IBM数据治理流程：通往成功的捷径

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"IBM数据治理流程" 数据治理是企业管理和利用数据的核心，特别是在主数据管理(MDM)项目中，它的重要性不言而喻。随着2010至2011年间大型企业的日益重视，他们明确指出没有预先建立的数据治理框架，MDM项目将无法获得资金支持。这一变化揭示了数据治理对于企业数据整合计划的关键作用。 "实现治理，尽早行动"成为企业和解决方案架构师的战略目标，因为他们需要确定数据整合项目的范围和策略。这一过程充满了挑战，包括理解数据的广度、多样性以及整合难题。数据治理旨在定义和实施一套有效的规则和流程，确保数据的质量、安全、一致性和合规性。 Sunil Soares的《IBM数据治理统一过程》为解决方案架构师、数据治理主管以及MDM项目经理提供了宝贵的指导。这本书包含了最佳实践、加速器和模型，帮助读者快速掌握数据治理的核心要素。通过这些资源，业务和IT领导者能够更好地理解和应对数据治理的各种选择，从而推动"实现治理，尽早行动"的策略。数据治理统一过程涵盖了多个关键领域，包括： 1. **数据战略规划**：定义数据治理的目标、策略和路线图，与企业的整体业务战略保持一致。 2. **组织结构**：建立跨部门的数据治理团队，明确职责和角色，确保所有利益相关者参与其中。 3. **政策制定**：创建和维护数据管理政策，确保数据使用符合法规要求和企业标准。 4. **元数据管理**：识别、记录和管理数据的描述信息，以便理解和使用数据。 5. **数据质量**：建立数据质量评估机制，监控和改进数据的准确性、完整性、一致性和时效性。 6. **流程治理**：定义和优化数据生命周期管理，包括数据的创建、存储、处理、分发和销毁。 7. **风险管理**：识别和管理与数据相关的风险，如数据泄露、合规性问题和业务中断。 8. **工具和技术**：选择和实施适当的数据治理工具，自动化某些流程并提高效率。 9. **培训和意识**：为员工提供数据治理培训，增强数据敏感性和正确使用数据的意识。 10. **持续改进**：定期评估数据治理的效果，根据反馈进行调整和完善。通过应用这些实践，企业不仅可以确保MDM项目的成功，还能构建一个高效、合规且可信赖的数据环境，进而支持业务决策、提升运营效率和竞争优势。《IBM数据治理统一过程》为实现这一目标提供了有力的支持。

This kind of normal, everyday Data Governance problem besets every

business. I’ve seen a lot worse. Most people just call them mistakes, but they can

lead to lost business, increased risk, and certainly extra cost. Whether you have a

formal Data Governance program or not, your organization has Data Governance

problems like these, and many others as well. You know it, and your customers

know it.

Once you recognize this problem, the choice is pretty simple: you can either

deal with it or ignore it. Since you are reading this book, you’ve decided not to

ignore it. Good.

Your next decision is how to deal with it. Mistakes are a part of life. Your

business makes them because people run your business. The data didn’t get

wrong on its own. You need to change the way the people who run your business

think about data, what they do with it, and how they build businesses that use

data in the ﬁ rst place. To do that, you need a system, a Data Governance program

that helps bring people together, to coordinate, collaborate, and communicate.

This book has some important tools to get you started the right way toward

building a Data Governance program, which can ﬁ x the simple, and complex,

errors and omissions your organization makes every day.

You already made the most important decision, in buying this book. Now

ﬁ nish the book and start your program, because time is not on your side. In the

few minutes it took you to read this, someone somewhere in your organization

has dropped a few forms, miscoded a new account, or sent duplicate bills to a

customer.

The clock is ticking. . . .

Steve Adler

Chairman,

IBM Data Governance Council

The IBM Data Governance Uniﬁ ed Process

-2-

data is located. This can pose challenges, especially in the case of personally

identiﬁ able information (PII). Organizations need to secure business-critical

data within their ﬁ nancial, Enterprise Resource Planning, and human resource

applications from unauthorized changes, since this can affect the integrity of

their ﬁ nancial reporting, as well as the quality and reliability of daily business

decisions. They must also protect sensitive customer information such as credit

card numbers and PII data, as well as intellectual property such as customer

lists, product designs, and proprietary algorithms from both internal and external

threats. Finally, organizations need to get the maximum value out of their data,

driving initiatives such as improved risk management and customer-centricity.

Data is at once an organization’s greatest source of value and its greatest

source of risk. Poor data management often means poor business decisions and

greater exposure to compliance violations and theft. For example, regulations

such as Sarbanes-Oxley in the United States, the equivalent European Sarbanes-

Oxley, and the Japanese Financial Instruments and Exchange Law (J-SOX)

dictate a balance between restricted access and the appropriate use of data, as

mandated by rules, policies, and regulations. On the other hand, the ability to

leverage clean, trusted data can help organizations provide better service, drive

customer loyalty, spend less effort complying with regulations and reporting, and

increase innovation.

Organizations must also consider the business value of their unstructured data.

This unstructured data, often referred to as content, needs to be governed just as

structured data does.

A good example of unstructured data governance is setting records

management policy. Many companies are required to maintain electronic and

paper records for a given period of time. They need to produce these records

quickly and cost-effectively during the legal discovery process. They also need to

be in compliance with the established retention schedules for speciﬁ c document

types. Several organizations use the term “Information Governance” to deﬁ ne this

program. Although we use the terms “data” and “information” interchangeably,

we will stick with the more commonly used term “Data Governance” throughout

this book.

Here are some beneﬁ ts that organizations can derive by governing their data:

Improve the level of trust that users have in reports

●

Ensure consistency of data across multiple reports from different parts of

●

the organization

The IBM Data Governance Uniﬁ ed Process

-4-

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