big data cloud computing

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Post on 18-Jul-2015



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CLOUD ANALYTICS Take your cloud to the next level by monetizing on Big Data and Analytics ToolsDr Kumar Prasoon ( C.I.O Safeer Group)

In retail, all kinds of connected devices generate a flood of complex structured and unstructured data.

POS Device(Point-of-sale Systems, used for receiving payments)

Near Field Communication Devices (NFCs) - the technology being used to establish radio communication between devices by touching them together or bringing them in very close proximity

PDT Devices(Used for scanning barcodes, maintaining stock etc.)SOURCES OF DATA

RFID Chips

Video Surveillance SystemsSocial Media Sites Used in marketing of products, tracking customer behaviour, trends etc.Used for the purposes of automatically identifying and tracking tags attached to objectswith video analytics that record store traffic patterns, employee-customer interactions, and customer-merchandise interactions (such as the dwell time around an end cap)SOURCES OF DATAUSESMonitoring.Customer Trajectory.

HELPS US INPromotions.Shelf allocation.Shelf life cycle management.Product positioning



USESCustomer Mind map(GPOMS)Customer DemographicsCustomer preferenceBuying patternCustomer feedback

HELPS US INPromotionsEffective category managementProduct assortmentPersonalized customer service

PDTCAMERASSURVEY/LOYALTY CARDSAFEER MEDIACASH COUNTERBIG DATASMART DATASOCIAL NETWORKING SITESData SourcesFLOW CHARTAnalysisSupply AnalyticsPath to PurchaseCustomer AnalyticsRetail AnalyticsMerchandising LogisticsMarketingE-CommerceBehaviour AnalyticsPurchase patterns from point of saleRetail AnalyticsWHY BIG DATA IS NOT JUST AN IT CHALLENGE?14Travel and TourismHealthcareAutomotiveBig Data AnalyticsRetail AnalyticsSecurityTraffic Management

15 Smart Data in SecurityPrioritizing threatsStopping crime in its tracksVisualizing threats Data uses the information that customers are already generating to provide travel companies with better more targeted and customized ( and ultimately more profitable) services and products Smart Data in Travel & Tourism

To gain new insight in patient care &early indications of diseaseSmart Data in Healthcare

Smart Data Analytics in Traffic Management

To improve the everyday life entangled due to our most common problem of sticking in trafficSmart Data in Automotive Industry

Optimizing supply chains by ensuring that all components, parts have adequate stock to meet the anticipated demand for replacement parts by predicting when they might fail, how many might fail and where

Analyzing vehicles in the field to predict/anticipate maintenance associated with specific vehicles

Monetizing gathered data by selling raw data to rental companies, insurance companies, public services providers, etc. and selling reworked and aggregated data to weather companies, web analysts, etc.CLOUD TOOLS USED FOR ANALYTICSPentaho Pros(+)Low Cost - Open Source ProjectDashboards and VisualizationBusiness Query Ad-hoc ReportingPredictive Analytics IntegrationPixel Perfect FormattingApplication Integration APIAdvanced Security CapabilitiesMobile Reporting App

Cons(-)Requires technical resources for implementationRequires plug-ins to compete with commercial productsUser interface will be less intuitive out of the box without customization

Pros(+)Data Volume and ScaleBusiness Query Ad-hocPixel Perfect FormattingScheduled DistributionDashboards and VisualizationPredictive Analytics IntegrationPortal Integration APIAdvanced SQL and MetadataAdministration AutomationAdvanced Security Capabilities

Cons(-)Specialty BI tools lead in VisualizationsSpecialty BI tools have a simplified user interfaceProduct works best with well defined data model

Pros(+)Data ExplorationAdvanced Data VisualizationsIntuitive End-User InterfaceWeb Based Report DevelopmentMash-ups non-structured data High business user adoption

Cons(-)Data ScalabilityLimited Scheduling/DistributionLimited Production ReportingLimited Integration APIsLimited Stats IntegrationLimited Administration Automation

Pros(+)Excellent Dashboard CapabilitiesEase of use for data mashupsLittle to no data modeling requiredRapid DeploymentEase of Use for End UserAuto-detect RelationshipsIn-memory BIData ExplorationCons(-)In-Memory Architecture Data ScalabilityLimited direct Query AccessLimited Scheduling/DistributionLimited Metadata/Object ReuseLimited Integration APIs Limited Administration AutomationEnterprise readiness and scalabilityNo Usages Stats integration

BI MagicQuadrant