![](https://csdnimg.cn/release/download_crawler_static/89029270/bg1.jpg)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |
电信领域数据管控的最佳实践
![](https://csdnimg.cn/release/download_crawler_static/89029270/bg2.jpg)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |
"THE FOLLOWING IS INTENDED TO OUTLINE OUR GENERAL PRODUCT DIRECTION. IT IS
INTENDED FOR INFORMATION PURPOSES ONLY, AND MAY NOT BE INCORPORATED
INTO ANY CONTRACT. IT IS NOT A COMMITMENT TO DELIVER ANY MATERIAL, CODE,
OR FUNCTIONALITY, AND SHOULD NOT BE RELIED UPON IN MAKING PURCHASING
DECISION. THE DEVELOPMENT, RELEASE, AND TIMING OF ANY FEATURES OR
FUNCTIONALITY DESCRIBED FOR ORACLE'S PRODUCTS REMAINS AT THE SOLE
DISCRETION OF ORACLE."
2
![](https://csdnimg.cn/release/download_crawler_static/89029270/bg3.jpg)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |
混合云架构(公有、私有)
集中化管理
资源最优化
强化支撑力
混合架构下信息化面临的巨大挑战
分布式数据平台(Hadoop、NoSQL)
松散、多样
灵活的模式
开发为中心
OLAP/OLTP 数据库
高密度信息
固定的结构
管理为中心
数据如
何管理
???
3
1. 随着各业务流程的丰富和复杂化,必然会造成大量数据的重复
提取,重复建设,并衍生各类数据质量问题;
2. 各流程间的数据无法相互关联,制约了未来各流程的业务融
合,限制了业务分析能力的发展。
![](https://csdnimg.cn/release/download_crawler_static/89029270/bg4.jpg)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |
什么是 数据管控 ?
• 定义:数据管控其实是一种体系,是一个关注于信息系统执行层面
的体系,这一体系的目的是整合IT与业务部门的知识和意见,通过将
流程、策略、标准、技术和组织的有效组合,对企业的信息化建设
进行全方位的监管,需要企业高层的授权和业务部门与IT部门的密切
协作。
• 目标:保证数据的时效性、合法性、完整性、一致性、可审计和安
全性
Governance 含有梳理之意,将纷杂变得顺畅
4
![](https://csdnimg.cn/release/download_crawler_static/89029270/bg5.jpg)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |
数据管控的主要驱动力及关键技术
Survey by: International Association for Information and Data Quality(IAIDQ)
5
4.30%
5.90%
5.90%
13.40%
18.70%
20.30%
25.70%
26.90%
39.00%
44.40%
45.50%
48.70%
48.70%
57.20%
66.30%
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00%
Rules discovery tools
Other
Product Information Management (PIM) tools
Customer data integration (CDI) tools
Master data mangement (MDM) tools
Business rules engines
Workflow tools
Data relationship discovery and mapping
Data remediation/cleansing tools
Metadata repositoty
Data quality monitoring
Data matching and reconcoliation (data de-duplication)
Data modeling (computer-aided software engineering)
Extract-transform-load (ETL) and other data integration tools
Data quality analysis, assessment or profiling