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Enterprise Data Management for Utilities Business Analytics

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Presentation on theme: "Enterprise Data Management for Utilities Business Analytics"— Presentation transcript:


2 Enterprise Data Management for Utilities Business Analytics
Martin Dunlea Senior Director Utilities IBU, Oracle Joe Zhou Chief Technology Officer, Xtensible Solutions


4 Program Agenda Introduction and Overview
Building Data Management Core Competencies Oracle Utility Data Management Solution – A Standards-based Approach for Utility Enterprise Roadmap Q&A

5 Volume, Velocity, Variety, Value
What Does Big Data Mean? What is the key difference in managing this data now? Volume, Velocity, Variety, Value SOCIAL BLOG DEVICES SENSORS But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, there are four key characteristics that define big data:  Volume. Machine-generated data is produced in much larger quantities than non-traditional data. For instance, a single jet engine can generate 10TB of data in 30 minutes. With more than 25,000 airline flights per day, the daily volume of just this single data source runs into the Petabytes. Smart meters and heavy industrial equipment like oil refineries and drilling rigs generate similar data volumes, compounding the problem.  Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day).  Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. As new services are added, new sensors deployed, or new marketing campaigns executed, new data types are needed to capture the resultant information. Value. The economic value of different data varies significantly. Typically there is good information hidden amongst a larger body of non-traditional data; the challenge is identifying what is valuable and then transforming and extracting that data for analysis. To make the most of big data, enterprises must evolve their IT infrastructures to handle these new high-volume, high- velocity, high-variety sources of data and integrate them with the pre-existing enterprise data to be analyzed. These characteristics challenge most utility’s existing architecture, tools, staffing competency and business processes! SMART METER VOLUME VELOCITY VARIETY VALUE

6 Top Performing Companies Use Analytics to Drive Business Performance
However, in utilities … Sources: 53/50% is high when compared to utilities. Other industries are doing a much better job – utilities are only at 13% Source: Oracle Study 2013 – “Utilities and Big Data: Accelerating the Drive to Value”

7 What Can We Do With the Data? Potential Use Cases – Sample List Only
Financial Forecasting Asset Failure Analysis Price Elasticity Risk Analysis Asset Optimization Event Correlation Power Quality Tariff Analysis Predictive Maintenance Load Balancing Outage Analysis Retail Customer Analytics Asset Planning Demand Response Load Forecasting Revenue Protection Fault Analysis Predictive Customer Modeling System Condition Analysis Customer Sentiment Analysis

8 Analytics are Fundamental to Improving and Sustaining Utility Business Performance
Improve… Customer Satisfaction Targeted Interactions Reliability More Effective Monitoring and Proactive Maintenance Operational Efficiency Better Planning and Execution Safety Understanding and Mitigating Hidden Risks PURPOSE: How analytics can help utilities meet their core business objectives/metrics DISCUSSION POINTS: Elaborate on each of the main business objective areas utilities are measuring their performance on and how analytics can help improve performance: Customer Satisfaction: marketing programs (e.g. conservation) to the right customers; personalization (e.g. credit treatment); improve response to customer issues by focusing on the right work Reliability: Taking action on the right assets at the right point in time. Identifying overloaded transformers before they fail by leverage detailed usage and weather data Operational efficiency: reduce costs by prioritizing/optimize work based on business value; increased revenue by identifying theft cases early on Safety: Better understanding of the condition of your assets in field and preparing field employees; detecting gas leaks and other safety hazards before they occur TRANSITION: How do you go about becoming a data driven organization? Takeaway: Use analytics to achieve your business objectives by becoming a more data driven organization. Segmentation-driven marketing offers Proactive alerting Personalized communication Asset management Transformer load management Employee utilization Revenue assurance Optimized field work Reducing public safety hazards Vegetation management Field work management Oracle helps your utility transform into a data driven organization.

9 The New Utility Enterprise Paradigm
Utility Enterprise is faced with the integration of Information Technology (IT), Operational Technology (OT) and Communications Technology (CT) Utility systems are extending into the field devices through ICT to enable much more dynamic and granular operations. The needs to drive interoperability and intelligence (both centrally and distributed) are rising rapidly. Standards are being developed to address the needs across all domains and layers IEC SG3 Smart Grid Architecture Model (SGAM)

10 Enterprise Data Management Framework
Enterprise Vision & Strategy Enterprise Architecture Enterprise Business & IT Core Processes Enterprise Business & IT Organizations Enterprise Infrastructure EDM Vision & Strategy EDM Governance EDM Core Processes EDM Organization EDM Infrastructure Vision Mission Strategy Goals & Objectives Value Propositions Sponsorship Stewardship Policies, Principles & Tenets Alignment Structure Data Quality Data Integrity Data Security & Protection Data Lifecycle Management Data Movement Semantics Management Database Management Master Data Management Information Services Services & Support CSFs & KPIs Structure (Virtual, Hybrid……) Roles & Responsibilities Functional Services Business Value and Relationship Management Information Architecture Blueprint Management Integrated DM Platforms (DBMS, Content Mgmt, ETL, SOA, EII, Data Modeling, BI/DW, Big Data , MDM) Knowledgebase and Repositories Standards & Best Practices

11 What is Enterprise Data Management?
All Forms of Data The system must be able to capture, process, organize, and analyze all forms of data in order to meet existing business requirements and support discovery of new business opportunities. The system must be able to maintain relationships and enable navigation between different forms of data. Common Information & Object Model The system must organize information to provide a single version of truth The system must share analysis artifacts to provide a single version of the question. Governance must be instituted to properly maintain information and analysis artifacts Integrated Analysis Analysis should be integrated into the user interfaces, devices, and processes such that users gain insight where and when they need it. Business analytics systems should be integrated with business processes in a way to automatically leverage the available analysis to optimize operational processes. Insight To Action The system must be able to monitor for important events and initiate alerts. Users must be able to drill down into information in order to perform analysis. Whenever possible, user should be given insight and guided to take proper action. Source: Oracle GTM for Big Data

12 What is Enterprise Analytics?
What happened and why did it happen? Descriptive Analytics --- using historical data to understand the “what” through reporting, scorecard, and clustering, etc. Predictive Analytics --- using current and historical data to predict the future through statistical and/or machine learning techniques. Prescriptive Analytics --- using the results of descriptive and predictive analytics to make suggestions on decision options through optimization and automation. What and when will it happen? Why it will happen and what options to take?

13 The Lifecycle of Enterprise Data Management, BI and Analytics
Source: Oracle Information Architecture: An Architect’s Guide to Big Data.

14 Utility Data Integration, Management and Analytics Landscape
Utility data sources and analytical needs are diverse and require a variety of technologies to work together. This architecture allows utilities to invest where business needs are today and grow as they evolve. Key to this architecture is a layer of common data and information to be interoperable and future proof. BDA – Big Data Appliance ODS – Operation Data Store MDM – Master Data Management EDW – Enterprise Data Warehouse BI – Business Intelligence DM – Data Mart

15 EDM Vision, Strategy & Business Case
Where to Start? EDM Vision, Strategy & Business Case Governance (People, Process and Organization) People ------ Analytics skill gap, culture shift in data sharing Process Control of data movement, quality, and protection Organization Competency Center as a core function of business and IT Technology (Big Data, EDW, Data Models, Master Data, Integration, BI ) Big Data ------ What is appropriate for your needs? Walk before run. BI/EDW EDW Appliance, Models, BI tools consolidation Data Management Master Data Management and data integration

16 An EDM Roadmap Deliver Pilot Strategize Build
Business and IT alignment to manage data and information as assets to the enterprise Agree on core EDM value and capabilities Strategize Architecturally significant use cases Business-driven and IT-enabled approach with future in sight Deliver business value in day one Integrated data management platform technologies Pilot Control your data movement and data integration Manage data fidelity with data model and master data management Standardize BI/DW platform Reengineering data sharing, access and analysis practices Build A service platform both in resources and technologies Balance in central and de-central capabilities through EDMCC Data become assets to be managed Analytics as a function to business processes Deliver

17 Oracle Utilities Data Model (OUDM)
Product Definition: Oracle Utilities Data Model (OUDM) is a pre-built, standards-based data warehouse solution designed and optimized for Oracle database and hardware. OUDM can be used in any applications environment and is easily extensible. OUDM enables utilities to establish a foundation for business intelligence and analytics across the enterprise, allowing each business domain to leverage a common analytics infrastructure and pre-defined cross-domain relationships, driving unprecedented levels of intelligence and discovery. OUDM is a utilities-specific data warehouse solution, based on CIM, the utilities industry standard, and is designed and optimized for Oracle EE edition database (with OLAP, Mining, Partitioning options) and hardware. It provides the foundation for a common analytics infrastructure; a place to combine data from all the various applications and business disciplines within a utility, such as meter data management, customer management, operations and assets, and by doing this in a pre-defined way, as all the relationships are already defined in the model – allowing new and exciting cross-functional analysis and discoveries within a utility.

18 Oracle Utility Data Model - A Solution Framework
Foundation Layer: where multiple source of data are merged and integrated into one version of truth, without consideration of how users will access them. Analytical Layer: where data and information are aggregated into ways to facilitate reporting, ad-hoc queries, and data analysis. Presentation Layer: where the results of reporting and analysis are shown to end users.

19 What is OUDM Really About?
It is based on Oracle Communications Data Model and IEC Common Information Model (CIM) It is a logical model that represents utility common semantics It represents the Oracle BI/DW solution best practices for utility enterprise It builds the integrated data foundation for advanced analytics It focuses on cross application data entities and relationships It can be used to meet master data management challenges It can be used to drive systems interoperability for utility enterprise

20 Unifying Enterprise Capabilities for Utility Integration, Data Management and Analytics – using OUDM
Utility data sources and analytical needs are diverse and require a variety of technologies to work together. This architecture, using OUDM/CIM, allows utilities to invest where business needs are today and grow as they evolve. For utilities that are looking to establish the core competency around data management, Oracle is the right partner.

21 Implementation Roadmap Train, Implement, and Transfer Knowledge
Demonstrate the capability and maturity of the OUDM solution Choose a couple of use cases to show how to use OUDM/CIM to integration and analyze data from multiple sources Use appropriate technologies (ESB, ETL, BI, etc.) to show how these technologies work together to deliver business value Demonstrate Focus on the immediate needs of a utility and deliver tangible results Provide hands on training and knowledge transfer to utility personnel Help establish sustainable methods, tools and infrastructure to meet future demands Implement Provide on-going support to utility team Work with strategic utility partners for the direction of OUDM future releases Deliver more packaged analytics and packaged integration. Expand to other enterprise technologies Sustain 21

22 Summary Why What How Who
Utility industry is faced with tremendous challenges both internally and externally Increased volatility Fusion of IT, CT and OT What Utilities must establish core competencies around data management and analytics in order to be more competitive, efficient and effective Utilities must do so proactively and strategically How Data management core competencies around people, process and technology Manage and use data and information as “assets” Who Business and IT must partner together to build the core competencies Engage strategic partners and leverage best practices and standards

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