Transformation of intelligent utilities with analytics, big data, and internet of things

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1 Trasformatio of itelliget utilities with aalytics, big data, ad iteret of thigs Subtitle; Helvetica Neue LT Std 75 Bold; 12pt/17; 2 lies maximum

2 Trasitio to itelliget utilities The traditioal utility busiess model of geeratig ad distributig eergy was relatively simple: utility compaies would oly collect very elemetary data relatig to the levels of cosumptio o customer premises. Customer egagemet oly happeed through oe way commuicatio of eergy bills.with the digitizatio of the traditioal grids, featurig advaced meterig ifrastructure (AMI) systems ad itelliget supervisory cotrol ad data acquisitio (SCADA) systems, came expoetial icreases i the data volumes. The additio of such smart ifrastructure to the grid has led to progressio from traditioal grid with uidirectioal data flow to the level of beig a iformed grid with eormous real-time data. Smart grid data growth resultig from itelliget sesors o the grid, further itegrated with structured ad ustructured data from social media ad other third party data sources ulocks the potetial to gai additioal isights for utilities. The ew data sources are creatig a utility Iteret of Thigs (IoT) where utility assets are itegrated with exteral applicatios ad data. These busiess isights are derived through a combiatio of busiess aalytics techiques such as data reservoirs, data acquisitio ad predictive ad prescriptive aalytics, eablig utilities to trasitio from a iformed grid to a itelliget grid. Further icreasig expectatios of digital-savvy customers due to social etworks, price compariso websites, ad olie reviews coupled with the additioal data geerated by the deploymet of smart techologies like smart meter, smart Home, smart Grid, smart City ad others have chaged the customer / utility relatioship. This has created ew Trasitio from traditioal grid to itelliget grid Traditioal Grid Traditioal grid with oe-way commuicatio ad mothly meter readigs Iformed Grid Smart Meterig ad Smart Grid deploymet for real-time moitorig Itelliget Grid Automated operatioal real-time decisio makig usig aalytics A explosio i data May PBs of data every day 25+TBs of log data every day 12+TBs of tweet data every day 30billio RFID tags today 1.3B I billio camera phoes world wide 100s of millios of GPS eabled devices sold aually 2+ billio people o the web by ed millio smart meters i m by % of world s date is ustructured 2 Trasformatio of itelliget utilities with aalytics, big data, ad iteret of thigs

3 digital trasformatio opportuities for utilities to adopt i order to leverage iformatio for competitive advatage, geerate ew reveue streams, ad break away from the covetioal model of sellig eergy as commodity to eergy as service. Capturig customercetric opportuities requires the ability to deal with a iflux of iformatio separatig the oise from real isights that ca help to icrease customer satisfactio, which is becomig oe of the key drivers of digital trasformatio. I the past, the techology to capture, itegrate, ad aalyze data was either ot available or ot costeffective for utilities to adopt. The evolutio of big data ad cloud solutios are eablig utilities to trasitio to itelliget eterprises by leveragig ew isights. The key drivers which are pushig utilities to adopt data maagemet & aalytics: Chagig busiess models with more itegrated busiess processes at eterprise level requirig detailed uderstadig of cross fuctioal impact for decisio makig. Need to prepare to deal with expoetial icrease i data from various sources with digitalizatio of grid. Ivestmets i aalytics (actig as the soft ivestmet required to realize operatioal efficiecy from hard ivestmet ) beig put i for grid moderizatio by tappig demad-side maagemet opportuities. Icreasig complexity of maagig both hard ad soft assets (firmware) with moderizatio of grid. Real-time decisio makig for the grid operatios to maitai grid disciplie with the icreasig complexity of grid due to itegratio of EVs ad distributed the geeratio sources. Icreasig market of available ad affordable techologies for data maagemet ad aalytics solutios. Icreasig expectatios of digital-savvy customers due to social etworks, price compariso websites, ad olie reviews require gettig real isights for utilities to capture customer cetric opportuities. 3

4 Evolutio from BI to Eterprise Aalytics Most utilities use traditioal busiess itelligece (BI) reportig solutios such as data warehousig, dashboards, reports ad descriptive aalytics. These solutios are implemeted to meet regulatory requiremets, esure accurate billig ad to improve operatioal efficiecy. However, digitizatio of the grid, or utility IoT, requires a evolutio i data maagemet maturity to support the ew data beig collected resultig i a huger for additioal aalytics capabilities. For example, five millio smart meters sedig updates every 15 miutes result i early 16 terabytes of iformatio a year, a 3,000- fold icrease i the amout of data. Meter data provides a sapshot of total cosumptio i a home or buildig, but coupled with cosumptio captured by i-home devices, adds additioal isights as well as more complexity to the solutio. The additioal data volume ad speed to capture data exceeds the ability of traditioal solutios, resultig i ew requiremets for storage, itegratio ad aalytics. The ability to maage ad aalyze petabytes of data allows utilities to deal with clusters of iformatio that could have a impact o the busiess. This requires aalytical egies that ca maage this highly distributed data ad provide results that ca be optimized to solve a busiess problem. Usig big data techology, slicig ad dicig of data collected from the grid ad from customers, results i ew isights for utilities leadig to operatioal efficiecies, ew products, ew customer programs, ad fresh reveue streams. I order to take advatage of aalytics, it is importat to visualize aalytics across the utility value chai as opposed to silos. The diagram below depicts the key fuctios of utilities i trasmissio & distributio, ad retail busiess with a large opportuity for aalytics to improve the decisio makig process. Grid Aalytics: Grid Aalytics determie isights from real-time moitorig of the grid usig grid sesors ad advaced meterig ifrastructure (AMI), while automatig the situatio aalysis to achieve better operatig efficiecies i order to aticipate ad avoid service iterruptios ad Utilities Aalytics Busiess Area Eterprise Asset Maagemet Fiacials Huma Resource Maagemet Compliaces Maagemet Regulatory Reportig Other Corporate Services Asset Maagemet Grid/Network Operatios Customer Egagemet Asset usage Aggregatio Aalysis Visualizatio of Asset Coditios Outage Maagemet Impact Distributio System Plaig Power Quality Aalytics Customer Segmetatio Campaig Maagemet Cross/Up Sellig opportuities Predetive Asset Maiteace Asset Plaig Distributed Geeratio Availability capacity Meter readig performace Remote Meter Operatios Social Media Aalytist Demad Maagemet Programs Subscriptio Chur maagemet Support Services Customer Operatios Reveue Protectio Meter data accuracy Call Ceter Operatios Customer Commuicatos Eergy Efficiecy Usage ad Cosumptio Eterprise Aalytics Grid Aalytics Customer Aalysis 4 Trasformatio of itelliget utilities with aalytics, big data, ad iteret of thigs

5 other operatig issues. Most grid algorithms were already i place i their rudimetary form but are ow sigificatly improved with real-time ad better quality data supported by big data techology. Customer Aalytics: New cosumer opportuities are emergig with the icreased adoptio of smart techology, mobile devices ad the itegratio of social media, coupled with ew digital-savvy cosumers lookig for more value ad prefereces. Customer isights from customer-related data provides opportuities i areas of reveue protectio, improved load forecastig, ad detailed customer segmetatio leadig to icreased erollmets i demad side maagemet programs. Eterprise Aalytics: Eterprise aalytics provides greater visibility ad isights from the utility value chai, from supply chai ad eergy procuremet to cosumptio icludig huma resources, project maagemet ad fiace. Utilities empowered with these isights are better positioed to uderstad how chages i oe part of the value chai impact others. The Big Data Problem: Utilities do t have a big data problem they have a big problem with data I order to realize the beefits of the ew isights, may utilities recogize that they do t have a coheret data maagemet strategy that icludes data goverace, data quality, a cetralized fudig vehicle for aalytics, ad a master data maagemet approach. Furthermore, busiess users ad data scietists rely o IT to test their hypotheses ad have o self-service tools. Leveragig aalytics for competitive advatage ad geeratig additioal reveue ca be achieved by uderstadig the phases of a big data jourey: Iitial phase, some ivestmets ad proof-of-cocept, ad cetralized ivestmets. The followig list of sigificat barriers to adoptio of aalytics i utilities should be cosidered: Limitatios of ROI models: I utilities that are ot decoupled, aalytics ca lead to decreased reveue by ecouragig eergy efficiecy resultig i coflict of ivestmet priciple. Regulatory Requiremets: May also pose a challege where ivestmets are focused o specific goals such as profitability or customer egagemet, as opposed to safety ad reliability. Scarce skill set: O the busiess side, data scietists who uderstad the busiess to create use cases as well as big data techology resources i IT, are both scarce. Siloed Structure: Maily betwee grid operatios ad busiess operatios, these pose a challege for adoptig aalytics which requires eterprisewide iformatio sharig. Techological complexity: Utilities are yet to get a firm grip over the complex techical ladscape with smart grid related techologies before addig a big data ifrastructure for which utilities have limited expertise. Data Acquisitio: Data acquisitio i utilities cotiues to be a complex task, which poses a sigificat barrier due to: hudreds of data silos, thousads of data files, tes of thousads of Excel workbooks o data ivetory, metadata, busiess glossary poor quality, ureliable, ofte late data ofte ot itegrated or correlated data existig data acquisitio processes still very maual ad labor itesive Where to Begi? Most of the utilities are aware of beefits they could gai from aalyzig the ewly-available data from the grid ad its customers, but may ot have a idea where to begi. A busiess user may ivest i a solutio to address their use case but this may ot be able to be leveraged by aother orgaizatio. There may be several alteratives to begi the aalytics jourey based o differet value propositios of aalytics depedig o IT structure, age of etwork ifrastructure, supportig systems ad uique challeges for which utilities eed specific isights to address. That said, if a utility uderstads where they are i the aalytics jourey, they are better eabled to complete the jourey. Utilities are facig the challege to traslate the large volumes of ew data ito true, actioable itelligece, ad to leverage the iformatio gaied from aalysis to make decisios resultig i improved busiess performace, service reliability ad customer 5

6 Phase Characteristics Activities Begier Have heard of big data Uderstad that there is a eed for aalytics AMI or Smart Grid program Big Data strategy Program lauch / fudig Oe or several POC s Leverage cloud Itermediate Big data strategy Some ivestmet or POC Decetralized aalytics orgaizatio Validate strategy Validate aalytics program Techology solutio Goverace Advaced Ivestmet i big data techology May have orgaizatio May have goverace Validate ivestmet Operatios relatioships. The value is there for those willig to start the jourey ad build the aalytics capabilities as they progress through each phase. Utilities eed collaboratio betwee the busiess ad IT stakeholders to create a comprehesive roadmap focused o aalytics with clearly defied objectives ad achievable milestoes by followig the below steps: Idetifyig ad prioritizig busiess iitiatives: The first step i defiig a comprehesive roadmap is to idetify the use cases ad related KPIs of respective busiess areas to provide busiess value that is i sych with the overall objective. Departmets have differet priorities whe it comes to harvestig data, so IT may have differet priorities tha distributio. Utilities should prioritize them i order of their importace to be drive for improvemets, cosiderig the uique set of challeges for the utility. Defie aalytics road map: Combie use cases ad techical requiremets ito a roadmap ad prioritize based o ROI, leadig practices, ease of implemetatio, impact o busiess processes, ad ivestmets i IT systems. Assemble these use cases ad alig fudig vehicles ito the roadmap classifyig the opportuities alog short-, mid- ad log-term horizos. Defiig Eterprise Iformatio Model: A Eterprise Iformatio Model is the foudatio for the aalytics solutio described i the data strategy. This icludes defiig the data elemets to be icluded, aligig with the busiess model, ad idetifyig data sources (iteral ad exteral; structured ad ustructured; static ad dyamic), access methods, data availability frequecy ad timelies, metrics, dimesioality ad istrumetatio. I some cases, a data reservoir may be selected before loadig the data to the data warehouse. This provides the ability to test hypotheses before loadig the data to the data warehouse ad may result i a update to the data model. As-Is Study ad Gap Aalysis: Evaluate the existig orgaizatioal data maagemet ad Utilities Aalytics Capabilities Jourey Idetifyig ad prioritizig busiess iitiatives Defiig Eterprise Iformatio Model As-Is Study ad Gap aalysis of existig capabilities Data aalytics solutio desig Defiig Aalytics roadmap ad start off data aalytics jourey 6 Trasformatio of itelliget utilities with aalytics, big data, ad iteret of thigs

7 Utilities Aalytics Eterprise Roadmap Eterprise Aalytics Customer Aalytics Y1 H1 H2 H1 H2 Meter Readig Performace Outage Maagemet Impact Remote Meter Operatios Customer Segmetatio Usage Aggregatio Aalysis Power Quality Visualizatio of Asset Coditio Y2 Distributed Geeratio Availability/Capacity Predictive Asset Maiteace Asset Plaig Demad Maagemet Subscriptio Targeted Marketig Other Corporate Services Campaig Social Media Aalytics Maagemet Customer Commuicatios Eergy Efficiecy Chur Maagemet Eterprise Asset Maagemet Meter data accuracy Call Ceter Operatio Exceptio Hadlig Reveue Protectio Usage ad Cosumptio Fiacials Regulatory Reportig Y3 Compliace Maagemet Huma Resource Maagemet Customer Aalytics Eterprise Aalytics techical capabilities to avoid risk of creatig straded costs from havig to rework data stores, or possibly ivestig i a solutio that does ot alig with the data maagemet requiremets. Measurig the gap betwee existig ad desired future-state skills to achieve busiess objectives forms the basis for developig the appropriate aalytics solutio. Data aalytics solutio desig: Develop a solutio to support the use cases ad techical requiremets such as flexibility, scalability, data availability, quality ad itegratio. This may require buildig out ecessary architecture addressig data stagig area, data maagemet platform, master data maagemet capabilities, busiess itelligece ad advaced aalytics platform. Utilities have a optio to develop the solutio i-house, ivest i egieered systems, ivest i a exteral solutio, or a hybrid approach. Coclusio Utilities may have differet catalysts to embark o a big data jourey but developig a roadmap is a key activity to sharpe the executive visio ad ispire the orgaizatio to adopt a ew aalytics visio. Oce the jourey has started, each step will achor back to beefits of additioal isights to improve decisios ad actios. Creatig these isights does ot complete the jourey they eed to be moitored i order to fie tue aalytical models, busiess processes ad applicatios. Regulatory requiremets, techology, weather, demographics ad other iflueces are cotiuously evolvig, which meas the utility eviromet is also cotiuously evolvig. The aalytics jourey must also cotiue to evolve. Capgemii s Big Data iitiative ca provide customers with solutios, capabilities ad a platform that eables agility ad iovatio for the busiess ad trasformatio for IT. The busiess imperative that we are addressig for our customers is how to create the value from the data to compete i the moder busiess eviromet. Capgemii s Big Data Strategy combies Capgemii s utilities idustry kowledge, process ad capability together with Big Data techology is desiged to brig value across the spectrum from defiig strategy to executio. 7

8 About Capgemii A global leader i cosultig, techology services ad digital trasformatio, Capgemii is at the forefrot of iovatio to address the etire breadth of cliets opportuities i the evolvig world of cloud, digital ad platforms. Buildig o its strog 50-year heritage ad deep idustry-specific expertise, Capgemii eables orgaizatios to realize their busiess ambitios through a array of services from strategy to operatios. Capgemii is drive by the covictio that the busiess value of techology comes from ad through people. It is a multicultural compay of 200,000 team members i over 40 coutries. The Group reported 2016 global reveues of EUR 12.5 billio (about $13.8 billio USD at 2016 average rate). For more details cotact: Ajay Verma Director, NA Eergy & Utilities Practice ajay.verma@capgemii.com Lear more about us at NA MACS_ _suksaha The iformatio cotaied i this documet is proprietary Capgemii. All rights reserved. Rightshore is a trademark belogig to Capgemii.