Shiny Dashboard & Interactive Analytics

An interactive Shiny dashboard turning raw VC funding data into filterable, explorable views with automated reporting.

Kiota IntelligenceR · ShinyReportingOutlier Detection

TL;DR: Pairing an interactive exploration layer (Shiny) with automated static reporting (RMarkdown) covers both ad-hoc analysis and recurring stakeholder updates from the same underlying pipeline.

The Problem

Static reports weren't enough for the Kiota Intelligence team to explore funding trends across sectors, stages and time windows — they needed to slice the data themselves, on demand, without waiting on a new export each time.

The Approach

I built a Shiny dashboard for funding visualization with dynamic filtering controls, so users could explore the dataset by sector, stage or time period interactively. On top of the visual layer, I added multidimensional outlier detection to flag unusual funding rounds, and temporal clustering to group companies by similar growth/funding trajectories. To keep stakeholders updated without manual work, I automated RMarkdown report generation with custom CSS styling for consistent, presentation-ready output.

Outcome

The dashboard replaced ad-hoc spreadsheet exploration with a self-serve tool, while the automated RMarkdown reports removed the recurring manual effort of preparing periodic updates for stakeholders.

Key Takeaway

Design insight: Pairing an interactive exploration layer (Shiny) with automated static reporting (RMarkdown) covers both ad-hoc analysis and recurring stakeholder updates from the same underlying pipeline.

FAQ

What is the key takeaway from "Shiny Dashboard & Interactive Analytics"?

Pairing an interactive exploration layer (Shiny) with automated static reporting (RMarkdown) covers both ad-hoc analysis and recurring stakeholder updates from the same underlying pipeline.

Who wrote this and what is it about?

This was written by Mahmoud Trigui, Senior Data Scientist. Interactive Shiny dashboard for VC funding data: dynamic filtering, multidimensional outlier detection, temporal clustering, and automated RMarkdown reporting.

Comments

Machine LearningFeature EngineeringMLForecastTime Series DecompositionForecastingLightGBMXGBoostCatboostClusteringSegmentationNLPLLMsWeb AppR MarkdownSQLOracle DBSAS-GuideSAS E-MinerDataikuBigQueryGCPPythonRCRISP-DMHypothesis TestingANOVAData AnalyticsDimensionality ReductionRecommendation SystemNetwork AnalysisGeospace AnalysisSpatial DataEmbeddingSampling TechniquesDecision RulesData StorytellingCVMChurnFraud DetectionSentiment AnalysisTopic ModelingIBM WatsonPowerBILooker StudioVBAStatistical LearningEnsemble ModelingStackingCross-ValidationProfilingABT ConstructionPlumberTidyverseShinyProphetDeep LearningScikit-LearnJSONSAS ProgrammingGitVS CodeCSS StylingAutomated ReportingOutlier DetectionTemporal ClusteringStartup SurvivalPre-Valuation ModelingK-MeansDecision TreesData SciencePredictive ModelingSVMLDAText ClassificationWeight PredictionPattern RecognitionReal-Time DetectionCommunity DetectionPipeline AutomationData Quality ChecksData ReliabilitySpecification MappingBusiness StrategyMarketing CampaignsTry & Buy FrameworksKPI DashboardsNetwork QualitySales AnalyticsMentoringStatistics LecturerRemote WorkHybrid WorkConsultingContractFull-TimeFreelanceSofrecomOrange GroupTunisia TelecomKiota IntelligenceVC AnalyticsSeries A PredictionProduction MLApplied AIPrompt EngineeringBusiness ForecastingDecision SystemsGraph AnalyticsHousehold DetectionMulti-SIM DetectionFTTH ForecastingAudit ExtractionInfrastructure ClassificationPydanticGPT-4OpenAI APIBase64 ClassificationZindiCodementorLAAS-CNRSESSAIMIT xPROTunisiaML CompetitionCell Tower AnalysisUber LogisticsUber Cape TownNecessary Condition AnalysisBehavioral SignalsSpike SmoothingObservation Unit DesignDendrogramWard ClusteringVIFTarget Encoding Machine LearningFeature EngineeringMLForecastTime Series DecompositionForecastingLightGBMXGBoostCatboostClusteringSegmentationNLPLLMsWeb AppR MarkdownSQLOracle DBSAS-GuideSAS E-MinerDataikuBigQueryGCPPythonRCRISP-DMHypothesis TestingANOVAData AnalyticsDimensionality ReductionRecommendation SystemNetwork AnalysisGeospace AnalysisSpatial DataEmbeddingSampling TechniquesDecision RulesData StorytellingCVMChurnFraud DetectionSentiment AnalysisTopic ModelingIBM WatsonPowerBILooker StudioVBAStatistical LearningEnsemble ModelingStackingCross-ValidationProfilingABT ConstructionPlumberTidyverseShinyProphetDeep LearningScikit-LearnJSONSAS ProgrammingGitVS CodeCSS StylingAutomated ReportingOutlier DetectionTemporal ClusteringStartup SurvivalPre-Valuation ModelingK-MeansDecision TreesData SciencePredictive ModelingSVMLDAText ClassificationWeight PredictionPattern RecognitionReal-Time DetectionCommunity DetectionPipeline AutomationData Quality ChecksData ReliabilitySpecification MappingBusiness StrategyMarketing CampaignsTry & Buy FrameworksKPI DashboardsNetwork QualitySales AnalyticsMentoringStatistics LecturerRemote WorkHybrid WorkConsultingContractFull-TimeFreelanceSofrecomOrange GroupTunisia TelecomKiota IntelligenceVC AnalyticsSeries A PredictionProduction MLApplied AIPrompt EngineeringBusiness ForecastingDecision SystemsGraph AnalyticsHousehold DetectionMulti-SIM DetectionFTTH ForecastingAudit ExtractionInfrastructure ClassificationPydanticGPT-4OpenAI APIBase64 ClassificationZindiCodementorLAAS-CNRSESSAIMIT xPROTunisiaML CompetitionCell Tower AnalysisUber LogisticsUber Cape TownNecessary Condition AnalysisBehavioral SignalsSpike SmoothingObservation Unit DesignDendrogramWard ClusteringVIFTarget Encoding