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Saama-POI Webinar Slides FINAL 04.27.2016 dm

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Data Can Be Simple Right?CaptureIngestExtractAggregateCleanseVisualizeAutomateMigrateStrategyRepositoryQuality AssessmentGovernanceArchaeologyCorruptionQuality AssuranceHarvestHarmonizeStorageQuality AssuranceLakeProcessMapSecurityAuditMachine LearningOptimizeManageVirtualizeWarehouseRecoveryProliferationScience

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CPG data sources Wealth of Potential & ChallengesTraditional Data SourcesSyndicatedPOSShipmentsSpendingEmergingCrowd-sourcedPanelRetail ConditionsDigital Promo TestSocial ListeningMany Others

Re-purposed Data SourcesPanelCOGsWeather

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Data Stages

AcquisitionIntegrationStorageAnalyticsDecisions

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INTEGRATIONALIGNMENTANALYSIS

VisualizationSecurity and Access ControlComplete Load Automation

DataMart

BI CubeExternal Data SourcesInternal Data SourcesPOS Data Sell-Thru, Sell-OutSyndicatedSocial & sentiment DataERP SAP; Shipment, Pricing, Sell-inTPMSEDW/BWMaster Data, Hierarchy, Price bracketsPEA DALSecurity and Access ControlAutomatic Mapping EngineData HarmonizationLoadValidateCleanseTransformAPIsSaama Fluid Analytics Cloud Data Integration EngineConfigurable Confidence % ThresholdsRules EngineConfigurable Business RulesAudit & Error Handling

Run ETL JobsAcceleratorsPEA Admin WorkbenchCustomer & Product Mapping (override auto mapping)Input Data Adjustment Event Dates, Shipped Volume, Price & COGS Updates to PEA Merged:Outlier Removal, Reported/ Non-Reported , Shopper Marketing SpendMapped & Un-Mapped Products, CustomersMissing and Misplaced EventsHarmonizer Merging ExceptionsCleansing and Business ExceptionsAdmin FunctionsManual Override

Ad-hocSelf Service BIPEACanned Reports, Executive & Analyst Dashboards, and Foundation for Predictive & Prescriptive analytics

Data Feed

Data Architecture

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Data Storage and Access

Lost at Sea or Calm in a Data Lake?

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Analytical Methodology

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Drive Strategic Agreement on Business Objective(s)

Incremental Revenue/TurnoverIncremental Profit / ROIVolume / % LiftMarket Share

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How will each use the system, and maintain consistency of interpretation?Joint Business Planning which data to share with retailerUnified planning processField awareness/adoption/incentive to provide accurate dataStudy and act upon results, provide diagnostic interpretations

Stakeholder Management / Roles

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Change Promotional Tactics

Shift spend among Products, Categories & Brands

Reduce / Eliminate unprofitable Spend

Increase Retailer AlignmentQuarterly / Annual Planning Process

Decisions Supported

Shift spend among RetailersIdentify & Expand best PracticesQuarterly / Annual Planning Budgets

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New POI Whitepaper

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Inability to Effectively Manage Promotions, and Benefit from them, Stems from Four Key FactorsComplexity Amount of resources/time required to analyze volume of trade promotions, given current systems, is unsustainable Fidelity: The fidelity of financial metrics within trade promotion analytics are highly suspect; end users trust outputData utilization: Much of the data that might help better inform trade analytics does not end up being used for analytics due to the difficulty in collecting, normalizing, and analyzing itData overload: Increasingly more data is being collected each day, but most of it is not being utilized. If anything, it tends to further cloak the problem because of the lack of resources and inability to get to the data that is most relevant.

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4 Key Capabilities Required for CPG Data & Analytics Excellence Pre-built Analytics Utilizing Advanced Modeling and Data Science Merging Disparate Data Expertise for Data Enrichment and Cleansing

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About Saama12

Data & advanced analytics solutions company since 1997Multi-vertical solutions High Tech, Insurance, Life Science/Pharma, CPGData scientists, Big Data engineers, consultants drive advanced analytics with business insights Transitioned from Services to Unique, Hybrid SolutionGlobal offices in San Jose, Phoenix, Columbus, London, Basel, & Pune

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