Sensor data: images
New RFID Sources
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对新式基础架构的需求
Systems of Engagement （SoE）
Big Data &
Analytics
对的决策 对的地方 对的时间点
System of Record （SoR）
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▪ 在可靠和安全的环境中处理关键业务
对整个企业的结构化和非结
构化数据t做全局导览
IBM Watson FoundaNtaiovignastion，从中发现
All Data
Discover价值
New/Enhanced Applications
实时收集、存储并分析数据， 历史用电状态数据的复杂
最快可达 50,000 data
查询处理
Meters
Discovery and exploration
What action should I take?
Decision management
What did I learn, what’s best?
Cognitive
Why did it happen?
Reporting and analysis
What could happen?
Fraud / theft protection
Call Centers
Outage Mgmt
Billing systems
案例: Smart Metering智慧电力计费 大数据分析应用可以带来 真正的业务价值
Grid Operations 电网管理
Field Service 外勤现场服务
减少停电次数和时间
Hadoop and
Streams
Multimedia
Systems of Insight
Enterprise Integration and Context Accumulation
Web Logs
Unstructured Exploratory
Dynamic
Social Data
Text Data: emails
Predictive analytics and
modeling
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Systems Security Storage
On premise, Cloud, As a service
IBM Big Data & Analytics Infrastructure
Customer selfserve portals
应用
▪ 存取和处理海量数据——包括结构化
和非结构化数据
▪ 速度及时响应随时可能出现的商业机
会，这就需要灵活、实时性的基础架 构
▪ The dynamics of SoR and SoE:
– 通过负载及资源部署的优化，来增强 灵活性和效益
– 通过采用包括基于开放标准的技术等 新技术来改善IT economics
从企业数据向大数据的扩展
具备洞悉能力的系统
Systems of Insight
Traditional Approach
New Approach
Structured, analytical, logical
Creative, holistic thought,
Systems of Record
intuition
Operational data zone
Landing, Exploration and Archive data zone
Deep Analytics data zone
EDW and data mart
zone
Information Integration & Governance
What is happening?
zone
What action should I take?
Decision management
分时时段电价的实时定价 或
提供及时的需求/响应服务
Information Integration & Governance
What did I learn, what’s best?
Cognitive
Why did it happen?
大数据分析的新型架构解决方案
All Data
IBM Watson Foundations
Data Zone
Байду номын сангаас
Application Zone
New/Enhanced Applications
Meters ERP Location Customers Grid
Real-time Data Processing & Analytics
ERP
Deep
protection
Location Customers
Grid
Operational data zone
Landing, Exploration
and Archive
data zone
关系掌控
构建和维护电网的唯 一试图
Analytics data zone
EDW and data mart
pointRs/esael-ctime Data Processing & Analytics
Customer self-
What is 分析用户用电情况，侦serve portals happening? 测偷电、改表等行为
Discovery and
exploration
Fraud / theft
Systems Of Engagement
Data Warehouse
Transaction Data
Internal App
Data Structured
Repeatable
Mainframe Data
Linear
OLTP System Data
ERP data
Traditional Sources
及时发现能源损耗问题、 以及偷电和欺诈行为
电网运维优化
Smart Metering
Resource Planning 资源规划
Customer Service / Customer Operations
电量使用预测更为精确 提高客户满意度
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法规遵从
实现真正的有效的 法规遵从
案例: 用大数据分析来加强 Smart Metering
Reporting and analysis
Call Centers
What could happen?
Predictive analytics and
modeling
Outage Mgmt
Billing
预测哪些用户适合于sy哪ste些ms分 时时段电价或需求/响应服务