Enterprise Solutions. White Paper. Data at the Forefront of Change: Effective Data Management Drives Successful Transformation

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1 Eterrise Solutios White Paer Data at the Forefrot of Chage: Effective Data Maagemet Drives Successful Trasformatio

2 About the Author Milid Parikh Seior Executive, Trasformatio Practice, Eterrise Solutios As a Seior Executive, Milid is resosible for leadig the Data Maagemet ractice, for Tata Cosultacy Services' (TCS') Eterrise Solutios. He focuses o helig cliets develo ad deliver retur o ivestmet (ROI) based eterrise data trasformatio strategies. These ecomass Master Data Maagemet (MDM), data goverace oeratig models, data quality, Big Data, visual aalytics, ad related maagemet. Milid has over 17 years of exeriece i helig market-leadig comaies drive busiess roductivity through strategic data maagemet, Eterrise Resource Plaig (ERP) eabled IT trasformatios, ad IT risk ad comliace maagemet. He also brigs strog exeriece i balacig the eole, rocess, data, ad techology dimesios i divestiture, mergers ad acquisitios, market eetratio, ad cost aligmet isired trasformatios.

3 Abstract The busiess value of data is ofte oly artially uderstood by orgaizatios as they race to streamlie their eole, rocesses, ad techology i lie with the latest idustry bechmarks. However, data, the modest core of ay busiess activity, is the oly costat i a age of chagig alicatio ackages, advacig itegratio techologies, ad evolvig busiess rocesses. It is data which drives successful customer relatioshis ad busiess outcomes. Effective data maagemet is emergig as the game-chagig strategy for cometitive busiess erformace. This is fuelled by the eed to orgaize the ever-growig stream of data from a rage of coected devices to cater to the demad for aytime, aywhere iformatio access. A comrehesive ad adatable data maagemet strategy is therefore essetial to leverage systems, rovide eterrise-wide visibility, ad romote cofidece i orgaizatioal arameters ad measuremets Key Performace Idicators (KPIs), Key Risk Idicators (KRIs), ad other metrics. Although orgaizatios have stressed o trasformatio efforts i the ast, traditioal data migratio aroaches have bee uable to offer 'fit-to-use' data. Moreover, maiteace of data quality ad data goverace levels has ot bee satisfactory. Techological iovatios eed to be leveraged to covert data ito useful busiess isights for both trasactioal ad aalytical uroses. By ackowledgig data as a itegral art of the techology-led trasformatio iitiative, orgaizatios ca realize the beefits of trasformatio from day oe. A flexible beefits-weighted data maagemet aroach ca ultimately seed u decisio makig, drive by real-time isights. This aer coducts a detailed study of data maagemet, highlightig idustry treds ad challeges ecoutered i hadlig iformatio silos ad disaggregated data. It recommeds a future-roof data maagemet aroach which ca hel orgaizatios take itelliget, iformed decisios to ehace their erformace.

4 Cotets Itroductio 5 Challeges Ecoutered i Data Maagemet 6 Treds i Data Use ad Maagemet 7 Leveragig a Sustaiable Data Maagemet Strategy 10 Coclusio 13

5 Itroductio Today, with the roliferatio of data volumes, ad the rise i user exectatios from data, access to the relevat data at the right time ca determie the success of a busiess strategy or iitiative. Data structures across a orgaizatio are comlex ad layered, arisig from differet sources, ad i differet formats. As data volumes cotiue to grow at a exoetial rate, orgaizatios must fid ways to mie this iformatio for critical busiess isights. Busiesses demad dyamic ad istat access to data across devices to derive real-time isights for quicker ad accurate decisio makig. For istace, by aalyzig customer behavior ad buyig atters, orgaizatios ca maximize from their cross-sell ad u-sell oortuities, ad i-deth uderstadig of customer refereces ca hel build loyalty ad brad value. With strategic decisios backed by isights derived from a suerior data reository, busiesses ca idetify ew aveues for ivestmets, ta ito ew markets, ad esure the cotiued success of their existig ortfolio. As market eviromets raidly chage ad ew olicies come ito lay, trasformatio ad adatio become the cetral teets for evolvig orgaizatios. 'Trasformatio' is a value-lade term, which ca refer to orgaizatiowide sweeig chages that could rofoudly imact a orgaizatio's caabilities, rocesses, ad erformace. Alterately, it may oly ecomass a arrowly-focused busiess domai chage, ertaiig to a orgaizatioal fuctio such as customer care, suly chai, or caital maagemet. I the ast two decades, as trasformatios leveragig techology breakthroughs ad rocess imrovemets have ufolded, data i the structured, semi-structured, ad ustructured form has also witessed staggerig growth. Ufortuately, this data which drives trasformatio is scattered across the orgaizatio, makig it difficult to admiister ad overcome iformatio silos. Moreover, data cotiues to grow by velocity, variety, ad volume leadig to the creatio of Big Data ad i attemtig to coe with this, the maiteace ad goverace of data quality are ofte comromised. Not surrisigly, data maagemet has grow ito a extremely challegig ad comlex task. Traditioally, eterrises have oly focused o the Extract Trasform Load (ETL) based data coversio aroach. While this system eables data itegratio from multile alicatios by differet vedors, it may ot scale eough to cater to the growig size ad diversity of data. A more holistic aroach to data maagemet hels busiesses shae data accordig to their eeds. Data eeds to be laced at the forefrot of techology-led trasformatios, iteractig with ad suortig the traditioal comoets eole, rocesses, ad techology. Its effective maagemet should result i 'fit-to-use', clea, ad actioable data with clearly idetified owershi i a osttrasformatio world. With the hel of trasformative techologies, orgaizatios ca covert legacy data ito critical busiess isights that ca be utilized i redesigig busiess rocesses. Data maagemet is therefore emergig as the key busiess differetiator. It rovides a solid bedrock for effective trasformatio, offerig strategic advatage by eablig the moetizatio of returs from this valuable asset. 5

6 Challeges Ecoutered i Data Maagemet Although all executive stakeholders both i busiess ad IT agree that data maagemet is a comlex iece i the trasformatio jigsaw uzzle, there is surrisigly low agreemet o how challeges i the data maagemet domai ca be tackled. As it is difficult to idetify immediate returs o ivestmet, gaiig cosesus from differet stakeholders is a challege. It is estimated that 40% of the beefits targeted i the aual caital rojects (trasformatio iitiatives) budget are ever realized. Cosider this our research idicates that early 40% of rojects succeed (achieve roject objectives withi the set time ad budget), over 40% are challeged (late, over budget, ad/or with less tha the required features ad fuctios- resultig i about 50% of the iteded beefits beig realized), ad almost 20% fail (are cacelled rior to comletio or delivered ad ever used). Therefore, average Beefit Realizatio = (40% X 100%) + (40% X 50%) + (20% X 0%) = 60%. This imlies that the remaiig 40% (deductig the 60% Beefits Realized - calculated usig the formula above) of the beefits targeted i the aual caital budget are ever realized. Moreover, our research idicates that rojects - trasformatio iitiatives of over $10 millio - oly have a 2% chace of succeedig o time ad o budget, ad rereset a statistical zero i the success colum. Cosider this- the effects of data quality o ost-trasformed busiess rocesses ca be estimated usig Six Sigma. Harry ad Schroeder estimate that a average successful busiess rocess must maitai a miimum of 3.5 sigma value. This equates to 22,800 defects er millio. At this rate, the average cost of quality is 20% of the overall busiess rocess costs. For examle, if a comay set close to $250 Millio aually to maitai Order-To-Cash busiess rocesses, the, at 3.5 sigma, the cost of quality would be $50 Millio. The challege of maagig iformatio cotiues to grow. There are myriad forces comlicatig a eterrise's effort to establish a cosistet, authorized, available data store. These iclude, but are ot limited to: Dulicate Data Etries across the Orgaizatio As a result of mergers ad acquisitios, ad a diverse set of third-arty fuctioal oeratioal alicatios withi the eterrise, dulicate data etries may be reset. Icosistet reresetatio of data across busiess fuctios leads to huge costs icurred for data cleasig, ad substatial rework o data-heavy rojects. Difficulties i Coformig to Regulatory ad Comliace Needs Busiesses ofte ed u ayig heavy ealties for ot comlyig with regulatios o data rivacy ad security. Agig systems may be uable to rovide adequate data visibility for measurig comliace levels. Moreover, the dyamic ature of data ad growth of differet storage facilities ca make it difficult to aly oe stadard comliace solutio across the orgaizatio. 6

7 Time Set i Re-itegratig Data I the absece of established data service level agreemets ad fit-for-use levels, a lot of time is set o reitegratig data, ad correctig wrog or missig iformatio, istead of egagig those resources i other roductive activities. It is imortat to set i lace a commo stadard or layer which trasforms metadata from multile alicatios ad sources ito a sigle format, makig it iteroerable across the eterrise. As busiesses move towards more customized offerigs, data maagemet lays a critical role i coectig disarate systems ad offerig data-drive isights. Treds i Data Use ad Maagemet Accordig to a Garter survey, about 50 ercet of Customer Trasformatios will suffer limited accetace, if ot outright failure, because of the lack of attetio to data quality issues.¹ Although data forms the base for research ad develomet activities, ad guides forecastig ad busiess itelligece, trasformatio rojects ofte do ot adequately focus o data. By haressig the true otetial of data, orgaizatios ca reimagie their busiess rocesses ad drastically ehace erformace. Data use ad maagemet have evolved, with the followig treds emergig i recet years: Aligig Data Quality Strategy with a Orgaizatio's Visio As orgaizatios grale with exhaustive data volumes, a imortat questio they eed to ask themselves is: How ca the data i our reositories be available over time i the format required, i lie with the orgaizatio's visio? To aswer this questio, it is imortat to first idetify the orgaizatio's goals, ad the ma out how data ca eable the achievemet of these goals. If the data quality strategy does ot comlemet the orgaizatio's visio, the it will ot be of ay busiess value. The itegratio of data maagemet with the busiess workflow will hel maximize from the caabilities of this techology-led trasformatio iitiative. Haressig the Power of Data Aalytics Data aloe caot chage the rules of busiess, uless the correct data iterretatio tools are reset, alog with efficiet delivery mechaisms that ca rovide timely busiess isights. May comaies have already udertake maual efforts to uderstad customer atters, but with limited success ad scalability. This is where refied data assessmet tools ad advaced algorithms ca hel seamlessly trasform data from offerig iformatio to isights, rovidig actioable oiters, ad a sigle cosolidated versio of truth. Advaced data aalytics tools are icreasigly beig emloyed by busiesses to icrease their share of the customer's wallet, retai their best customers, ad eve limit high risk relatioshis. For istace, it is o loger eough to simly kow how may users visit a articular website. With each website visitor brigig a uique set of data which eeds to be catured, aalyzed, ad measured, data maagemet may seem a dautig task. However, with advaced web aalytics, site visitors ad their chage i behavior over time ca be miutely tracked. [1] Garter, A Garter ress release Says More Tha 50 Percet of Data Warehouse Projects Will Have Limited Accetace or Will Be Failures Through 2007 (2005), accessed November 06, 2014, htt:// 7

8 Website sectios that are attractig the most attetio ad iteractio ca be tracked, alog with keyword aalytics, ladig-age aalysis, ad erformace reorts. As more customized tools are develoed to uderstad customer refereces ad buyig behavior, more isightful reorts will be derived from the data reository. For examle, leadig retailers such as Amazo, Walmart, ad several others usually reset website visitors with several recommedatios that are customized to each visitor, ad are based o the buyig refereces ad browsig history of other users. Customers will ow be reseted with customized advertisemets ad couos whe they are ear a articular store. Haressig the ower of data aalytics o a massive scale i a very systematic way makes this kid of targeted marketig ossible. Realizig the Value of Data Stewards As data maagemet gais stroger footig, the role of dedicated data stewards will evolve to review, assess, ad validate idetified data etities, relatioshis, ad hierarchies. This will hel rioritize ad resolve issues i a roactive way. The ature of the task ad timeliess ad accuracy required will dictate the umber of data stewards required for differet activities withi the orgaizatio. This collaborative aroach to data maagemet will evetually imrove data quality ad goverace. Adotig a Rage of Data Quality Tools With mobile hoes beig omireset, comaies are movig closer to the 'follow the cosumer' aroach. With growig emhasis o the busiess value of high quality data, orgaizatios have begu to focus o data quality tools to do away with legthy data cleasig rocesses. From their origial fuctio of suortig data coversio ad auditig related trasformatio activities, data quality tools are ow beig used for the followig fuctios: Data arsig ad stadardizatio: Allows data aalysts to stadardize ad validate data, through formattig of values ito cosistet layouts accordig to idustry stadards, user-defied busiess rules, orgaizatioal ractices, ad so o Profilig: Eables etity, attribute, referetial, ad rules-based data rofilig ad aalysis across all the idetified data sources to determie uiqueess, comleteess, validity, ad structure referetial itegrity Cleasig: Esures that the existig data adheres to data stadards, ad does ot cotai dulicate etities that ca lead to misiterretatio of data ad iefficiecy i busiess rocesses. It also esures that data is accurate, comlete, ad cosistet. Data cleasig addresses the followig seve dimesios: Comlete: Esures data is comlete Cosistet: Checks whether data is cosistet across all systems ad i all forms of reresetatio Dulicate: Esures dulicate data is elimiated across all sources Valid: Verifies that the data is valid ad fit for use, ad ot outdated 8

9 Accurate: Esures that data available is accurate ad correct Itegrity : Itegrity measures existece of missig data or ot refereced data Timely: Checks whether data is harmoized ad itegrated across multile systems i a timely maer Matchig ad De-duig: Ivolves idetificatio, likig, or mergig data withi or across datasets ad sources Moitorig: Addresses the deloymet of cotrols ad metrics to esure that the data coforms to busiess rules. It also icludes the develomet of Data Quality (DQ) dashboards to moitor data quality ad trigger corrective actios across data domais ad sources Erichmet: Ehaces the value of iteral orgaizatioal data by erichig attributes with data from exteral sources such as: Geograhic: Post code, couty ame, logitude ad latitude, ad olitical district Behavioral: Purchases, credit risk, ad referred commuicatio chaels Demograhic: Icome, marital status, educatio, age, ad umber of childre Psychograhic: From hobbies ad iterests to olitical affiliatio Cesus: Household ad commuity data Maitaiig Regulatory Comliace ad Ehacig Data Security The seamless flow of data across differet chaels, social media websites, the Cloud, ad other latforms has draw attetio to the security of data i trasit. Moreover, the widesread adotio of a rage of wearable devices that geerate their ow data has cotributed to the growth i data volumes. This has also icreased axieties about the rivacy ad cofidetiality of this data, refocusig attetio o the security of data. To comly with idustry regulatios ertaiig to oeratios, ew develomets, risk maagemet, data rivacy ad security, such as the Sarbaes-Oxley Act (SOX), BASEL, Health Isurace Portability ad Accoutability Act (HIPAA), Trasfer of PII, EU Safe Harbor ad others, orgaizatios eed cost-effective ad sustaiable solutios to esure data quality ad goverace. Istead of adotig a iecemeal aroach to comliace, which leads to sigificat rework at later stages, orgaizatios are realizig the value of a comrehesive forward-lookig aroach which ca esure that data is comliat ot just i the reset times, but for future regulatios too. Busiesses are uder ressure to adot stricter cotrols to rotect the data they ow from uauthorized access, use, modificatio, ad so o. The ext sectio details recommedatios o how the idetified challeges ad objectives ca be effectively addressed by orgaizatios. 9

10 x E t a Leveragig a Sustaiable Data Maagemet Strategy The recommeded data maagemet aroach is based o idetifyig ad actively ivolvig aroriate levels of the maagemet team i the desig ad imlemetatio of clea data. Maagemet will eed to dislay a heighteed sese of resosibility ad claim owershi of data across the orgaizatio. This collaborative aroach to data maagemet eeds to be customized for each orgaizatio by itegratig the orgaizatio's kowledge base, tools, techiques, temlates, ad methods with other orgaizatioal assets, existig goverace, ad quality rograms. To assess whether the data is fit for use, orgaizatios will have to check whether the quality of data is suitable for its alicatio ad i accordace with user exectatios. Data quality levels are costatly evolvig; however, the basic teets uderliig data quality are accuracy, timeliess, relevace, data iterretability, ad iteroerability. Broadly seakig, the data maagemet aroach should be: Established: Time-tested, rove, ad well-defied Tool-Eabled: Powered by customizable reusable tools, techiques, ad temlates Comrehesive: Oe to takig ito accout all factors by aalyzig eole, rocesses, techology, strategy, ad the orgaizatio's missio Modular: With clearly defied missio-orieted data strategy modules data readiess, data goverace, data quality, ad data migratio that are detailed later i this sectio Reusable: Providig reusable assets which exedite data readiess ad data migratio for the ext iitiative Scalable ad adotable: With the ability to exad data quality ad data goverace ito full-size eterrise rograms ost-trasformatio The recommeded data maagemet aroach has the followig four layers, which are also illustrated i Figure 1: T Oortuities a r s Peole fo rmatio Strategy Data Techology A Process r o a ch Challeges io t u c e ed t a r e l ce Ac B Data Maagemet Aroach e t r te T busiess Is o o ls Data Readiess Data Migratio & Met h o Data Maagemet d ig s h t s Data Quality Data Goveraace A d a b le & S c a l a b le Figure 1: A Overview of the Data Maagemet Trasformatio Aroach 10

11 Data Migratio: This is a traditioal comoet resosible for Extract, Trasform, ad Load activities. It extracts data from differet sources, stores the data i the correct format, ad loads it to the aroriate database. Data Quality: This comoet maages the overall quality of the data, guided by the 'Garbage I Garbage Out' ricile. It assesses quality throughout the data lifecycle, esecially from a 'fit-to-use' ersective. For certai key data elemets, the source data quality is also assessed. This comoet works as a extesio of the existig Data Quality rogram to follow its madate ad use, develo, ad stregthe tools, temlates ad methods. It should be scaled u ost-trasformatio to cotiue moitorig quality. Data Goverace: This comoet works as a extesio of the existig rocesses goverig legacy data. Alterately, it could be set u aew as art of the trasformatio rocess, ad further scaled u i the osttrasformatio world. Data goverace icludes the resosibility assigmet matrix RACI (Resosible, Accoutable, Cosulted, ad Iformed) to moitor the executio of data maagemet tasks. Data Readiess: This comoet maages data delivery, by overseeig the other three comoets (goverace, quality, ad migratio), ad workig with busiess ad IT owers to esure that the data is ready ad available for migratio. Data readiess is also resosible for itegratio with roject goverace, orgaizatioal chage maagemet, testig, roductio cut-over, ad other fuctios. The key fuctios of each of these layers are cocisely reseted i Table 1 ad Figure 2. Key Activities Udertake by: Data Migratio Data Quality Data Goverace Data Readiess Perform data maig Develo ad execute the Data Quality Pla Create a strategy ad bluerit for defiig, desigig, ad adotig data goverace Idetify ad cofirm 'true' data sources, data elemets, data owers, ad key techical resources Perform data erichmet Defie ad develo quality metrics View data strategically Data architecture ad itegratio assessmet Desig ad execute data validatio Desig ad execute data rofilig Take reliable data-based decisios Coduct data assessmets Perform data correctio Desig ad execute data cleasig Desig ad imlemet Proof Of Cocet (POC) Execute coversio Desig ad execute data validatio Defie ad develo data validatio rocedures Review excetios reorts Perform data correctio Itegrate with orgaizatioal level Data Coucil Review record couts Perform data erichmet Reort to the roject goverace ad steerig committee Review balaces Moitor data quality Review recociliatios Table 1: Key fuctios udertake by the four data maagemet comoets 11

12 These four hierarchical comoets should be modular i ature ad comlemet each other, with mutually exclusive tasks ad roles. The data goverace ad quality comoets of our recommeded aroach ca be used to achor existig rograms ad act as a extesio, drawig uo ad utilizig tools, techiques, ad methods. I tur, they ca hel stregthe existig rograms by rovidig a latform ad sreadig the message of data quality ad goverace as art of trasformatio. Hierarchical layers of Data Maagemet Eterrise Cororate Goverace Data Goverace/Coucil Data Quality Data Readiess Resosible for iteractio of the data maagemet comoets with other trasformatio comoets. Techical Architecture Busiess Subject Areas Datasets Project Activities Data Goverace Goverace of the i-scoe legacy data across busiess uits ad geograhies offerig, could be a extesio of existig ad scaled u. Program Maagemet Risk Maagemet OCM Traiig Data Quality Provides Quickwis ad focus o fit-to-use asects of legacy data i additio to a successful migratio. A extesio of existig data quality rogram, or sets u a ew oe ad scales u. Data Migratio Extract, Trasform ad Load activities. Figure 2: Missio critical, hierarchical layers of data maagemet Effective data maagemet will result i harmoized 'fit-to-use' data across the eterrise, as revealed by the followig case study. 12

13 Successful busiess trasformatio eabled by data maagemet A global techology comay decided to embark o a multi-millio dollar global trasformatio jourey. Previously, the orgaizatio had udertake oly traditioal data migratio aroaches, ad did ot have a data goverace framework to assess, clease, ad reare data for use. The comay, as art of its strategic laig for the trasformatio rogram, ut data at the forefrot of the iitiative, i additio to eole, rocess, ad techology. It develoed a Data Maagemet Strategy aliged with the orgaizatioal goals. The trasformatio addressed data lifecycle maagemet ad data itegratio, with metrics ad scorecards maagig data related tasks ad commuicatios. This four-legged strategic aroach allowed the multiatioal giat to elimiate data related delays, ad develo ad foster a eviromet that valued data as trasformative i ature, durig ad ost-trasformatio. Coclusio With traditioal data models udergoig trasformatios ad disrutive treds chagig the rules of busiess, data maagemet is o loger a otio. Neither is it a back-office fuctio which ca be give half-hearted attetio. Effective data maagemet is a focus area for orgaizatios today, offerig busiess-ready data while mitigatig the risks of data migratio failure, resolvig data quality issues, securig missio critical data, ad offerig data goverace solutios. Orgaizatios that ca establish a effective data maagemet strategy will be able to geerate isights from their data coveietly, ad will emerge as the leaders of tomorrow. Further readig: Doubleday Busiess, M. Harry ad R. Schroeder, Six Sigma: The Breakthrough Maagemet Strategy Revolutioizig the World's To Cororatios, Jauary /20/ Garter, Measurig the Busiess Value of Data Quality (October 10, 2011), htts:// Garter, Fidigs From Primary Research Study: Data Quality Issues Create Sigificat Cost, Yet Ofte Go Umeasured (October 21, 2010), htts:// CRM Media, Data Quality Best Practices Boost Reveue by 66 Percet (Jauary 22, 2009), htt:// 66-Percet asx 13

14 About TCS Eterrise Solutios Uit With oe of the most owerful IT cosultig ad services ortfolios i the world, TCS' Eterrise Solutios hel comaies otimize data ad workflow, maage risk ad comliace, icrease oeratioal efficiecy ad roductivity ad reach eak erformace. TCS Eterrise Solutios drive busiess trasformatios, emowerig our cliets to take advatage of emergig oortuities ad market dyamics. From IT strategy ad trasformatio to eterrisewide software alicatios, we otimize techology ad busiess rocesses. Through our Cetres of Excellece ad Iovatio Labs, couled with our fuctioal busiess exertise, we develo custom idustry solutios ad accelerators. Our rove, reeatable ad scalable methodology eables eterrises to realize the maximum busiess value from ivestmets. Our success is bolstered by log-term strategic alliaces with the world s most advaced busiess software comaies, helig us deliver better, faster ad more cost-effective solutios. Cotact For more iformatio about TCS Eterrise Solutios Uit visit: htt:// global.marketig@tcs.com Subscribe to TCS White Paers TCS.com RSS: htt:// Feedburer: htt://feeds2.feedburer.com/tcswhiteaers About Tata Cosultacy Services (TCS) Tata Cosultacy Services is a IT services, cosultig ad busiess solutios orgaizatio that delivers real results to global busiess, esurig a level of certaity o other firm ca match. TCS offers a cosultig-led, itegrated ortfolio of IT ad IT-eabled ifrastructure, egieerig ad TM assurace services. This is delivered through its uique Global Network Delivery Model, recogized as the bechmark of excellece i software develomet. A art of the Tata Grou, Idia s largest idustrial coglomerate, TCS has a global footrit ad is listed o the Natioal Stock Exchage ad Bombay Stock Exchage i Idia. For more iformatio, visit us at IT Services Busiess Solutios Cosultig All cotet / iformatio reset here is the exclusive roerty of Tata Cosultacy Services Limited (TCS). The cotet / iformatio cotaied here is correct at the time of ublishig. No material from here may be coied, modified, reroduced, reublished, uloaded, trasmitted, osted or distributed i ay form without rior writte ermissio from TCS. Uauthorized use of the cotet / iformatio aearig here may violate coyright, trademark ad other alicable laws, ad could result i crimial or civil ealties. Coyright 2014 Tata Cosultacy Services Limited TCS Desig Services I M I 12 I 14