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Dr.TanushreeBiswasABSTRACT
Businessanalytics(BA)isacombinationofdisciplinesandtechnologyforsolvingbusinesschallengesusingdataanalysis,statisticalmodelsandotherquantitative methods. It involves an iterative, rigorous investigation of an organization's data, with an emphasis on statistical analysis, to drive decision-making. Big data and businessanalyticshavegainedprominenceintherecentdecade,andfirmsmustlearnhowtousethemtocreatevalue.Despitetherelevanceoftheseconcerns,little researchhasbeendonefromanorganization'sperspective.Anorganizationmustintegrateanalyticstoimprovedecision-making.Dataanalysisgivesorganizations practical insights from daily data. These insights help companies make decisions, solve problems, understand client demands, and identify trends. Business intelligencesolutionslinkdatasources,doextensivedataanalysis,developinteractivedashboards,generateactionableinsights,andstreamlinedecision-making.This paperdiscussesimplementinganalyticsviadevelopingateamandleadership,involvingstakeholders,andusingBusinessIntelligencetools.
KEYWORDS:Analytics,bigdataanalytics,managementanalytics,businessintelligence,marketinganalytics.
INTRODUCTION:
Theworldasweknowitwillcontinuetobeincreasinglydrivenbydata.Froma businessowner'spointofview,dataanalyticsisthetoolthathelpsthemunderstandhowtheirbusinessisdoingandfindareasthatneedattention.Dataanalyticsisthekeytomakinggooddecisionsbecauseitshowswhetherabusinessis movingintherightdirectionornot.Skilleddataanalysts,therightsoftware,and therightinfrastructurewillhelptofindmarkettrendsandexplainwhyoneproductorserviceisdoingbetterthananotherthatmaynotbedoingaswell.
Dataanalysiscanuncoverusefulinsightsforbusinesses.Dataanalysisincludes text-analytics, statistical analysis, diagnostic analysis, predictive analysis, and prescriptiveanalysis.Textanalysisusesmachinelearningandnaturallanguage processing(NLP)tomakesenseofunstructuredtextdataandgetinsightsfromit. Textanalysislooksattrendsandpatternstofindoutwhysomethinghappenedin adeepandfocusedway Collecting,analysing,modelling,interpreting,andpresentingdatausingdashboardsareallpartsofstatisticalanalysis.Ittellsthestory ofwhathappened.Therearetwotypesofstatisticalanalysis:descriptiveanalysis andinferentialanalysis.Descriptiveanalysiscanbedonewithwholesetsofnumbersorwithgroupsofnumbersthathavebeensummedup.Itdoesn'ttrytofigure outwhatwillhappeninthefuture.Instead,itlearnsfromthepastbymanipulatingdatainwaysthatmakeitmoremeaningfulandthereforemoreuseful.Inferentialanalysislooksatsamplesthataremadefromthewholesetofdata.Inthis typeofanalysis,thesamesetofdatacanleadtodifferentconclusionsiftheanalystchoosesdifferentsamples.Diagnosticanalysislooksforpatternsindataby usingwhatwe'velearnedfromstatisticalanalysis.Itgivesareasonforwhysomething happened. With this kind of analysis, analysts can use patterns found in older data to solve problems in the present. With prescriptive analysis, all of a company'sdataandanalyticsareputtogethertofindthebestwaytomoveforward.Itlooksatmanypossiblesituations,predictswhatwillhappenineachone, anddecideswhatacompanyshoulddobasedonwhatitfinds.
Dataanalysishasnumerousadvantagesovertraditionalmethods.Dataanalysis helpsbusinessesbyimprovingtheirwebsites,makingshoppingmorepersonal, keepingcustomers,researchingtheircompetitors,improvingemployeeperformance,makingoperationsmoreefficient,andmakingiteasiertokeeptrackof inventory
ImplementingDataAnalyticsinBusiness:
Deployingbusinessanalyticsisdifficult.Thisprogrammerequiresreorganising the organisation, attracting new professionals, and redesigning tactics and culture[4].Abusinessmustproperlydeployananalyticsprogrammetobenefitfrom it.Implementationcomprisesbuildingateamofanalystsleadership,integrating analytics into organisational culture through participation of stakeholders, employingbusinessanalyticsandintelligenceplatforms,andassessingneedsof anorganizationanditscapabilities.Organizationsneedtounderstandtheirgolas
Organizations need a process to get the most out of their data. This method involves:
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Decidingongoals
Getting,cleaning,andanalysinginformation
Ÿ Visualisingdataindashboards
Before establishing a system of business analytics, any organization must first documentitsneedsanddemands.Thiswillassistidentifyingthecharacteristics ofanalyticssystemsandproceduresthatneedtobedevelopedorbroughtin,as wellasthestaffthatneedstobehired.Theimmediatedemandforabusinessanalytics system is to be able to make decisions regarding the expansion of fleets basedonanalytics.Inordertoproperlyinformitsstrategyplan,thecorporation requiresabusinessanalyticssystem.
BuildingTeamofBusinessAnalysts:
Theappropriateteamofbusinessanalystscanturnhugeamountsoffactsandfigures into relevant business insights. Building an effective business analytics teamdemandsspecifictalentsandabilities.Mostexpertssearchforintellectual curiosity when forming teams. This trait cannot be taught or bought, but it's widelydesiredbecausetechnicalskillsaren'tenough.Organizationsshouldlook forapplicantsinvolvedinavarietyofprojectsandeagertolearn.Analystswith these traits look deeper into the data and ask more questions when analysing. Organizationsshouldnotbeshyawayfromhiringpeoplewiththistrait[24].
Organizations should be cautioned against simply hiring programmers Data analysis requires scripting in multiple languages, but management should also search for employees that can visualise data. Visualizing data can offer more insightthanotherways.Beingabletodescribehowafirmcanemploydataanalysis output is crucial to the analytics program's success. The analytics team shouldalsolookforoutthefinestdatastoryteller.Datastorytellingiscomplex and requires a business acumen. Data in its raw format is difficult to interpret, henceitneedsahumantouchtobeunderstood[24].
Whenbuildingananalyticsteam,considerusingdomainexperts.Theseprofessionals can translate firm data into actionable intelligence.These people make sure analytics insights make a difference in business operations. Experts say requiredexperiencemightrangefromsixmonthstofiveyearsinhighlyspecialisedareas.Thetimerequiredtotrainthesespecialistsisworthitforfirmssince theybringarealityperspectivethatdataanalysiscan't.Domainexpertiseshould bemovedbetweentheanalyticsteamandthelineofbusinesssotheymaylearn theday-to-dayoperations[24].
IdentifyingTalent:
Thefirmshouldchooseapplicantswithgoodprogrammingskillsas thefinest datascientistscanalterdatainadditiontoobservingandevaluatingit.Astatisticianreviewsandinterpretsdata,whileadatascientistcanupdatethedatacollectioncode.Organizationsshouldalsosearchforgoodcommunicators.Theseanalysts must communicatethe data's story and give visuals when possible.These candidatesshouldshowhowtheyimprovedacompanyprocessusingtheirwork. Someanalystssupplydatathatleadstocomputer-madealgorithms,whileothers leadtohuman-madedecisions.Goodcommunicationsareespeciallyimportant forhumandecision-makers[25].
Candidatesmustbeinnovators.Recruitersmustobservethattheseanalystscan think creatively as the business changes. Candidates who've used the identical techniquesinseveralfirmsmaylackinnovationskills.Goodapplicantsshould havebusinessknowledgetomatchtheirskillsofanalysisandmath[26].
IdentifyingtheSkillsinthedomainofBusinessAnalytics: Data analysts can have many skills and credentials. Candidates for these roles havevaryingtalentsandbackgrounds,buttherearesomebasics.Adataanalyst shouldknowMicrosoftExceltocorrectlystructuredata.Excel'sfeaturesmake dataadministrationeasy Unstructureddataisuselessforanalytics.Dataanalyst careers can overlap with arithmetic, statistics, or programming and software development.AllcandidatesshouldknowSQLandwebdevelopment[26].
DataanalystslearnRandPython.Pythoniseasytouseandidealforlargeprojects, while R is good for statistical computing and graphics. IBM and Oracle offervariouscertifications.Thesecertificatesreflectanalysts'thoroughknowledgeofthesesystems[26].
InvolvementofStakeholders:
There is no guarantee that a successful business analytics programme will be implementedifthepersonnelresponsibleforitisnotwell-qualified.Thesuccess ofananalyticaleffortcanbeinfluencedbythelevelofsupportandinvolvement fromkeystakeholders.CorporatestakeholdersareincludedinFig.1.Analytics mustbeadoptedbyallstakeholdersinanorganisation.Theheadoftheanalytics teammustbeabletocommunicateeffectivelyinordertospreadthewordabout the programme. Talk to the company's executives first. To secure support and funding,thepersoninchargeofanalyticsmustdemonstratethevalueofthediscipline[27].CEOs'aimsandneedsmustalsobediscussedduringthismeeting. Executives'trustinthesystemcanbestrengthenedanditsresultsusedindecision-makingbyanalyticsleaderswhorecognisetheirneeds[23].Theeffectiveness of programme integration relies heavily on executive support, but other membersoftheorganisationmustbeinvolvedaswell.Itisimperativethatbusinessandleadershipquantsengagewithexecutives,divisionalleaders,aswellas potentialanalyticconsumers.Theanalyticsstaffneedstotalktodataconsumers tofindoutwhattheyneed[23].Inordertomeettheneedsoftheirbusinessprocesses,theanalyticsteammustmeetoftenwithdataconsumersafterimplementationtoreviewtheprogram'sprogressortosuggestareasofweaknessthatmust beaddressed.Frequentlyaskingdatausersiftheiranalyticssupporthaschanged is an excellent idea. Stakeholder connections can be established by involving dataconsumersthroughouttheorganisationinthedevelopmentofanalytics.Asa resultofstakeholderinvolvement,dataconsumers'buy-inandcontinuingcommunicationfosterscreativeusesofanalysesandbusinessdecisionsbasedonanalytics.
lyticssystem'sbusinessaim.Todevelopasuccessfulanalyticsculture,employeesmustknowthevalueofanalyticsandthegoals.Undefinedbusinessgoalsare aleadingcauseofanalyticsprojectfailure.Best-in-classfirmsemployanalytics toanswerbusinessquestions.Generalcompanygoalslikegrowingincomearen't adequateandaren'tassuccessful[28].
Forananalytics-drivenculture,organizationsmustshareinformation.Multiple truths must be eliminated by siloing information. This can only happen when stakeholders share data and information openly and IT and business lines are aligned.ITteamsmustprovidebusinesslineswithneededinformation.Business divisionsmustworkwithITandeachothertoensuredatasecurityandcontrol. To make unbiased judgments for the organisation, all departments must exchangedata[28].
Morepeopleareinvolvedinanalyticsinorganisationsthataredoingwell.The term "citizen data scientists" refers to non-IT users who can be integrated into business processes. Analytics innovation will be driven by users. Businesses requireanalyticaltechnologiesthatdonotnecessitateacollegedegree.Analytics modelsandworkflowscanbebuiltutilisingreusabletemplatesinthebestsystems.Internetmarketsallowbusinessestotradeanalyticsmodels.Ananalyticsdrivenculturecanbefosteredthroughthis.Cultureswithastrongfocusondata analyticsmustusedatatoinformtheirdecisions.Itispointlessfororganisations torelyongutfeelingsorpreviousexperienceatthepreviousstages[28].
BestStatisticalPracticesandIdentifyingDatasets:
Theprocessofestablishingthebusinessrequirementsandusecasesforabusiness analytics system includes identifying the necessary datasets for analyticsbaseddecisionmaking.Withouttherightdataset,analyticaloutputswillbeirrelevanttodecisionmakers.
Effective decision-making is made possible by having the right information at hand.Threeelementsarerequiredforeffectivedecision-makingbasedonanalytics.Qualifiedstatisticiandiagnosisandevaluation[4].Firstandforemost,statistical qualifications are team qualifications for the analysis of data.The crew needs statistical training and expertise [4].They must be good communicators who can explain statistical models and results to top management and executives. Statistical models and approaches must also be understood by analytic teammembers[4].
Then,reviewstatisticaldiagnostics.Diagnosticstatisticsdeterminedataanalysis quality[4].Toassesstheanalysis'flawsorerrorsandthequalityandtrustworthinessoftheresults,thesemethodsareused[4].Themedianincomeofretireesin thearea,forexample,ordatafromacommunitycensus,canbedeterminedusing statisticaldiagnosticproceduresbyafirm.
Finally,doastatisticalreview Astatisticalreviewexaminesanalytics-baseddecision-making[4].Thereview'sscopeincludesdatacorrectnessandspeed[4].It furtherpointstowardsfittingoftheanalyticmodelsastheneedsandthedemands ofthebusinesswithrespecttothesoftwareanditstime-frameofgeneratingthe output[4].Theassessmentalsosuggestswaystoimprovetheprocess.Afterconsideringoptimalstatisticalpractisesteps,decisionmakerscanusebusinessanalyticsandfeelmoreconfidentintheirdecisions.
CONCLUSIONS:
CreatingBusinessAnalyticsCulture:
Inorderforbusinessanalyticstobesuccessful,theyneedtobeingrainedinthe cultureofthefirm[23].Accordingtoresearchconductedonbusinessanalytics programmes,companieswhodonotsuccessfullyincorporateanalyticsintotheir organisationalcultureareunabletomeettherequirementsoftheprogrammes.A robustculturethatisdrivenbyanalyticsneedstobedevelopedinordertoinvolve stakeholders.Itispossibleforbusinessesthatadoptacultureofanalyticstogeta competitiveeconomicadvantageovertheirrivalsbymorerapidlygainingdata insightandmakingbusinessdecisions.Companiesacrossawidevarietyofsectors are making significant investments in analytics. Because technological investmentsalonearenotsufficient,businessesneedtocultivateculturesthatare drivenbyanalytics[28].
Businessesthathaveoutstandingbusinessanalyticsprogrammesinvestinboth technologicaladvancementandacultureofanalysis.Aculturethatisdrivenby analyticscanbedevelopedwithinorganisationsbyfollowingthesefivesteps.A strongcultureofanalyticsneedstobeimplementedfromthetopdown,justlike anyotherbusinessprogramme.Inorderforanalyticstobeeffective,seniormanagementneedstoembraceandchampiontheiruse.Usersofthesystemofbusinessanalyticshavearesponsibilitytoguaranteethatseniorexecutivesarefully committed to making decisions based on analytics. Leaders who are sincerely dedicatedwillsetapositiveexamplefortheirfollowers[28].
Aculturedrivenbybusinessanalyticsensuresthatallworkersunderstandtheana-
Summarizing,businessesnowfacenewchallengesduetotheproliferationofbig data.Therehasneverbeenmoredatacollectedatsucharapidandintenserate, and business analytics is allowing companies to meet the increasing business demands that allow them to stay ahead of the competition.Analyticaljourneys aredesignedtouncovernewopportunities,connections,andinsightsthatwere previouslyoverlooked.
REFERENCES:
I. Y Liu,“BigDataandDataAnalytics”,JournalofBusinessForecasting,Winter 2014-2015,pp.18-21.
II. S. Kudyba and M. Kwatinetz, “Introduction to the Big Data Era”. Retrieved from https://www.modernanalytics.com/wp- content/uploads/2014/07/Chapter-1.pdf
III. B.H.Wixom,B.Yen,andM.Relich,“MaximizingValuefromBusinessAnalytics”.MISQuarterlyExecutive,2013,12(2),pp.111-123.
IV R. Bartlett, A practitioner’s guide to business analytics. New York, NY: McGraw-Hill,2013.
V C.Hsinchun,R.L.Chiang,andV C.Storey,“Businessintelligenceandanalytics:Frombigdatatobigimpact.”MISQuarterly,2013,36(4),pp.1165-1188.
VI. M. S. Hopkins, S. LaValle, F Balboni, N. Kruschwitz and R. Shockley, “10 Insights:AFirst look at The New Intelligence Enterprise Survey on Winning withData”,MITSloanManagementReview,2010,52(1),pp.21–31.
VII. Goewey,B.(2015,August21).5benefitsofusingbusinessanalytics[Webblog post]. Retrieved May 15, 2017, from http://www.datamensional.com/5- benefits-of-using-business-analytics/
VIII. Crossland, K. (2016, February 12). The 4 key benefits of business analytics. Techopedia. Retrieved May 15, 2017, from https://www.techopedia.com/2/ 30429/it- business/business-intelligence-bi/the-4-key-benefits- of-businessanalytics.
IX. WhyBusinessIntelligenceisKeyforCompetitiveAdvantage.(n.d.)Retrieved from: http://cisonline.bu.edu/news-resources/why-business- intelligence-iskey-for-competitive-advantage/
X. C.L.Jain,“HowtoUseBigDataandPredictiveAnalyticstoImprovetheSuccessofNewProducts”.ReviewOfBusiness,2016,37(1),48-55.
XI. G.N.Laursen,BusinessAnalyticsforSalesandMarketingManagers:Howto CompeteintheInformationAge.Hoboken,NewYork:Wiley,2011.
XII. DeZyre, How big data analysis helped increase walmarts sales turnover? Retrieved from https://wwwdezyre com/article/how-big-data-analysishelped-increase-walmarts-sales-turnover/109
XIII. SAS (2016) Four ways walmart uses analytics Retrieved from http://wwwsas com/en us/ insights/articles/ analytics/four-ways-walmartuses-analytics.html
XIV Cairns, R. (2003). Kmart selects business objects as its business intelligence platform.Retrievedfromhttp://www.businesswire.com/news/home/20030610 005293/en/Kmart-Selects-Business-Objects-Business-Intelligence-Platform
XV Vicki,L.P.(2002,Mar30).Somecustomersinraleigh,N.C.,areasaykmarts failtohavemerchandise.KnightRidderTribuneBusinessNewsRetrievedfrom https://csuglobal idm oclc org/login?url=http://search proquest com csuglobal.idm.oclc.org/docview/461833071?accountid=38569
XVI. Leinwand,P.,&Mainardi,C.(2010).Whycan’tkmartbesuccessfulwhiletarget and Walmart thrive? Retrieved from http://247wallst com/specialreport/2012/10/17/the-worst-business-decisions-of-all-time/3/ XVII. McIntyre,D.,Allen,A.,Weigley,S.,&Sauter,M.(2012).Theworstbusiness decisions of all time Retrieved from http://247wallst com/specialreport/2012/10/17/the-worst-business-decisions-of-all-time/3/ XVIII. Egan,M.(2015).Kmart’ssaleshavefallenoffagiganticcliff.Money.cnn.com. Retrieved from http://moneycnn com/2015/06/08/investing/kmart- salesdecline-sears-eddie-lampert/
XIX. Google. (2015). Google Analytics Case Study: American Cancer Society Retrieved from https://static googleusercontent com/media/www goog le.com/en//analytics/customers/pdfs/americancancersociety.pdf
XX. TheGuardian.(2013,Sep17).AbandonedNHSITsystemhascost€10bnsofar Retrieved from https://wwwtheguardian com/society/2013/sep/18/nhsrecords-system-10bn
XXI. Hefford,R.(2011,Dec2).WhyNHSNationalProgrammeforITdidn’twork. CIO Retrieved from http://wwwcio co uk/it-strategy/why-nhs-nationalprogramme-for-it-didnt-work-3431723/
XXII. Marr,B.(2015,Mar17).Wherebigdataprojectsfail.Forbes.Retrievedfrom https://www forbes com/sites/bernardmarr/2015/03/17/where-big-dataprojects-fail/#331ba245239f
XXIII. Rathore,S.,Panwar,A.,&Soral,P (2014).Criticalfactorsforsuccessfulimplementationofbusinessanalytics:Exploratoryfindingsfromselectcases.InternationalJournalofBusinessAnalyticsandIntelligence,2(2),12-22.
XXIV Waxer, C. (2013). 6 tactics for building and sustaining a data analytics team. Retrieved from: http://data- informed.com/6-tactics-building-sustaining-dataanalytics-team/
XXV Marr, B. (2016). How to build a big data and analytics team. Retrieved from: http://data-informed.com/how-to-build-a-big-data-and-analytics-team/
XXVI. Elango, R. (2016). How to build a successful data analytics career Retrieved from: https://wwwsimplilearn com/how-to-build-successful- data-analystcareer-article
XXVII. Ransbotham,S.,Kiron,D.,&Prentice,P K.(2016).Beyondthehype:Thehard workbehindanalyticssuccess.MITSloanManagementReview,1-16.
XXVIII. Rogers,S.(2016).Howandwhytobuildananalyticsdrivenculture.Retrieved from: https://upside tdwi org/Articles/2016/06/24/Building- AnalyticsDriven-Culture.aspx?Page=2
XXIX. Liu,Y.,Han,H.,&DeBello,J.(2018).Thechallengesofbusinessanalytics:successesandfailures.