Evidence Based Big Data Benchmarking to Improve Business Performance Evidence Based Big Data Benchmarking to Improve Business Performance The market: Assessing Industrial Needs BDVA Meetup 27 June 2019, Riga Richard Stevens - IDC
Outline ✓ Objectives ✓ Summary of analytics questionnaire ✓ Use case selection ✓ Case study analysis methodology ✓ Preliminary insights from case study analysis DataBench Project - GA Nr 780966 2
Matching Needs to Measurements
How to Recognize Value of Big Data Technologies Prepare a preliminary classification of the main business drivers and KPIs for companies using BDT • Desk research using OECD / Eurostat / IDC data Survey businesses using BDT and representing to rank importance of these KPIs • 700 Companies from diverse business sectors • different types and sizes of companies Perform detailed surveys about the technical infrastructure • Size, type and approach to analytics Correlated business data and technical parameters 03/07/2019 DataBench Project - GA Nr 780966 4
What’s important for companies Respondents were asked about the 7 main KPI categories selected by the DataBench conceptual framework as measuring the most relevant business impacts: Product/service Cost reduction Time efficiency quality Customer Business model Revenue growth satisfaction innovation Increase in the number of New products or services launched 03/07/2019 DataBench Project - GA Nr 780966 5
Desk Research Thorough examination of RESEARCH ✓ over 100 research papers centered on PAPERS measuring business performance ✓ Characterization of use cases and vertical industry ✓ 633 use cases in total ICT VENDORS ✓ 59 use cases per industry on average Comparing data from survey with data from the desk analysis provides mainstream vs. ICT 14-15 innovation insights . PROJECTS DataBench Project - GA Nr 780966 DataBench Project - GA Nr 780966 6 6
Dimensions and values of business features 03/07/2019 DataBench Project - GA Nr 780966 7
Dimensions and values of the technical features 03/07/2019 DataBench Project - GA Nr 780966 8
Example: Agriculture • KPIs Agriculture KPI priority Agriculture 070% Importance KPI 060% - Cost Reduction 050% - Time Efficiency 040% - Product/service quality 030% - Customer satisfaction 020% - Business Model 010% innovation 000% Moderately Extremely/Very Slightly/Not • USE CASES important Cost reduction 022% 023% 055% Time, efficiency Use Case # Responses % Responses 038% 025% 037% Product/service quality 034% 037% 029% Field mapping & crop scouting 44 68% Revenue growth 037% 035% 028% Customer satisfaction 042% 031% 028% Price Optimization 42 65% Business model innovation 028% 026% 046% Inventory and service part 42 65% Increase in # of new 031% 043% 026% products optimization Mean at this interest level 040% 032% 028% all KPIs 03/07/2019 DataBench Project - GA Nr 780966 9
Statistical analysis: correlation matrix q5.LevelOfBenefits q7.BDImpact.IncreaseSer viceProduct q7.BDImpact.BusinessModelInno vation q7.BDImpact.CustomerSatisf action q7.BDImpact.ProductSer viceQuality q7.BDImpact.TimeEfficiency q16.ApproachToDataMngmt q14.Systems.Appliances q14.Systems.CommercialPlatf orms q14.Systems.OpenSourcePlatf orms q14.Systems.Hadoop q14.Systems.NewSQL q14.Systems.Graphs q14.Systems.NoSQL q14.Systems.InMemory q14.Systems.ColumnarDB q14.Systems.Relational q13.DataSize q12.TechnologyInvestment.AI q12.TechnologyInvestment.QC q12.TechnologyInvestment.BlockChain q12.TechnologyInvestment.IoT q12.TechnologyInvestment.Cloud q11.RealTimeIntegr ation Corr q10.BusinessProcessIntegr ation q8.KPIExpectedImpro vement.NewProductsLaunched q8.KPIExpectedImpro vement.BusinessModelInno vation 1.0 q8.KPIExpectedImpro vement.CustomerSatisf action q8.KPIExpectedImpro vement.RevenueGrowth q8.KPIExpectedImpro vement.ProductSer viceQuality q8.KPIExpectedImpro vement.TimeEfficiency q8.KPIExpectedImpro vement.CostReduction 0.5 q3.AbilityToBenchmark q2.Regulator yCompliance q2.ImproveOperational q2.ImproveFacilities 0.0 q2.ImproveBusiness q2.ImproveMarket q2.ProductInno vation q2.BusinessGoal.Optimiz ePricing −0.5 q2.BusinessGoal.CustomerBeha viour qs6.StatusOfBDuse q15.DataType.StructuredText q15.DataType.Timeseries −1.0 q15.DataType.Media q15.DataType.GeospatialTemporalData q15.DataType.LinkedData q15.DataType.TextData q15.DataType.TablesStructuredFiles q19.Analytics.Prescriptive q19.Analytics.Predictive q19.Analytics.Diagnostic q19.Analytics.Descriptive q18.TechnicalPerformance.Availability q18.TechnicalPerformance.AccuracyDataQuality q18.TechnicalPerformance.Cost q18.TechnicalPerformance.Throughput q18.TechnicalPerformance.EndToEndExecutionTime q17.Processing.Iter ativeInMemory q17.Processing.NearRealTime q17.Processing.StreamProcessing q17.Processing.BatchProcessing q4.KPIImpor tance.IncreaseSer viceProduct q4.KPIImpor tance.BusinessModelInno vation q4.KPIImpor tance.CustomerSatisf action q4.KPIImpor tance.RevenueGrowth q4.KPIImpor tance.ProductSer viceQuality q4.KPIImpor tance.TimeEfficiency q4.KPIImpor tance.CostReduction q4.KPIImportance.CostReduction q4.KPIImportance.ProductServiceQuality q4.KPIImportance.TimeEfficiency q4.KPIImportance.BusinessModelInnovation q4.KPIImportance.CustomerSatisfaction q4.KPIImportance.RevenueGrowth q4.KPIImportance.IncreaseServiceProduct q18.TechnicalPerformance.EndToEndExecutionTime q17.Processing.BatchProcessing q17.Processing.StreamProcessing q17.Processing.NearRealTime q17.Processing.IterativeInMemory q18.TechnicalPerformance.AccuracyDataQuality q18.TechnicalPerformance.Throughput q18.TechnicalPerformance.Cost q18.TechnicalPerformance.Availability q19.Analytics.Descriptive q15.DataType.TablesStructuredFiles q19.Analytics.Diagnostic q19.Analytics.Predictive q19.Analytics.Prescriptive q15.DataType.GeospatialTemporalData q15.DataType.TextData q15.DataType.LinkedData q2.BusinessGoal.CustomerBehaviour q15.DataType.Timeseries q15.DataType.StructuredText q15.DataType.Media q2.BusinessGoal.OptimizePricing qs6.StatusOfBDuse q2.ProductInnovation q8.KPIExpectedImprovement.CostReduction q8.KPIExpectedImprovement.ProductServiceQuality q2.ImproveMarket q8.KPIExpectedImprovement.TimeEfficiency q2.ImproveBusiness q8.KPIExpectedImprovement.BusinessModelInnovation q8.KPIExpectedImprovement.CustomerSatisfaction q8.KPIExpectedImprovement.RevenueGrowth q2.ImproveFacilities q2.ImproveOperational q2.RegulatoryCompliance q8.KPIExpectedImprovement.NewProductsLaunched q3.AbilityToBenchmark q10.BusinessProcessIntegration q12.TechnologyInvestment.Cloud q12.TechnologyInvestment.BlockChain q11.RealTimeIntegration q12.TechnologyInvestment.IoT q12.TechnologyInvestment.QC q12.TechnologyInvestment.AI q14.Systems.Relational q14.Systems.ColumnarDB q13.DataSize q14.Systems.InMemory q14.Systems.NoSQL q14.Systems.OpenSourcePlatforms q14.Systems.Graphs q14.Systems.CommercialPlatforms q14.Systems.NewSQL q14.Systems.Hadoop q7.BDImpact.ProductServiceQuality q16.ApproachToDataMngmt q14.Systems.Appliances q7.BDImpact.BusinessModelInnovation q7.BDImpact.TimeEfficiency q7.BDImpact.CustomerSatisfaction q7.BDImpact.IncreaseServiceProduct q5.LevelOfBenefits 03/07/2019 DataBench Project - GA Nr 780966 10
Statistical analysis: factor analysis • The factor analysis stresses that companies that have already obtained and measured business benefits from BDT projects are focused on traditional batch processing. • In contrast, companies that experiment with more advanced real time applications of BDTs have not yet measured business benefits. • Moreover, companies that have not yet exploited BDTs or have a traditional exploitation of BDTs (batch) are technology enthusiast and/or plan to explore more innovative applications of BDTs, but do not view future business benefits as measurable with economic KPIs at this stage of development of BDTs. 03/07/2019 DataBench Project - GA Nr 780966 11
RESEARCH PAPERS desk analysis INDUSTRIAL NEEDS SURVEY ICT VENDORS desk analysis ICT 14-15 PROJECT desk analysis Use Case Analysis DataBench Project - GA Nr 780966 12 12
Use case selection criteria The list of use cases is based on the IDC industrial needs survey. The list of DataBench use cases was defined by: ✓ using one use case from state of the art use cases list -> to be able to assess the business KPIs, ✓ using one use case from the desk analysis -> to account for research and emerging use cases, ✓ preferring use cases specific to the industry -> to make it easier to identify pilots with quantitative business KPIs, ✓ keeping some cross industry use cases, e.g., supply chain optimization is a topic in manufacturing as well as in retail. DataBench Project - GA Nr 780966 DataBench Project - GA Nr 780966 13 13
Case study analysis methodology The follow-up interview should cover the aspects/perspectives missed by the first interview by: ✓ involving respondents with a more specific profile, ✓ focusing on the collection of quantitative business KPIs. 03/07/2019 DataBench Project - GA Nr 780966 14
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