

usually include Customer, Supplier, Asset and Product. But other objects such as

Invoices, Campaigns, or Service Requests can also cross applications and need

consolidation, standardization, cleansing, and distribution. Different industries will

have additional objects that are critical to the smooth functioning of the business.

It is also important to note that since MDM supports transactional applications, it

must support high volume transaction rates. Therefore, Master Data must reside

in data models designed for OLTP environments.

Enterprise MDM

Maximum business value comes from managing both transactional and analytical

master data. These solutions are called Enterprise MDM. Operational data

cleansing improves the operational efficiencies of the applications themselves and

the business process that use these applications. The resultant dimensions for

analytical analysis are true representations of how the business is actually running.

What’s more, the insights realized through analytical processes are made available

to the operational side of the business.

Oracle, with its recent acquisition of Hyperion, provides the most comprehensive

Enterprise MDM solution on the market today. The following sections will

illustrate how this combination of operations and analytics is achieved.

INFORMATION ARCHITECTURE

The best way to understand the role that MDM plays in an enterprise is to

understand the typical IT landscape. The best place to start is with the

applications. Almost all companies have a heterogeneous set of applications. Some

are home grown, others are bought from vendors, and still others are inherited

during corporate mergers and acquisitions.

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