A Dozen Ways Insurers Can Leverage Big Data for Business Value

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1 White Paper A Doze Ways Isurers Ca Leverage Big Data for Busiess Vaue The amout of structured ad ustructured, itera ad extera data coursig ito every orgaizatio is voumious ad icreasig expoetiay. Data today comes from disparate sources that icude customer iteractios across chaes such as ca ceters, teematics devices, socia media, aget coversatios, smart phoes, emais, faxes, poice reports, day-to-day busiess activities, ad others. Garter s 2011 Top 10 ist of IT Ifrastructure ad Operatios Treds predicts a percet growth i data over the ext five years. Orgaizatios actuay process oy about 10 to 15 percet of the avaiabe data, amost a of it i structured form. Whie maagig this overwhemig data fow ca be chaegig, isurers ca reap very rea beefits ike icreased productivity, improved competitive advatage ad ehaced customer experiece by capturig, storig, aggregatig, ad evetuay aayzig the data. This vaue does ot come from simpy maagig big data, but rather, from haressig the actioabe isights from it. Isurers that ca gea objective-drive busiess vaue by appyig sciece to their data ad derivig isights wi maitai a competitive advatage ad stay ahead of the curve i this iformatio age.

2 About the Authors Ajay Bhargava, Director, Aaytics ad Big Data, TCS Ajay Bhargava has more tha 23 years of idustry, research, ad teachig experiece i areas reatig to Databases, Eterprise Data Maagemet, Data Warehousig, Busiess Iteigece, ad Advaced Aaytics. Over the years, he has provided busiess ad techoogy-orieted strategic, metorig, ad customer-cetric soutios to his ciets. Ajay heads TCS Aaytics ad Big Data Ceter of Exceece for its Isurace ad Heathcare customers. He pubishes artices, ad is a speaker ad paeist at various cofereces o Eterprise Data Maagemet, Busiess Iteigece, ad Advaced Aaytics. As Adjuct Facuty, he has aso taught at The Uiversity of Texas, Austi ad Coege of Egieerig Pue, Idia. Ajay hods a M.S. i Computer Sciece ad M.S. i Aerospace Egieerig from The Uiversity of Texas at Arigto. He obtaied his B.Tech. i Aeroautica Egieerig from Idia Istitute of Techoogy (I.I.T.), Mumbai,

3 Tabe of Cotets 1. Big Data, Defied 4 2. Haressig ad Harvestig Big Data 5 3. Tweve Ways to Create Vaue from Big Data 7 4. The Opportuity 9 5. Gettig Started Cocusio 11 3

4 Big Data, Defied Big Data creates ew opportuities for busiess stakehoders that were ot possibe with the aaysis of structured cotet i traditioa ways. Athough there is a ot of idustry hype surroudig Big Data, it ca be described usig the foowig five characteristics: Voume: Big Data refers to the eormous ad expoetiay growig amout of data foodig ito ad out of every eterprise. Exampes of Big Data reevat to isurace compaies ca be foud i a variety of sources icudig: The structured grauar ca detai records (CDR) i a ca ceter; Detaied sesor data from teematics devices; Extera iformatio icudig weather, geography ad peri, traffic, demographic ad psychographic behavior data; Ustructured data from socia media, adjustor otes, poice reports, ad medica records. There are currety more tha a 100 vedors ike Isurace Services Orgaizatio (ISO), Choicepoit, ad Lexus/Nexus providig extera data services to isurers. Variety: Proiferatig chaes have ed to burgeoig types of data. This presets isurace orgaizatios with the chaege of extractig isights from data that goes beyod the usua structured eviromet of data warehouses, ad is from disparate source systems that icude mobie, oie, aget-geerated, adjustor otes, medica reports, socia media, text, audio, video, og fies, ad more. Veocity: Isurace firms must be abe to access, process, ad aayze huge voumes of iformatio as quicky as possibe i order to make timey decisios. Isurers specificay eed to reduce atecy to: Optimize cross-seig ad up-seig i a ca ceter eviromet; Provide quick turaroud o eterprise itraet documets search to study the impact of differet evets; Reduce deivery time for busiess reports i a data warehousig eviromet. Veracity: The reiabiity ad cosistecy of the data its depedabiity ad quaity is a critica issue for isurers ookig to derive meaigfu isights. This hods true for both, Big Data ad sma data. I some cases, such as i voice to text coversios, we fid that eve ot-so-perfect data quaity ca resut i meaigfu isights, especiay if isurers are tryig to aayze macro-eve pheomeo, such as setimet aaysis. Vaue: Isurers that haress busiess objective-drive vaue isights wi come out ahead of the pack i this chaegig eviromet. However, i order for isurers to derive true vaue from Big Data, they must depoy ew busiess modes that eabe performig aaytics o the data faster ad more cost-efficiety. Vaue, therefore, is the most importat of the five Big Data characteristics. 4

5 Haressig ad Harvestig Big Data There are two fudameta aspects to Big Data. The first is haressig, which ivoves coectio, admiistratio ad maagemet of Big Data. The secod is harvestig, the skis ad techiques required to appy sciece to data i order to derive actioabe ad meaigfu isight from it. Haressig Big Data At the most basic eve, haressig is the amassig of Big Data; it reates to how isurers maage Big Data ad how they create a ecosystem that ca ot oy create Big Data but sustai it as we. Years ago, haressig data was much easier tha it is today; however, beefits of usig this data were more imited as we. Haressig Big Data today ivoves accessig a combiatio of additioa sources of data ike socia media as we as ewer techoogy that provides isurers access to data ad the abiity to aayze it. Garter estimates that betwee 80 ad 90 percet of a data produced is ustructured. Today, isurers ca tap ito a treasure trove of ustructured data of a varieties: text, audio, video, adjustor otes, cick streams, ad og fies, for istace, ad combie it with other structured types such as weather, traffic, peri, ad geographic data. Isurers ca o oger maage Big Data with traditioa techoogies. Istead, isurace orgaizatios must everage a whoe ew cass of patforms, ike the ope source Hadoop ecosystem ad its commercia variats offered by IBM, Coudera, Hortoworks, MapR, etc. Hadoop is a distributed fie system with the Map Reduce paradigm (A framework for processig paraeizabe probems across huge datasets usig a arge umber of computers. a) Map step - Master ode breakig ad aocatig the processig to be carried out by idividua odes, which i tur, process the task ad retur it back to the Master, ad b) Reduce step - the master ode coectig the aswers to a the sub-probems ad combiig them to form the output. Each of these steps ca be carried out i parae) ad techoogies buit o top of them. The Hadoop-based frameworks represet a paradigm shift i ot oy beig abe to hade differet kids of data, but aso providig speedy processig capabiities for huge voumes of data. Additioay, IT departmets that are typicay resposibe for admiisterig ad maagig the Big Data eviromet are adoptig programmig aguages such as R ad Pytho. Whie they origiated i the cotext of hadig Big Data at Yahoo ad Googe, these programmig aguages ca be used to hade may traditioa data processig tasks as we. Oe exampe is the use of the Hadoop patform as a ETL eaber for busiess agiity. Today, isurers are adoptig this use case, ofte as their first experiece i everagig Big Data to meet busiess service eve agreemets (SLAs). Eve though the traditioa data warehousig eviromet sti ivoves structured data, isurers that ivest i this patform kow they wi be abe to support ustructured data as we, ow ad i the future. Harvestig Big Data Ustructured data caot be cosumed i its raw form. It must be processed ito a cosumabe form before it ca be iterpreted or acted upo. Harvestig utiizes techoogy ad agorithms that eabe isurers to aayze, deiver actioabe isights ad derive rea vaue from Big Data. Skis such as statistica aaysis, data miig, ecoometrics, busiess aaytics, visuaizatio techiques, ad more are i high demad as they provide a soid foudatio for derivig usefu isights from the data. Uiversities have started tryig to fi the suppy demad gap by offerig various graduate programs i busiess aaytics to provide for the ext geeratioa skis eeded to mie actioabe isights. 5

6 To achieve Strategic Objectives Dictate Actios Isights ead to Busiess Requiremets Icudes Busiess Objectives, Expected Beefits, Use Cases ad KPIs Map to Aaytics Requiremets Icudes factor aaysis, tredig, statistica ad predictive modes to fufi Busiess Requiremets. FUTURE ORIENTED BI Requiremets Icudes reports/metrics/aaysis eeded to fufi Busiess Requiremets PAST ORIENTED Iterate Data & Process Requiremets Icudes Coceptua ad Dimesioa Data Mode, Subject Areas ad Busiess Processes eeded to fufi BI ad Aaytics Figure 1: Top-dow Approach to Creatig Actioabe Isights from Big Data Whie the abiity to successfuy haress ad harvest data is critica to a Big Data strategy, the harvestig aspect is where isurers derive the true vaue from their data, with the hep of aaytics. Defiig use cases ad hypotheses for Big Data harvestig becomes crucia whe foowig a focused top-dow approach to creatig actioabe isights (see Figure 1). Athough this is a focused approach, may times isurers eed to do perform exporatory data aaysis to idetify use cases utiizig Big Data i the first pace. This iitia bottom up approach becomes a prerequisite for determiig ad prioritizig use cases for which Big Data Proof of Cocepts (PoCs) shoud be pursued. Rea vaue is derived whe actioabe isights ca make a positive differece i achievig the orgaizatio s strategic objectives. Some of the other comparisos betwee haressig ad harvestig are show i Tabe 1. 6

7 Big Data Dimesio Haressig Harvestig Data Data - Nou Aaysis - Verb Focus Data Maagemet, Scaabiity, Itegratio, Performace Story Teig, Actioabe Isights, Askig the right Qs The "V"s Voume, Variety, Veocity Veracity, Vaue Process Architectures, Data Fows Busiess SMEs with idustry process kowedge Techoogy Data & Process Modes, DBMS, Hadoop ecosystem Use Cases, Hypothesis, Busiess Objectives Roe Programmers, Data Scietists, Data Stewards Statisticias, Busiess Aaysts, Data Scietists Expertise Hadoop, R, Scriptig Laguages, NoSQL, SQL Busiess Requiremets, What-If Qs, SQL, Visuaizatio Discipie Sciece, Egieerig, Mathematics, Statistics Art, Busiess, Statistics, Idustry specific Orgaizatio IT - CIO, CTO Busiess - CMO, CFO, CCO Time Spet 80% i Itegratio, Maiteace, Storage, Performace 80% i Aaysis, Actioabe Isights, Visuaizatio Vaue Ivestmet, Eaber, Admiistrator Creator, Competitive Differetiator, Cosumer Tabe 1: Uderstadig Big Data Haressig ad Harvestig Harvestig ad haressig activities are compemetary to oe aother, two sides of the Big Data coi. Big Data patforms do ot repace existig traditioa data maagemet ad aaytics patforms. Istead, they merey compemet, add, mature, ad improve upo the existig eviromet ad capabiities. Tweve Ways to Create Vaue from Big Data Accordig to McKisey & Co. (1), Big Data creates vaue i five differet ways: First, maagig Big Data ca icrease trasparecy, makig data more easiy accessibe to reevat stakehoders. Secod, as they create ad store more trasactioa data i digita form, orgaizatios ca coect accurate, detaied performace data i rea-time or ear rea-time, eabig experimetatio to idetify eeds ad improve performace. \ Third, Big Data gives orgaizatios the meas to improve customer segmetatio ad the, better deveop ad taior products, services, ad promotios to target each specific segmet. Fourth, a Big Data strategy ca icude sophisticated aaytics to provide actioabe isights that miimize risks ad improve decisio-makig. Ad fiay, Big Data ca be idispesibe for orgaizatios ookig to create ew busiess modes ad improve products ad services. 7

8 Leadig-edge isurers have started (or are paig) to expoit Big Data for at east a doze use cases, each of which adds vaue to their orgaizatio i oe or more of the five ways described above. These use cases are give beow. 1. Makig performace improvemets i existig data warehouse eviromets. IT has begu to adopt Hadoop-based architecture to speed up ETL i a data warehouse eviromet to meet reportig busiess SLAs. 2. Detectig fraud. Chief caims officers (CCOs) are adoptig a muti-chae approach to fraud detectio by ookig at structured data i their caims ad poicy data warehouses, ad combiig it with textua data i adjustor otes, poice reports, ad socia media. Specia Ivestigative Uits (SIUs) are very iterested i idetifyig suspicious caims or caims that have subrogatio or itigatio potetia. Text Aaytics ad Natura Laguage Processig (NLP) capabiities are eabig iovative soutios i caims fraud detectio, i additio to automated busiess rues, predictive aaytics, socia media aaytics, ad ik aaysis techiques. 3. Combiig customer chaes. Combiig direct customer coectios (emai, ca ceter, aget, porta, faxes, adjustor reports, etc.) with idirect customer coects such as socia media, bogs, og fies ad so o, provides a more hoistic, 360 degree view of each customer. This heps to create a persoaized, uified commuicatio respose, eabig Chief Marketig Officers (CMOs) to achieve better brad vaue ad gai competitive advatage whie directy impactig the bottom ie by reducig commuicatio waste. Aso, miimizig capita expediture by usig coud techoogy, aog with mobie visuaizatio techiques that hep executives access hoistic customer iformatio aywhere, eabig quick ad cost-effective decisio-makig. 4. Optimizig ca ceter workoad. Aayzig teecom data from the switches (CDRs) ad combiig it with caims data heps i uderstadig what activity was performed by whom ad how efficiety. This isight is used to provide traiig guideies for empoyees. Tempora ca patters aaysis o voumious ad raw teecom data ca hep assist i staffig optimizatio as we. 5. Usig teematics data to derive prescriptive ad predictive vaue. Isurace CIOs are currety ivestigatig how aaytics ca hep improve drivig behavior of motorists by sesig teematics data ad respodig i earrea time. I additio to prescriptive aaytics, that is, devices aertig upo risky behavior such as speedig or seat bet o-usage, they are aso iterested i predictig drivig behavior risks, aticipatig road deays, etc. 6. Leveragig cross-se ad up-se potetia. By aayzig text ad speech i a ear rea-time eviromet, orgaizatios are preseted with ew opportuities to covert the ca ceter from a cost ceter to a ivestmet ceter by providig cross-se ad up-se capabiities. 7. Improvig setimet aaysis to improve customer service. Isurers are usig NLP ad text aaytics for socia media, as we as speech aaytics for ca ceter coversatios to improve their setimet aaysis; this heps them meet customer service improvemet objectives better. 8. Utiizig socia media to itroduce ew products ad services. Isurace CMOs are movig away from capita-itesive teevisio ad iteret promotios to itroduce ew products ad services, ad target customers i specific regios. They are ow utiizig socia media as a cost-efficiet ad effective aterative, iovativey chagig the busiess mode. Isurers ca experimet with this approach for differet customer segmets, ad the upgrade their strategies to a atioa eve. 8

9 9. Cosig the oop betwee pricig risk ad caims. Uderwriters are studyig oss ad fraud propesity of existig caimats i order to better price risk for ew prospects, especiay i the property casuaty isurace busiess. This heps i miimizig risks ad, to a arge extet, price the risk appropriatey. 10. Leveragig extera data for more accurate pricig. Usig rea-time ocatio, traffic, ad weather data ca ead to more appropriate pricig o property casuaty isurace based o how ad where the isured customers actuay drive their cars. 11. Ehacig itraet search capabiities. May reisurers ad isurace agets are usig Big Data to improve speed ad search capabiities o their itraet, especiay for ustructured PDFs ad Word documets, which was ot possibe previousy. These are beig used by their fiacia divisios, as we as i ca ceter scearios to provide rea-time recommedatios. 12. Creatig comprehesive customer satisfactio surveys. Most isurace orgaizatios perform customer surveys usig a very sma customer sampe size. Big Data eabes isurers to survey their etire customer base, processig the survey resuts i a fast ad cost-effective way to pait a truer picture from the customer resposes. McKisey Report - 5 differet ways of VALUE creatio Use Cases Icrease trasparecy Improve performace, ear-rea time aaysis Improve customer segmetatio Provide actioabe isights Create ew busiess modes to improve products, 1 ETL Performace Improvemets X 2 Detectig Fraud X X X X X view of Customer X X 4 Ca Ceter Optimizatio X X 5 Teematics X X X X 6 Cross-Se ad Up-Se X X X X 7 Setimet Aaysis X 8 Socia media for ew products ad services X X X X 9 Cosig oop betwee pricig risk ad caims X X X 10 Extera data for pricig X X 11 Search o ustructured documets X X 12 Better customer surveys X X Tabe 2: The Big Data Use Case-Beefit Grid The tabe above shows how each of the doze use cases ca create vaue from Big Data. The Opportuity 2 Accordig to a Jue 2012 research from aayst firm Novarica, i geera, Big Data is ot yet a big priority i most isurace orgaizatios. Isurers are argey hampered by severey fragmeted data eviromets ad iformatio sios, as we as isufficiet ivestmet i toos ad techoogies to support a Big Data strategy. Most carriers are 9

10 sti maturig ad expadig their use of traditioa data aaytics ad predictive modes to optimize processes, reduce osses ad geeray improve their book of busiess. Isurers who are t exporig ad embracig Big Data, ad deveopig a Big Data strategy wi fid that they are osig their competitive advatage ad potetia to achieve efficiecies. They wi be uabe to gea actioabe isights from the moutais of data foodig ito their orgaizatios. Some of the key fidigs of the research with respect to Big Data adoptio ad opportuity were: Whie a vast majority of isurers are usig aaytics for actuaria (95%) ad pricig (83%) processes, fewer tha haf the isurers are usig aaytics to improve operatioa areas ike saes, marketig or optimized work assigmet for uderwriters or caims adjusters. Reativey few isurers are fuy immersed i a comprehesive Big Data strategy ad reapig its beefits; however, the good ews is that most isurers are paig their Big Data approach. Eve fewer isurers capture, persist, ad aayze Big Data withi their computig eviromet today, but those that do typicay everage traditioa computig, storage, database ad aaytics, i additio to ewer patforms such as the Hadoop ecosystem. Larger property casuaty isurers uiversay pa to embrace Big Data ad aaytics across a fiacia ad risk maagemet areas (as we as most operatioa areas). Oy about haf of the smaer isurers are paig the same actios. Gettig Started Big Data soutios ecompass a ew geeratio of software ad architectures desiged to ecoomicay extract vaue from eormous voumes ad variety of structured ad ustructured data by eabig rapid data capture, discovery, ad/or aaysis. Accordig to Novarica, the isurers who have created a cuture where busiess eaders trust aaytics ad act o the isights provided wi be best positioed to profit from the potetia vaue of Big Data. Isurers shoud take steps to create that cuture today if it does t aready exist i their compaies. The key is to start sma with a PoC. Foowig is a exampe of how isurers ca everage a Big Data patform ad some key cosideratios to keep i mid. I this exampe, IT is iterested i usig a Big Data eviromet to speed up og-ruig ETL processes i a traditioa data warehouse eviromet, because the traditioa processig is eadig them to miss reportig SLAs for busiess. Big Data Chaeges: At-A-Gace Isurers are faced with a umber of factors that combie to make Big Data a big chaege: The exposio of data ad proiferatio of chaes; A icreasigy competitive adscape, especiay i the P&C ad ife sectors; The fiacia tsuami of the past severa years, as we as the resutig icreasigy demadig reguatory requiremets i both North America ad Europe; A uusuay high umber of catastrophic osses caused by brush fires, hurricaes, earthquakes, ad other atura disasters i recet years; The costat stream of iovatios that have made it essetia for isurers to dea with Big Data at the same time they are maagig the chaeges of their existig sma data eviromets; Sioed data eviromets. 10

11 PoC Objective: Icrease Busiess Agiity Big: Busiess Use Case/Hypothesis Speed up ETL so IT ca meet reportig SLAs for busiess Sma: Ivestmet < 120K$ Big: Executive Support CxO eve Sma: Scope/Desig/Impemetatio Few ogest-ruig ETL scripts Big: Architecture (Data Patform) Hadoop eco-system - scaabe for growth Sma: Team 5 FTE Big: Coaboratio - IT & Busiess Needed for Haressig AND Harvestig Sma: Duratio < 12 caedar weeks Big: Busiess Vaue Busiess is abe to take timey decisios/actios Sma: Icremeta Success Provide fudig for ext phase Tabe 3: Sampe PoC for Big Data Adoptio It is importat for isurers to deveop a good busiess use case for meetig the strategic objectives of that ie of busiess. I additio, soid backig from a CxO/VP eve executive is extremey importat ot oy for fudig, but to evageize ad commuicate the objectives ad eed for adoptio of Big Data to the arger orgaizatio, icudig parters ad vedors. Athough the scope ad ivestmet i terms of peope (approximatey five fu time empoyees), toos (for exampe, ope source Hadoop ecosystem), techoogies ad ifrastructure (for exampe, commodity hardware or coud) might be sma, the architecture shoud keep the og term view i mid. For the effective haressig ad harvestig of Big Data, cose coaboratio betwee IT ad busiess is imperative to iterativey experimet ad drive actioabe isights. Isurers ca the use this icremeta success to get icreased fudig for ext phases ad/or use cases. Cocusio As isurers idetify ad uderstad the scearios for appyig Big Data withi their busiesses, they wi eed to tweak their existig processes to be abe to igest the data variety, esure data veracity, icreasig voume or growig eed of rea-time veocity, of data to derive objective-drive actioabe vaue. Isurace orgaizatios that are abe to deveop a aaytics-drive cuture, ad that ear how to haress the power of Big Data ad harvest the vauabe iformatio ad isight Big Data provides ca create competitive advatage ad positivey impact their brad as we as their top ad bottom ie. Refereces [1] McKisey Goba Istitute report, Big Data: the Next Frotier for Iovatio, Competitio ad Productivity, May [2] Novarica, Isurace Techoogy Research Couci, Aaytics ad Big Data at Isurers: Curret State ad Expectatios, Jue

12 About TCS Isurace TCS is a word eadig Busiess ad IT cosutig compay, providig soutios to over 100 Goba Isurers for over three decades. The experiece gaied by TCS i Soutio Buidig for the Isurace idustry, has bee ecapsuated ad istitutioaized i various Isurace Soutio Ceters that we have setup across the word for deiverig busiess vaue to its customers. Our expertise i the Isurace sector covers a wide spectrum of busiess that icudes: Life, Auities, Pesios Property ad Casuaty Heath Isurace Reisurace We have aways respoded to ciet requiremets i a positive maer with kee focus o deiverig busiess vaue. We have deivered severa chaegig soutios to Isurace Compaies i the foowig areas: Busiess Soutios ad Cosutig IT Outsourcig Eterprise Appicatios Techoogy Soutios Assurace Services Products ad Asset Based Soutios Busiess Process Maagemet Isurace Uit has bee efficiety everaged for the deveopmet of products, compoets ad assets, which serve as soutio acceerators i the areas of Product Deveopmet, Marketig ad Distributio, Poicy Admiistratio, Caims Maagemet, Reisurace, Biig ad Accoutig. Cotact For more iformatio about TCS Isurace services, cotact or visit Subscribe to TCS White Papers TCS.com RSS: Feedburer: About Tata Cosutacy Services Ltd (TCS) Tata Cosutacy Services is a IT services, cosutig ad busiess soutios orgaizatio that deivers rea resuts to goba busiess, esurig a eve of certaity o other firm ca match. TCS offers a cosutig-ed, itegrated portfoio of IT ad IT-eabed ifrastructure, egieerig ad TM assurace services. This is deivered through its uique Goba Network Deivery Mode, recogized as the bechmark of exceece i software deveopmet. A part of the Tata Group, Idia s argest idustria cogomerate, TCS has a goba footprit ad is isted o the Natioa Stock Exchage ad Bombay Stock Exchage i Idia. For more iformatio, visit us at IT Services Busiess Soutios Cosutig A cotet / iformatio preset here is the excusive property of Tata Cosutacy Services Limited (TCS). The cotet / iformatio cotaied here is correct at the time of pubishig. No materia from here may be copied, modified, reproduced, repubished, upoaded, trasmitted, posted or distributed i ay form without prior writte permissio from TCS. Uauthorized use of the cotet / iformatio appearig here may vioate copyright, trademark ad other appicabe aws, ad coud resut i crimia or civi peaties. Copyright 2013 Tata Cosutacy Services Limited TCS Desig Services I M I 06 I 13

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