International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025 www.irjet.net p-ISSN: 2395-0072
Modern Business Analytics through Cloud-Native Architectures: A Comprehensive Study Using AWS Data Lake and Machine Learning Services
Shiraz Anas Quadri Syed1, Shayesta Syeda2
1AWS Solutions Architect, Los Angeles, California, USA 2Student, Business Analytics, Hyderabad, Telangana, India
Abstract - With the fast-paced digitalization of contemporarybusinesses,dataisnotonlyeverywherebutitis key to informed decision making. As businesses grow, the demand for more scalable, flexible and intelligent analytics infrastructures have increased, and legacy on-premises systems have become outdated. This paper investigates how cloud-native architectures based on platforms like Amazon Web Services (AWS) are disrupting the world of Business Analytics through unlimited performance data lakes, automated data engineering pipelines or sophisticated machine learning capabilities
Based on a fusion of the business analytics frameworks and cloud engineering design principles, this paper analyses how AWS offerings (Amazon S3, AWS Glue, Lake Formation, Redshift, Athena and SageMaker) collectively fulfil tasks for descriptive,diagnostic,predictiveandprescriptiveanalytics.It combines two separate areas of expertise - data-driven business analysis and cloud-native architecture design –created together, but remotely.
The study’s results signify that AWS’s integrated analytics environment powers more scalable and cost-effective data processing, delivers faster insights, democratizes machine learning, and fosters decision intelligence across an organization.Together,thefindingsofourcombinedresearch, suggestthatcloud-nativearchitecturesforbusinessanalytics are a key step change in using data to drive competitive advantage and provide organizations with an infrastructure that is commercially mature as well as analytically sophisticated.
Key Words: Business Analytics, Cloud-Native Architecture, AWS Data Lake, Machine Learning, RealTime Analytics, Large Language Models (LLMs), Data Engineering, Predictive Analytics
1. INTRODUCTION
Digital ecosystems are rapidly transforming the way in whichorganizationscapture,storeandanalyze data.So,the explosivegrowth ofe-commerce,mobileapps,Internetof Things devices and cloud-based platforms have driven unimaginableamountsofstructuredandunstructureddata. Forbusinessescompetinginthisenvironment,analyticshas
evolved from a back-office activity to an organizational competencythatisfoundationaltocompetingonexcellence ornewcustomer-centricservices.Thisiswhythediscipline known as Business Analytics has become so prominent withintoday's enterprise,addressingtheneedforrawdata toevolveintoactionableinsightandpredictiveintelligence. Traditionalanalyticsinfrastructureusuallyrelyingonanonpremisesdatabase,fixedcomputecapacity,andbatch-style processing: The demand for real-time insights, cost efficiencyandmassivemachinelearningismoving sofast backendcanhardlykeepup.Asitturnsout,thesepressures havepushedthetrendtowardscloud-nativearchitectures (with elasticity, distributed processing and machine learningservicesbuilt-in)evenfurtheralong.AmazonWeb Services(AWS)offersarobustanalyticseco-systemcapable to store data, automate ETL, govern processes, visualize outputs and predict final outcomes all under one scalable environment.
This study is investigating the confluence of business analytics and cloud-native architecture, specifically how AWS allows businesses to transition from Traditional BI (Business Intelligence) to advanced decision intelligence. While this compendium represents two disparate professionalperspectives,onebasedonbusinessanalytics theory and the other based in cloud engineering the collaboration was remote, blending domain expertise asynchronously. This is consistent with the real-world scenarioofindependent projectsinvolvingcross-discipline collaborations betweendata team, architectsandanalysts frequentlyworkinseparategeographicallocations.
Contemporary business analytics frameworks generally consist of four foundational analytic types including descriptive, diagnostic, predictive and prescriptive. Data ingestion and organization must be robust at each layer with manageability of access and scalable compute. AWS helps these organizations via services like Amazon S3 for data lake storage, AWS Glue for serverless ETL, Amazon Redshiftforpetabyte-scalewarehousing,AmazonAthenafor interactive SQL querying and Amazon QuickSight for visualization. Advanced models use Amazon SageMaker, Amazon Forecast, Amazon Comprehend and Amazon Personalizetoput machinelearningatscaleintoaction.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025 www.irjet.net p-ISSN: 2395-0072
The combination of these capabilities provides a full analyticslifecyclethatallowsenterprisecustomerstoingest alltypesofdata,auto-transformpipelines,enforceandscale governance processes, deploy predictive models and turn insightsintobusiness-readyintelligence.Byfusingbusiness analyticstheorywithAWSarchitecturalbestpractices,we highlight howcloud-nativeenvironmentsraisethebaron analyticsagility,insightquality,andenterprisereflexes.
2. Literature Review
Mohajeri, M. A. (2024). Leveraging large language model for enhanced business analytics on AWS. AuthordelvesintohowLargeLanguageModels (LLMs)and cloud-nativeenvironmentscanhelpboosttoday’sgeneration of Business Analytics. Designed in partnership with a fast fashion company, the research shows exponential data growth has rendered traditional analytics inadequate; organizationsnowleveragecloudecosystems–notablyAWS -todeliverscalabledataprocessing,modeldeploymentand deliveringreal-timedecisions.Mohajerishowsthatpowerful AWS services like Amazon SageMaker make it quite easy nowtoimplementadvancedLLM-poweredanalyticsthrough simplified data preparation, feature extraction and text analysis.
Author also compares AWS against Google Cloud Platform (GCP)andMicrosoftAzureandconcludes,thatalthoughthey allhaveattractiveanalyticalofferings,noneofferthesame cohesive end-to-end analytics platform as AWS does. BuildingonthebroaderBusiness Analyticsknowledgethat integrated cloud environments drive faster insights and supportsbetterstrategicdata-drivendecisionmaking.
Thallam, N. S. T. (2023). Comparative Analysis of Public Cloud Providers forBig Data Analytics: AWS, Azure, and Google Cloud. International Journal of AI, BigData, Computational and Management Studies, 4(3), 18-29.
The author of provides in-depth examination of big data analyticscloudplatformsatenterpriselevel.Usingliterature review, benchmarking experiments as well as real-world case studies of Netflix (AWS), BMW (Azure) and Spotify (GCP), the authors demonstrate how each provider has varying strengths which corresponds to differing organizational requirements. AWS shows best scale and integration with mature ecosystem of tools while offering solid performance for workloads at a great scale; Azure proves to be strong place in the enterprise space deeply integratedproductbyMicrosoftandhasgood compliance support;GoogleCloudbeingAI&MLpowerhouseleadson real-time data processing and analyzation. Thallam found that there isn’t a single cloud provider that is best for everything workload patterns, cost needs and security opportunitiesshouldguidewhichclouds agenciesselect. Findings of this study support general Business Analytics literature that relies on flexible and scalable cloud
infrastructures to accommodate high volume data processing and advanced analytical models. And, importantly, the research highlights trends such as multicloudadoption,afocusonsustainabilityandAI-ledsecurity monitoring–allofwhicharecomingtotheforeforBusiness Analytics teams who want to blend high-performance computing capabilities with governance and cost control. Through demonstrating the variations in analytics capabilities of cloud platforms, Thallam (2024) offers a strategicperspectivefororganizationstoaligntheirchoice ofcloudwiththeiranalyticaland operatingneeds.
Gatlin, K. (2024). Real-Time Analytics on Amazon Web Services and Google Cloud: Unlocking DataDriven Insights.
Real-timeanalyticsisalsobecomingamoreimportantarea, especiallyasmoreorganizationsrelyonreal-timeinsightsto compete in the fast-paced digital world (Gatlin 2024). In Gatlin's view, batch-based analytics is simply insufficient anymore˙especiallyinsectorssuchasfinance,e-commerce and healthcare that hinge on the ability to rapidly detect trends,anomalies,anduserbehaviorsandcanchangetheir operationaldecisionsasaresult.
AWS solutions including Amazon Kinesis, Redshift, and QuickSight and Google Cloud solutions such as BigQuery, Pub/Sub,andLookeralloworganizationstoingeststreaming data, process it with low latency, and visualize insights in real-time. This resonates with wider Business Analytics literature classifying on-time data processing as a major building block for agile decision-making, great customer experiencesandprocessoptimization.
Methodology & Architectural Approach
Thisworkpresentsahybridmethodologicalprocessbetween theoreticallyBusinessAnalyticsandhands-onarchitectural validationgroundedinAWScloudengineering.Inline with theinterdisciplinarynatureofourfocus,thisapproachwas structured to combine theoretical concepts and practical cloud-nativeimplementationsformodernanalyticsatscale on AWS. The participatory research style for this work (entirely remote between two researchers with complementaryskills)reflectsdistributedcollaborationthat is increasingly the norm in both analytical and cloud engineeringpractices.
TheBusinessAnalyticsperspectiveadoptedbytheapproach is hingedon,andextensivelyleansonthefourlayersofan analyticalmodel:
Descriptive
Diagnostic
Predictive
Prescriptive
This framework provides a reference point to enable the evaluationofAWScapabilitieswithoutonlyfocusingonthe technicalaspect,butalsowhateacharchitecturecomponent offersintermofanalytics.TheBusinessAnalyticsresearcher addedperspectiveondecisionscience,statisticalmodeling
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025 www.irjet.net p-ISSN: 2395-0072
andtheenterpriseanalyticslifecycle toputintocontexthow dataneedstobeshaped,processedandexplainedifitisever todrivebusinessvalue.
From an engineering perspective, the AWS architectural practicederivedfromthe AWSWell-ArchitectedFramework and focused on operational excellence,security, reliability, performanceefficiency,andcostoptimization.Thisinvolves theassessmentofAmazonS3whenusedasprimarydatalake storage, AWS Glue for serverless ETL, Lake Formation for governance, Redshift (for analytic querying), Athena (for interactive SQL), and SageMaker (to orchestrate your ML models). The architectural layouts have been established throughamixofAWSreferencearchitecture,bestpractices andactualuse casepatterns.
Thetwomethodologicalstrandsconfluenceiterativelyrather than concurrently, with researchers producing written analyses to be incorporated later. This was highly asynchronouswhichmeantthatdecisionmakerscouldboth work in parallel, interpreting the problem from their own domainwithoutbiasorinfluence.However,theinsightsfrom each section could only be combined to tell a coherent architecturalstoryabouthowAWStechnologiescanaddress BusinessAnalyticsgoalsoncetheywere‘grounded’intheir homediscipline.
Cloud-Native Architecture for Business Analytics
Contemporary Business Analytics heavily depends on fast processing, unification and interpretation of massive amountsofdataprovidedbydifferentsources(Dutta,2019). Legacyanalyticalenvironments,whicharebasedaroundonpremisesdatawarehousesandsiloeddepartmentalsystems, cannot deliver the speed and scale needed for modern analytics. These restrictions have resulted in a significant paradigm shift towards cloud-native architectures that providedynamic scaling,distributedcomputational power andintegrabilitytobigdataanalytics.
Froma businessanalyticsstandpoint,thereasonforgoing cloud-native is not just about technology or digital transformation;it’saboutbeinginanenvironmentinwhich insights are created quicker, models can be iterated more rapidly.BusinessAnalyticsshouldinvolveflexibilityandnew sources of data, the discovery of unexpected patternsand knowledge, new business questions and rapid experimentation.Cloudnativeisdesignedtodirectlysupport such requirements with its scalable compute, componentbased services and metered usage of resources. These qualities allow analysts to go further than historical reporting and perform more sophisticated predictive and prescriptiveanalytics.
Cloud-native concepts like container pattern, serverless running and distributed storage have direct influences on major BA results – data freshness, analytic accuracy and
insights resident. In practice, this compares to liberating ourselvesfromfixedhardwaredependenciesandbatch-only processing, which empower us to frequently retrain our models or update a dashboard near real time or querying overlargedatasetinteractivelywithoutperformancepenalty. Allofthisinturnmaximizesthevaluederivedfrombusiness analyticsfordecisionmaking,forecasting,optimizationand strategicplanning.
We present AWS here not as the focus of this study, but merely as an example of what aspects cloud-native infrastructures can make available to analytical functions. Services such as Amazon S3 (storage), AWS Glue (data transformation), Athena (ad-hoc querying), and Redshift (datawarehouse)constituteasolidfoundationthatfitswell intotheBAprinciple.Buttheprincipledbasis isstillBusiness Analytics:cloud-nativesystemsmatterbecausetheyenable analysts notsimplybecauseoftheirtech.
Bypoweringscalabledataingest,schema onreadflexibility and distributed compute frameworks, cloud-native architectureseliminatemanyofthehurdlesanalystsfacedin the past. As we continue our work together, AWS’ differentiator is the way it accelerates and elevates the Business Analytics lifecycle: richer descriptive options, diagnostic insights using more advanced models, detect & adjust to trends in an automated manner with predictive pipelines,andrealizeprescriptiverecommendationsthatare actionable.
AWS Data Lake & Data Engineering Layer Format
Thecapabilitytodealwithmassive,heterogeneousandupto-date datasets is crucial in recent Business Analytics. Regardless of whether analysts are doing revenue forecasting,predictivemodelingconsumer behavioratscale, supplychaindiagnosticsorriskanalysis–thebedrockofall analyticsremainsgooddataandlotsofitdoneinastructured way (Zdraveevski et al, 2020). This is why the data engineering layer, which collects, preps, organizes and polices data is one of the most important pieces of mind infrastructureforBusinessAnalytics
Withtraditional datawarehousingsolutions,businessesare handcuffed to strict schema requirements and long developmentcycles,andunabletoeasilyincorporatesemistructured or unstructured data from sources like logs, clickstreams,socialmediaandsensordata.Theselimitations would frequently hinder analytic workflows and limit the kindsofinsights theycouldgenerate.BusinessAnalytics,on theotherhand,needstobeflexible,analystsshouldhavethe freedom to query across data types, iterate over new hypotheses rapidly and bring in additional sources as businessquestionschange.
That is why cloud-native data lakes have emerged as the optimal basis for Business Analytics: Schema-on-read, separatedstorageandcompute,cost-efficientscalability.This isagenericpatternandAWS isonlyoneofmanypossible
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025 www.irjet.net p-ISSN: 2395-0072
examples. Amazon S3 as implemented today into BA tool landscapesisnotpopularbecauseitisanativeAWSresource, rather it makes a great centralized platform for analyst to dumptheirleftandrightrowdatainahugehoardwithlittle structure(whichisessentiallythebusinessrequirement)at low cost. It’s the concept that matters: Data lakes allow analyststopostponethedecisionaboutdatamodelinguntil analysistime,whichpreservesasmuchanalyticalfreedomas possible.
But even the lighthouse data lake isn’t enough. Therefore, many aspects and challenges of ETL (such as data quality, data consistency, and data integration) are relevant to BusinessAnalytics.Automationisverynice,andcloud-native ETL services like AWS Glue are proving that transforming databetweendimensionsandloadingitintoacatalogcanbe madetohappenwithoutanyonewritingcodeatall.
The governance layer is also vital in Business Analytics particularlyintheregulatedsectors.AWSLakeFormationisa good example of how contemporary cloud-native tools improve governance by enabling fine-grained access controls,dataprovenance,auditabilityandencryption.These capabilities help ensure that analysts are working with secure,compliant,well-manageddatasetsbreakingthe myth ofnotbeingabletoun-silodatathatissensitiveormission critical.
Afterdatahasbeensavedandprepared,analystsneedfast querying applications to explore patterns and derive meaning.WhileAmazondeclaredwarondatapreparation, the Alexa Platform did more than that to accelerate the BusinessAnalyticsprocessinbothdiagnosticanddescriptive phases with tools like Amazon Athena (serverless SQL querying) and Redshift (data warehousing). They power analysts to execute sophisticated queries against big data sets in seconds rather than a day, allowing for deeper exploratory and inferential analysis. Crucially, these performance improvements provide direct benefits to BusinessAnalyticsoutputsintermsofcorrectness,speedand depth.
Andtogether,thesecloud-nativedataengineeringlayerhelp the entire Business Analytics lifecycle from easier availability,higherqualityandgovernanceofdataanalytics to more accessible analytical abilities. AWS is used in this analysis to illustrate the technical side of these improvements,buttheheartofthematter(improveddata pipelines→improvedinsights→ betterbusinessdecisions) isunchanged.
Table -1: How Data Engineering Enhances Business Analytics
BA Requirement CloudNative Capability
Flexible exploration Schema-onread
Fasterinsights Automated ETL
AWS Example BA Requirement
AmazonS3
Flexible exploration
AWSGlue Fasterinsights
Data governance Unified access control Lake Formation Data governance
Interactive analysis
High-speed SQLqueries Athena/ Redshift Interactive analysis
AWS Machine Learning Layer
MachineLearninghasbecomeanessentialcontinuationof advancedBusinessAnalyticstoday,renderingcompaniesto further advance from standard reporting practices into extendingpredictive andprescriptivefortes.Intoday’sageof data-obsession,youarenotjustexpectedtoexplainhistorical performance but predict future outcomes and uncover hiddentrendsinlargepopulations,suchascustomergroups and strategic decision impacts. And meeting such goals necessitatesscalablemodelingenvironments,reliabledata pipelinesandautomatedworkflowsallofwhicharebolstered bycloud-nativeinfrastructure.
Intheabovecontext,AWSislistedherenotbecauseit'sthe mainsubjectbutratherasanexampletoshowhowcloudhosted ML services can enhance the Business Analytics lifecycle. The focus still is heavily on analytical use: forecasting, classification, anomaly detection, optimization andevennaturallanguageunderstanding.
Enhancing Predictive Analytics
BuildingpredictivemodelsPredictiveanalyticsisakeypart of BusinessAnalyticssystemsandpredictingsuchthingsas demand,pricingstrategy,inventoryoptimization,financial risk.AmazonSageMakeroffersaplacewherethesemodels canbetrained,tunedanddeployed atscale,withthevalue beinglinkedtoitsbusinessoutcomes.
Analysts, for example, can build regression or gradient boosting models or deep learning models that predict customer lifetime value or forecast monthly sales using SageMaker. It’s not about the platform itself – it’s about reducingtime-to-insight:youdon’twanttobefiddling with serversorevensettingupacomplexinfrastructuremanaged environmentsenableBusinessAnalyticsteamstoiterateat tapspeed.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025 www.irjet.net p-ISSN: 2395-0072
Prescriptive Analytics
Prescriptive analytics is about making the best possible decisions – which can mean recommending products, adjusting marketing & budgeting plans and reallocating resources. AWS services like Amazon Personalize (recommendation systems) demonstrate how machine learningmodelsembeddedintoapplicationscouldinfluence real-time behavioral data to make decisions. But from a BusinessAnalyticsstandpoint,thetruevalueisbasedonhow these models allow organizations to better optimize for results,bemore personalizedanddevelopsoundstrategies.
Expanding Diagnostic Analytics Through NLP
Naturallanguageprocessinghasstartedtogaininpopularity and importance in Business Analytics particularly with applications for customer sentiment analysis, product reviews, company communications/postings or customer support transcripts. Businesses can have all these NLP prebuiltfunctionsinAmazonComprehend thatcanextract entities,sentimentsandtakeabouttopicsbutthepointisnot thefunctionalitiesagain:givenasetoftextananalystmight unveil qualitative insights over traditional numeric data. Thesenuggetsofwisdomcansometimesbegamechangersin diagnosingwhatisbehind customerbehaviororoperational trends.
Advancing Forecasting Accuracy
Predictingfuturebasedonhistoricaldataisoneofthewidely usedtaskinBusinessAnalytics.AmazonForecastabstractsa great deal of the complexity of sophisticated forecasting models,butitssignificanceisinenablingbusinesssensitive workloads:inventoryplanning,staffingprojections,revenue and supply chain forecasts as well as financial projects. Increased forecast accuracy leads directly to improved strategicplanningandreduceduncertainty.
Integration With the Analytics Lifecycle
What really makes cloud-native machine learning so compelling for Business Analytics is that its upstream and downstreamworkflowsarehookedin.Automatedpipelines, retraining triggers, scalable inference end-points and continuous monitoring facilitate moving from descriptive analysis to predictive modeling to prescriptive recommendations.Bydoingthis,businessanalystscanuse thesetoolstooperationalizeinsights,sotheydonotmerely serve as a source of intelligence for decision makers but rathergiveshapetothedecisionsaswell.
As such, AWS’s machinelearning layer is discussed in this reportnotasanendgamescreenshotbutas theexemplarof howcontemporarycloud-nativetoolsisservingthedeeper mission:tohelporganizationsgetevenmoresophisticated modeling into the hands of their people rapidly and responsively.
Practical Applications
In order to illustrate the practical relevance of Business Analytics in current business environment, I will focus on real-world examples where analytical methods influence decision-making with cloud-native technologies being the basisforamoreflexible,scalableandrobustimplementation. Thetechnicalblueprint,whileAWS-centricisessentiallyall about the Business Analytics tasks (Forecasting, Segmentation,RiskModellingandOperationalOptimization) that drive business value that we can map to real PnL numbers.
Theseexamplesillustratewellhowourresearch approachis also collaborative: one researcher contributes analytical interpretations, and the other offer’s architectural view of howcloud-nativesystemsrealizetheunderlyingworkflows. They are distant and individual, but the two ends meet through how Business Analytics thrives on such modern datainfrastructures.
Retail Demand Forecasting
Retailers rely heavily on precise demand forecasts to maximize inventories, minimize stockouts, and enhance revenue planning. Business Analytics takes the lead in applying statistical and time-series forecasting models to historical sales, seasonality, promotions and external influences(weather,market indexesetc.).
In practical use,thisBAprocessisfast-trackedinacloudnativeenvironmentbecauseanalystsareallowedto:
Accesslargesetsofsalesdatafromcentralizeddata lakes.
Runforecastmodels atthescalewithflexibility.
Automaticallyupdatethemodelasnewdatacomes inCustomerSegmentationforMarketingAnalytics.
Marketingdepartmentsusecustomersegmentationtofind differentbehavioralgroupsfordirectmarketing,todevelop better products and reach new customers or clients. BusinessAnalyticsisthestatisticalendofmost marketing segmentation, including Kubernetes clustering, RFM analysis/segmentationanddemographicprofiling.
Cloud-nativesupportallowsanalyststo:
Combinebehavioral,transactionanddemographic information.
Scaleautomaticclusteringtomillionsofrecords.
Makesegmentsrealinliveoperationalapplication layer.
AWSserviceslikeSageMakercanbuildanddeploysegments atscale,butit'sabusinessinterpretation(segmentpriorities, messagingstrategies,personalizationrulesandengagement metrics)thatdefinesthe marketingstrategy.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025 www.irjet.net p-ISSN: 2395-0072
Supply Chain Optimization
Logistics, Transportation, Warehousing and Procurement Supply chains produce a large volume of structured and unstructured data. Business Analytics is a key driver in arrangingrerouting,minimizingdelaysandbalancingsupplydemandvariabilities.
Byusingcloud-nativeanalyticenvironments,BAteamsare able to:
Unify IoT data from sensors, shipment logs, and inventorydatabases.
Trainoptimizationalgorithmsatscale.
Simulatedifferentsituations(theexamplecouldbe supplierfailure,routeblockade).
Buildequipmentpredictivemaintenancemodels.
AWS services can accommodate these workloads, but the optimization logic such as linear programming, simulation modeling, or probabilistic forecasting originates with BusinessAnalyticsprocedures.
SaaS User Behavior Analytics
BusinessAnalyticsfortheSoftware-As-A-Servicescompanies to track how usersare using your product identify risk of churnandimproveusageengagement(Rameshetal,2025). Typicaltoolsusedbyanalystsincludefunnelanalysis,cohort analysis,churn predictionmodelsanduserlifecyclemetrics.
Cloudnativefeaturesmake thiseasierwith:
scalablepipelinesfortrackingevents.
rapiddataquerying
automatedpredictivemodelsfor churn.
productteamreal-timedashboards.
Table -2: MappingBusinessAnalyticsFrameworkto Cloud-NativeSupport
Business Analytics Layer Purpose CloudNative Strength AWS Example
Descriptive Summarize what happened Fast querying, unifieddata Athena/ QuickSight
Diagnostic Explainwhyit happened Integrated datasets, scalable compute Redshift/ Glue
Predictive Forecastwhat willhappen
Parallel ML training, automation SageMaker/ Forecast
Prescriptive Recommend optimal actions Real-time model deployment Personalize
Table -3: BusinessAnalyticsUseCaseswithCloud-Native Support
Business Analytics Use Case BA Technique CloudNative Value AWS Example
Retail Forecasting Timeseries modeling Automate dML, scalable compute Forecast
Customer Segmentati on Clustering, RFM Real-time deployme nt SageMaker/Personal ize
Fraud Detection Anomaly detection Streaming inference Lambda/SageMaker
Supply Chain Optimizatio n Optimizati onmodels IoT+ simulation scale IoTCore+Redshift
User Behavior Analytics Cohort analysis Fast queries, dashboard s Athena/QuickSight
Discussion
Theresultofthisstudyclearlyillustratesasynergybetween Business Analytics and cloud-native architectures: the efficacyofanalyticsisnolongeronlydeterminedbymodels and frameworks, but also by the infrastructure that hosts them. Cloud-native environments like AWS do provide increasedscale,integration, andautomationbutthatvalueis powered by principles of Business Analytics statisticsbasedreasoning,decisionscience-basedmodels,forecasting logic, and segmentation theory optimization frameworks (Minichino,2023).
Our hybridization of Business Analytics architectures and cloud-native design patterns evidence that the Cloud isan accelerator, promoting high performance. Traditional barriers like hardware limitations, long ETL cycles, siloed datasets,oreven limitedcomputeresourcesareremoved. Cloud-nativesystems effectivelyenable BusinessAnalytics teams to derive insights faster, iterate on models more cleanlyandbroadentheiranalyticalhorizons–enablingthem to analyze streaming data, parse unstructured inputs and conductlarge-scalemachinelearningallat massivescales. Conceptually, this implicates all 4 layers in the Business Analyticslifecycle.
Descriptiveanalyticsgetstimelierandmoreaccurate,asthe cloud-nativesystemscananalyzefreshdataonanongoing basis allowing execs to make decisions based on current conditionsinsteadofbasedoldreports.
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Diagnostic analytics widensinscope,withanalystsbeing enabled by disparate data sets and scalable compute resourcestodigdeeperintocauses,relationships,andeffects.
Predictive analytics becomes more trustworthy and actionable,becausecloud-nativeMLenvironmentsboasteasy model retraining, performance monitoring, and deployment shorteningthetimebetweenexperimentsand real-world utilization.
Prescriptiveanalytics getsmorepracticalascloudservices can build predictions right into decision-making streams, recommendation systems, optimization engines and workflows.
However,the conversationalsoidentifiesvariousobstacles organizationswillneedtoovercometoeffectivelycapitalize onBAinacloud-nativeworld.
Key Challenges and Considerations
Data Governance and Quality
The data and analytics team still carries most of the responsibilityfordataquality,consistencyandgovernancein cloud-nativesystemsthatmakeitrelativelyeasytostoreand accessthesethings.Cloud-readydoesn'twork withoutgood governance,regardlessofhowwellarchitectedyourcloud may be. Business Analytics needs dependable and trusted data,onewhichleadstotheneedofcorporationsinvesting heavilyindatastewardship,accesspolicies,validationrules.
Skill Gap between Business Analysts and Cloud Engineers
Onefrustrationwesawinourjointeffort reflectsthereality of internal work dynamics: analysts and cloud engineers oftenhavedifferenttypesoftechnicalconversation.Analysts careaboutbusiness outcomesandstatisticallogicengineers want to optimize architectures and pipelines. Successful integration needs better communication between crossfunctional teams, shared documentation and the right toolingtoworktogether.
Cost Management and ROI Interpretation
Cloud-native systems are cheap, but not free. Business Analyticsleaderswillneedtomatchanalyticalworkloadsand strategic value to keep compute, storage and ML deploymentsfinanciallydefensible.BAteamsarefrequentlya key elementinmaximizingtheuseandmeasuringthereturn ofananalyticsinvestment.
Organizational Readiness
Cultural changeisneededwhenmovingfromclassic BIto advancedcloud-nativeanalytics.Executiveswillneedtotrust onpredictivemodels;datascientistswillhavetousemoreof thoseautomatedMLpipelinesandanalystsshouldleaninto new workflows and tools. As such, Business Analytics maturityisajourney onbothtechnicalandorganizational fronts.
Integrated Insight
Viewingtheresults,thisdiscoursesupportsthemainfinding ofthis study:
BusinessAnalyticsisanorecticallytheintellectualheart,and cloud-nativearchitecturesaretechnicallyits megaphones. The AWS example above illustrates how today’s cloud platforms can eliminate totems of operationalundifferentiatedwork,allowingBusinessAnalyticstooffer deeper insights, reduce time-to-cycleand effectively touch more people in an organization. But Business Analytics theoriesand frameworksandinterpretiveskills,stillmatter profoundly, technology speeds up the analysis; it doesn't determineit.
Conclusion
The aim of this research was to investigate the impact on modern Business Analytics by embracing cloud-native architectures,takingtheAWSplatformasapracticalexample on how to build flexible IT infrastructure for analytical workloads.Eventhoughthecloudhasgivenusthemeansto massive scale computing – machine learning (ML) automation and real-time insights delivery, all of these analytic values still hold firmly on core Business Analytics theory.Thesethemesofdescriptivesummaries,diagnostic investigations, predictive modeling and prescriptive optimizationstill informhoworganizationsmakesenseand sensemakingofdata.
Ourjoint,remotework-basedstudyprovidedevidencethat Business Analytics is heavily relying on cloud-native technologies and is not tied to only one vendor or technology.Insteadofthecloudbeingabottleneck,in effect, itactsasanacceleratoreliminatingconstraintsthatanonpremises system would place on the analyst and allowing analysts to do deeper work more quickly and more iteratively.
The key characteristics of cloud-native–elastic compute, adaptative storage, distributed processing, serverless automationandintegratedmachinelearningbyprovidingthe neededworkflows tosupportBusinessAnalyticsobjectives suchasimprovingdataavailability(inordertobetterinform decisions),increasingmodelscale(toletanalystsexperiment with broad data sets) and enhancing decision-making precision.
The real-worldcasestudiescoveredforecasting,customer segmentation, financial risk modeling, supply chain optimizationandSaaSuseranalyticsexemplifyhowawide range of applications have enabled different industries to employBusinessAnalyticsforoperationalefficienciesatthe sametimeasstrategicgain.Andinallinstances,thecloudis justservingas theinfrastructureplatformtosupportthese analyticsapproachestoexecuteatinternetscaleandvelocity.
Thestudy alsoidentifiedcontinuinghurdles.Foranyofthem moving to cloud-based analytics, Data governance, tunes between the talents of Analysts and Cloud Engineers, Organizationsreadiness,andFinancialReturnareallcritical factors. These challengeshighlight the need for more than
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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justcutting-edgetechnologytoguaranteeBA'ssuccess; an intelligent strategy, partnership, and expertise are imperative.
Attheendoftheday,datashowsthatBusinessAnalyticsis CloudArchitecturenotsomuchthedrivingforcebehindthe data-driventransformation!Cloud-nativeenvironmentsuch as AWSamplifieswhatBusinessAnalyticsteamscandeliver intermsofdynamic,trustworthinessandimpactfulinsights. Analytical Foundation + Cloud: The new wave of DQ In a world growing richer in data heterogeneity, the interplay betweenGoodAnalyticsandFlexiblecloudswilldictatewhat makes-up next-gen decision intelligence. With this comprehensive view, business can not only interpret the past with data but also design the future with clarity and confidence.
REFERENCES
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[3] Gatlin, K. (2024). Real-Time Analytics on Amazon Web Services and Google Cloud: Unlocking DataDrivenInsights.
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[5] Minichino, J. (2023).Data Analytics in the AWS Cloud: Building a Data Platform for BI and PredictiveAnalyticsonAWS.JohnWiley&Sons.
[6] Zdravevski,E.,Lameski,P.,Apanowicz,C.,&Ślȩzak, D.(2020).FromBigDatatobusinessanalytics:The case study of churn prediction.Applied Soft Computing,90,106164.
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BIOGRAPHIES
The lead author is an AWSaccredited Cloud Solutions Architect-focusedoncloud-native data engineering, analytics pipeline and machine learning services. Currently her work extends to building scalable AWS architectures to serve the Business Analytics and data for decision making.
The second author is a BA (BusinessAnalytics)studentandis interestedindata-drivendecisionmaking, cloud analytics and streaming data She brings to the research knowledge and analysis of modern BA methods. The lead author is an AWSaccredited Cloud practitioner focused on cloud-native data engineering,analyticspipelineand machine learning services Currently her work extends to building scalable AWS architectures to serve the Business Analytics and data for decisionmaking