4 Program Agenda Introduction and Overview Building Data Management Core CompetenciesOracle Utility Data Management Solution – A Standards-based Approach for Utility EnterpriseRoadmapQ&A
5 Volume, Velocity, Variety, Value What Does Big Data Mean?What is the key difference in managing this data now?Volume, Velocity, Variety, ValueSOCIALBLOGDEVICESSENSORSBut 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 METERVOLUMEVELOCITYVARIETYVALUE
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 FinancialForecastingAsset FailureAnalysisPrice ElasticityRisk AnalysisAssetOptimizationEvent CorrelationPower QualityTariff AnalysisPredictive MaintenanceLoad BalancingOutage AnalysisRetail Customer AnalyticsAsset PlanningDemand ResponseLoad ForecastingRevenue ProtectionFault AnalysisPredictive CustomerModelingSystem ConditionAnalysisCustomer SentimentAnalysis
8 Analytics are Fundamental to Improving and Sustaining Utility Business Performance Improve…Customer SatisfactionTargeted InteractionsReliabilityMore Effective Monitoring and Proactive MaintenanceOperational EfficiencyBetter Planning and ExecutionSafetyUnderstanding and Mitigating Hidden RisksPURPOSE:How analytics can help utilities meet their core business objectives/metricsDISCUSSION 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 workReliability: Taking action on the right assets at the right point in time. Identifying overloaded transformers before they fail by leverage detailed usage and weather dataOperational efficiency: reduce costs by prioritizing/optimize work based on business value; increased revenue by identifying theft cases early onSafety: Better understanding of the condition of your assets in field and preparing field employees; detecting gas leaks and other safety hazards before they occurTRANSITION: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 offersProactive alertingPersonalized communicationAsset managementTransformer load managementEmployee utilizationRevenue assuranceOptimized field workReducing public safety hazardsVegetation managementField work managementOracle 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 layersIEC SG3 Smart Grid Architecture Model (SGAM)
10 Enterprise Data Management Framework Enterprise Vision & StrategyEnterprise ArchitectureEnterprise Business & IT Core ProcessesEnterprise Business & IT OrganizationsEnterprise InfrastructureEDM Vision & StrategyEDM GovernanceEDM Core ProcessesEDM OrganizationEDM InfrastructureVisionMissionStrategyGoals & ObjectivesValue PropositionsSponsorshipStewardshipPolicies, Principles & TenetsAlignmentStructureData QualityData IntegrityData Security & ProtectionData Lifecycle ManagementData MovementSemantics ManagementDatabase ManagementMaster Data ManagementInformation ServicesServices & SupportCSFs & KPIsStructure(Virtual, Hybrid……)Roles & ResponsibilitiesFunctional ServicesBusiness Value and Relationship ManagementInformation Architecture Blueprint ManagementIntegrated DM Platforms(DBMS, Content Mgmt, ETL, SOA, EII, Data Modeling, BI/DW, Big Data , MDM)Knowledgebase and RepositoriesStandards & Best Practices
11 What is Enterprise Data Management? All Forms of DataThe 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 ModelThe system must organize information to provide a single version of truthThe system must share analysis artifacts to provide a single version of the question.Governance must be instituted to properly maintain information and analysis artifactsIntegratedAnalysisAnalysis 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.InsightTo ActionThe 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 ApplianceODS – Operation Data StoreMDM – Master Data ManagementEDW – Enterprise Data WarehouseBI – Business IntelligenceDM – Data Mart
15 EDM Vision, Strategy & Business Case Where to Start?EDM Vision, Strategy & Business CaseGovernance(People, Process and Organization)People------Analytics skill gap, culture shift in data sharingProcessControl of data movement, quality, and protectionOrganizationCompetencyCenter as a core function of business and ITTechnology(Big Data, EDW,Data Models, Master Data, Integration, BI )Big Data------What is appropriate for your needs? Walk before run.BI/EDWEDW Appliance,Models,BI tools consolidationData ManagementMaster 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 enterpriseAgree on core EDM value and capabilitiesStrategizeArchitecturally significant use casesBusiness-driven and IT-enabled approach with future in sightDeliver business value in day oneIntegrated data management platform technologiesPilotControl your data movement and data integrationManage data fidelity with data model and master data managementStandardize BI/DW platformReengineering data sharing, access and analysis practicesBuildA service platform both in resources and technologiesBalance in central and de-central capabilities through EDMCCData become assets to be managedAnalytics as a function to business processesDeliver
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 semanticsIt represents the Oracle BI/DW solution best practices for utility enterpriseIt builds the integrated data foundation for advanced analyticsIt focuses on cross application data entities and relationshipsIt can be used to meet master data management challengesIt 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 solutionChoose a couple of use cases to show how to use OUDM/CIM to integration and analyze data from multiple sourcesUse appropriate technologies (ESB, ETL, BI, etc.) to show how these technologies work together to deliver business valueDemonstrateFocus on the immediate needs of a utility and deliver tangible resultsProvide hands on training and knowledge transfer to utility personnelHelp establish sustainable methods, tools and infrastructure to meet future demandsImplementProvide on-going support to utility teamWork with strategic utility partners for the direction of OUDM future releasesDeliver more packaged analytics and packaged integration.Expand to other enterprise technologiesSustain21
22 Summary Why What How Who Utility industry is faced with tremendous challenges both internally and externallyIncreased volatilityFusion of IT, CT and OTWhatUtilities must establish core competencies around data management and analytics in order to be more competitive, efficient and effectiveUtilities must do so proactively and strategicallyHowData management core competencies around people, process and technologyManage and use data and information as “assets”WhoBusiness and IT must partner together to build the core competenciesEngage strategic partners and leverage best practices and standards
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