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DataBench in a nutshell - The market: Assessing industrial needs, Richard Stevens, BDV Meet-Up Riga, 27/06/2019


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DataBench in a nutshell - The market: Assessing industrial needs
Richard Stevens, IDC
BDV Meet-Up, Riga
27 June 2019

Published in: Data & Analytics
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DataBench in a nutshell - The market: Assessing industrial needs, Richard Stevens, BDV Meet-Up Riga, 27/06/2019

  1. 1. Evidence Based Big Data Benchmarking to Improve Business PerformanceEvidence Based Big Data Benchmarking to Improve Business Performance The market: Assessing Industrial Needs BDVA Meetup 27 June 2019, Riga Richard Stevens - IDC
  2. 2. 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
  3. 3. Matching Needs to Measurements
  4. 4. How to Recognize Value of Big Data Technologies • Desk research using OECD / Eurostat / IDC data Prepare a preliminary classification of the main business drivers and KPIs for companies using BDT • 700 Companies from diverse business sectors • different types and sizes of companies Survey businesses using BDT and representing to rank importance of these KPIs • Size, type and approach to analytics Perform detailed surveys about the technical infrastructure Correlated business data and technical parameters 03/07/2019 DataBench Project - GA Nr 780966 4
  5. 5. 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: 03/07/2019 DataBench Project - GA Nr 780966 5 Cost reduction Time efficiency Product/service quality Revenue growth Customer satisfaction Business model innovation Increase in the number of New products or services launched
  6. 6. DataBench Project - GA Nr 780966 6 Desk Research DataBench Project - GA Nr 780966 6 Thorough examination of ✓over 100 research papers centered on measuring business performance ✓Characterization of use cases and vertical industry ✓633 use cases in total ✓59 use cases per industry on average Comparing data from survey with data from the desk analysis provides mainstream vs. innovation insights. RESEARCH PAPERS ICT VENDORS ICT 14-15 PROJECTS
  7. 7. Dimensions and values of business features 03/07/2019 DataBench Project - GA Nr 780966 7
  8. 8. Dimensions and values of the technical features 03/07/2019 DataBench Project - GA Nr 780966 8
  9. 9. Agriculture • KPIs • USE CASES 03/07/2019 DataBench Project - GA Nr 780966 9 Extremely/Very Moderately important Slightly/Not Cost reduction 022% 023% 055% Time, efficiency 038% 025% 037% Product/service quality 034% 037% 029% Revenue growth 037% 035% 028% Customer satisfaction 042% 031% 028% Business model innovation 028% 026% 046% Increase in # of new products 031% 043% 026% Mean at this interest level all KPIs 040% 032% 028% 000% 010% 020% 030% 040% 050% 060% 070% Agriculture KPI priority Importance KPI - Cost Reduction - Time Efficiency - Product/service quality - Customer satisfaction - Business Model innovation Use Case # Responses % Responses Field mapping & crop scouting 44 68% Price Optimization 42 65% Inventory and service part optimization 42 65% Example: Agriculture
  10. 10. Statistical analysis: correlation matrix 03/07/2019 DataBench Project - GA Nr 780966 10 q4.KPIImpor tance.CostReduction q4.KPIImpor tance.TimeEfficiency q4.KPIImpor tance.ProductSer viceQuality q4.KPIImpor tance.RevenueGrowth q4.KPIImpor tance.CustomerSatisf action q4.KPIImpor tance.BusinessModelInno vation q4.KPIImpor tance.IncreaseSer viceProduct q17.Processing.BatchProcessing q17.Processing.StreamProcessing q17.Processing.NearRealTime q17.Processing.Iter ativeInMemory q18.TechnicalPerformance.EndToEndExecutionTime q18.TechnicalPerformance.Throughput q18.TechnicalPerformance.Cost q18.TechnicalPerformance.AccuracyDataQuality q18.TechnicalPerformance.Availability q19.Analytics.Descriptive q19.Analytics.Diagnostic q19.Analytics.Predictive q19.Analytics.Prescriptive q15.DataType.TablesStructuredFiles q15.DataType.TextData q15.DataType.LinkedData q15.DataType.GeospatialTemporalData q15.DataType.Media q15.DataType.Timeseries q15.DataType.StructuredText qs6.StatusOfBDuse q2.BusinessGoal.CustomerBeha viour q2.BusinessGoal.Optimiz ePricing q2.ProductInno vation q2.ImproveMarket q2.ImproveBusiness q2.ImproveFacilities q2.ImproveOperational q2.Regulator yCompliance q3.AbilityToBenchmark q8.KPIExpectedImpro vement.CostReduction q8.KPIExpectedImpro vement.TimeEfficiency q8.KPIExpectedImpro vement.ProductSer viceQuality q8.KPIExpectedImpro vement.RevenueGrowth q8.KPIExpectedImpro vement.CustomerSatisf action q8.KPIExpectedImpro vement.BusinessModelInno vation q8.KPIExpectedImpro vement.NewProductsLaunched q10.BusinessProcessIntegr ation q11.RealTimeIntegr ation q12.TechnologyInvestment.Cloud q12.TechnologyInvestment.IoT q12.TechnologyInvestment.BlockChain q12.TechnologyInvestment.QC q12.TechnologyInvestment.AI q13.DataSize q14.Systems.Relational q14.Systems.ColumnarDB q14.Systems.InMemory q14.Systems.NoSQL q14.Systems.Graphs q14.Systems.NewSQL q14.Systems.Hadoop q14.Systems.OpenSourcePlatf orms q14.Systems.CommercialPlatf orms q14.Systems.Appliances q16.ApproachToDataMngmt q7.BDImpact.TimeEfficiency q7.BDImpact.ProductSer viceQuality q7.BDImpact.CustomerSatisf action q7.BDImpact.BusinessModelInno vation q7.BDImpact.IncreaseSer viceProduct q5.LevelOfBenefits q4.KPIIm portance.C ostR eduction q4.KPIIm portance.Tim eEfficiency q4.KPIIm portance.ProductServiceQ uality q4.KPIIm portance.R evenueG row th q4.KPIIm portance.C ustom erSatisfaction q4.KPIIm portance.BusinessM odelInnovation q4.KPIIm portance.IncreaseServiceProduct q17.Processing.BatchProcessing q17.Processing.Stream Processing q17.Processing.N earR ealTim e q17.Processing.IterativeInM em ory q18.TechnicalPerform ance.EndToEndExecutionTim e q18.TechnicalPerform ance.Throughput q18.TechnicalPerform ance.C ost q18.TechnicalPerform ance.AccuracyD ataQ uality q18.TechnicalPerform ance.Availability q19.Analytics.D escriptive q19.Analytics.D iagnostic q19.Analytics.Predictive q19.Analytics.Prescriptive q15.D ataType.TablesStructuredFiles q15.D ataType.TextD ata q15.D ataType.LinkedD ata q15.D ataType.G eospatialTem poralD ata q15.D ataType.M edia q15.D ataType.Tim eseries q15.D ataType.StructuredText qs6.StatusO fBD use q2.BusinessG oal.C ustom erBehaviour q2.BusinessG oal.O ptim izePricing q2.ProductInnovation q2.Im proveM arket q2.Im proveBusiness q2.Im proveFacilities q2.Im proveO perational q2.R egulatoryC om pliance q3.AbilityToBenchm ark q8.KPIExpectedIm provem ent.C ostR eduction q8.KPIExpectedIm provem ent.Tim eEfficiency q8.KPIExpectedIm provem ent.ProductServiceQ uality q8.KPIExpectedIm provem ent.R evenueG row th q8.KPIExpectedIm provem ent.C ustom erSatisfaction q8.KPIExpectedIm provem ent.BusinessM odelInnovation q8.KPIExpectedIm provem ent.N ew ProductsLaunched q10.BusinessProcessIntegration q11.R ealTim eIntegration q12.TechnologyInvestm ent.C loud q12.TechnologyInvestm ent.IoT q12.TechnologyInvestm ent.BlockC hain q12.TechnologyInvestm ent.Q C q12.TechnologyInvestm ent.AI q13.D ataSize q14.System s.R elational q14.System s.C olum narD B q14.System s.InM em ory q14.System s.N oSQ L q14.System s.G raphs q14.System s.N ew SQ L q14.System s.H adoop q14.System s.O penSourcePlatform s q14.System s.C om m ercialPlatform s q14.System s.Appliances q16.ApproachToD ataM ngm t q7.BD Im pact.Tim eEfficiency q7.BD Im pact.ProductServiceQ uality q7.BD Im pact.C ustom erSatisfaction q7.BD Im pact.BusinessM odelInnovation q7.BD Im pact.IncreaseServiceProduct q5.LevelO fBenefits −1.0 −0.5 0.0 0.5 1.0 Corr
  11. 11. Statistical analysis: factor analysis 03/07/2019 DataBench Project - GA Nr 780966 11 • 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.
  12. 12. DataBench Project - GA Nr 780966 1212 Use Case Analysis ICT 14-15 PROJECT desk analysis INDUSTRIAL NEEDS SURVEY RESEARCH PAPERS desk analysis ICT VENDORS desk analysis
  13. 13. DataBench Project - GA Nr 780966 13 Use case selection criteria DataBench Project - GA Nr 780966 13 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.
  14. 14. Case study analysis methodology 03/07/2019 DataBench Project - GA Nr 780966 14 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.
  15. 15. San Raffaele Hospital Whirlpool- BOOST H2020 e2mc Research case studies 03/07/2019 15 H2020 EWShopp •Evaluating performances of Arango and Orient DB •Expected measured KPIs mainly focus on customer satisfaction ESA •Gaia is the only mission surveying the complete sky with unprecedented precision and completeness •Processing 100 GBs of raw data every day IDEKO •Enhancing descriptive analytics and experimenting anomaly detection using ML
  16. 16. Airbus Event Registry Cerved Industrial case studies 03/07/2019 16 TravelBird • Predictive analytics on event data • Revenue growth Intel •Real-time predictive analytics (mainly anomaly detection) •Avoid revenue loss due to malfunctions of the equipment Pam 112 eGeos
  17. 17. Case study analysis: cases by industry 03/07/2019 DataBench Project - GA Nr 780966 17 •Pittarosso •Pam •H2020 EW-Shopp Retail & Wholesale •eGeos •ESAAgriculture/EO •INTEL •Fater •Whirlpool Manufacturing •San Raffaele HospitalHealthcare •TravelBird •Event Registry •Ideko Business / IT Services / AI •Siemens •112Transport and Logistics Financial Services Telecom/Media Utilities / Oil & Gas
  18. 18. Early Insights • From the evidence that has been collected so far, an important lesson learnt is that most companies believe that technical benchmarking requires highly specialized skills and a considerable investment. We have found that very few companies have performed an accurate and extensive benchmarking initiative. In this respect, using DataBench like Solutions grants them with an easier access to a broader set of technologies that they can experiment with. • On the other hand, they acknowledge the variety and complexity of technical solutions for big data and envision the following technical risks: • The risk of realizing that they have chosen a technology that proves non scalable over time, either technically or economically. • The risk of relying on cloud technologies that might create a lock in and require a considerable redesign of software to be migrated to other cloud technologies. • The risk of discovering that cloud services are expensive, especially as a consequence of scalability, and that technology costs are higher than business benefits. 03/07/2019 DataBench Project - GA Nr 780966 18
  19. 19. Evidence on business KPIs from case study analysis • We have evidence of business KPIs for case studies where we have reached the pilot stage according to our case study methodology. • Evidence is aligned with results from survey (business benefits are in the 5-8% range). • We have already performed 4-5 case studies at pilot stage confirming results • Business KPIs are seldom quantified in cases studies from the literature (most projects are at POC level). • From the desk analysis, multiple business KPIs are affected simultaneously and the benefits from a single project are often difficult to isolate from other factors affecting the same business KPI. 03/07/2019 DataBench Project - GA Nr 780966 19
  20. 20. Sample case study: intelligent fulfilment in retail 03/07/2019 DataBench Project - GA Nr 780966 20 • Automatic replenishment optimization is a fundamental process in the retail industry, it involves multiple departments, e.g., logistics, order management, etc., and affects sales through stockouts and, thus, revenues and customer satisfaction. • Currently, in the analyzed case study the replenishment process is carried out manually by the point of sale (POS) employees that periodically check for the presence of a sufficient number of items/products to fulfill the expected demand for the following days. • The manual process has evident drawbacks, as it is error prone and suffers of the bias introduced by the judgment of the employee.
  21. 21. Sample case study: intelligent fulfilment in retail 03/07/2019 DataBench Project - GA Nr 780966 21 • The company is piloting an automatic replenishment procedure that will optimize the order scheduling process by using a set of sensors to detect the number of items on the shelves and by adopting a machine learning algorithm to forecast products demand. • Overall, the main goals of the case study are to improve the quality of the service provided to customers and to improve the efficiency of the replenishment process. • From a technical perspective, innovative aspects of the new automatic replenishment procedure include: ✓ the adoption of an ad-hoc prediction algorithm for product demand forecasting, ✓ the positioning of a set of image sensors able to monitor in real-time the number of items on the shelves, ✓ the adoption of an image recognition algorithm able to identify the number of products on the shelves, and consequently to identify mis-placed items, items positioned inaccurately, etc.
  22. 22. IT architecture: intelligent fulfilment in retail 03/07/2019 DataBench Project - GA Nr 780966 22
  23. 23. Blueprint 03/07/2019 23
  24. 24. Business KPIs: intelligent fulfilment in retail 03/07/2019 DataBench Project - GA Nr 780966 24 • Business benefits carried by the intelligent fulfillment are manifold. • General indicators, useful to assess the effectiveness of the process, include: ✓ revenue growth due to avoided lost sales, ✓ customer satisfaction, ✓ improvement in the efficiency of the fulfillment process, that results to be more structured and organized, ✓ more specific indicators useful to assess the efficiency of the intelligent fulfillment process include number of stockouts, inventory turnover and, with a focus on logistic efficiency, mean time between orders. In this context, the inventory turnover measures the time spent by an item in the warehouse, high levels of storage represent an undesirable condition because they increase storage management costs. • The case study is still in its piloting stage and it has not yet delivered quantitative business benefits. Nonetheless, it is providing deep insights to the whole POS management and efficiency. • Economic scalability issue has been recognized: cameras acquiring images have to be placed on shelves in 250 POS (over 40 Km of cameras). • Technical scalability issue: is the lambda architecture scalable with image processing? At what costs?
  25. 25. DataBench Project - GA Nr 780966 Get in touch with us!