UNIVERSITY/PUBLICDRIVEN APPLICATIONSHow to access, organize, and develop valuesfrom Big Data to meet societal challenges, ...
Group Participants• A. Letizia Allegra Mascaro• Andrew Rindos• Albert Lejeune• Alessandro Bria• Andrea Lodi• Benoît Otjacq...
Group 2 geographyFR, IT, USA (Washington DC, PA, MD, NC), Saudi Arabia, Russia, Spain,UK, Luxembourg, Denmark, Thailand
Table of ContentA.B.C.D.E.State of the artCurrent WorkNear future WorkChallenges and issuesRecommendations
STATE OF THE ARTA.
How organizations view Big DataSchroeck, M., Shockley et al (2012) Analytics: The real-world use of big data How innovativ...
Three elements• Big data is seen as data(different kinds of), storage ofdata and analysis of the data1• Social aspects of ...
Schroeck, M., Shockley et al (2012) Analytics: The real-world use of big data How innovativeenterprises extract value from...
Data is there and we need to make the best out of it
We produce and consume Data for a specific purpose
“Big Data is the set oftechnicalcapabilities, processes, strategiesand skills forcontinously convertingvast, fast, varied ...
CURRENT WORKB.
Marco Pospiech and Carsten Felden ( July 29, 2012). AMCIS 2012 Proceedings.Big DataA Stateof theArtGroup 2:improving final...
Big Data – Group 2 Survey1•ApplicationDomains2 •Use / consumption3 •Functional Areas
SURVEY RESULTS(17/20 RESPONDENTS)
Group 2 – Big Data production& utilization32%68%Data Production &UtilizationData Utilization
Group 2 – Application domains727852 261 1250123456789Application domain
12 1262 281028012345678910111213Group 2 - Functional Areascovered
Group 2 - Type of AnalyticsGroup 2 – Type of Analytics35%30%13%22%Type of analyticsDescriptiveShallow PredictiveDeep Predi...
CHALLENGES & ISSUESB.C.D.
Government /Healthcare /Soc. SciencesInformation SciencesData-intensiveSciencesApplication DomainsState ofthe ArtNearFutur...
Government /Healthcare / Soc.SciencesInformation SciencesData-intensiveSciencesApplication Domains (1/2)State of theArtNea...
Government /Healthcare / Soc.SciencesChallengesand issuesApplication Domains (1/2)Knowledge discovery and sharing, data st...
EnterprisesEducationApplication Domains (2/2)State of theArtLot of professionals dealing with datamanagement, a relatively...
Challengesand issuesApplication Domains (2/2)Educating to Big DataBig Data to support EducationMining techniques, Analytic...
RECOMMENDATIONSE.
Organizational EducationalStrategy &PolicyRecommendationsExpectedbenefitsFunctions &TechnologyExpectedbenefits
Organizational EducationalStrategy &PolicyRecommendations (1/2)1) Support curricula design in MngtScience, Analytics, Data...
Organizational EducationalFunctions &TechnologyRecommendations (2/2)1) Translate Big Data, SmarterPlanet, Analytics and Op...
Improving final usersexperience in Big Datawith newmethodologies, theories,technologies, educationa
UNIVERSITY/PUBLICDRIVEN APPLICATIONSHow to access, organize, and develop valuesfrom Big Data to meet societal challenges, ...
University Public Driven Applications - Big Data and Organizational Design
University Public Driven Applications - Big Data and Organizational Design
University Public Driven Applications - Big Data and Organizational Design
University Public Driven Applications - Big Data and Organizational Design
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University Public Driven Applications - Big Data and Organizational Design

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BDOD 2013 WORLD SUMMIT ON BIG DATA AND ORGANIZATION DESIGN Paris, May 2013

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University Public Driven Applications - Big Data and Organizational Design

  1. 1. UNIVERSITY/PUBLICDRIVEN APPLICATIONSHow to access, organize, and develop valuesfrom Big Data to meet societal challenges, tooptimize public utility usages and to reducewaste?
  2. 2. Group Participants• A. Letizia Allegra Mascaro• Andrew Rindos• Albert Lejeune• Alessandro Bria• Andrea Lodi• Benoît Otjacques• Carlo Cavazzoni• David Nguyen• Eiman Kanjo• Elias Carayannis• Fabien Mieyeville• Fabrizio Piccolo• Florent Pratlong• Francesco Saverio Pavone• Gabriel Juhás• Georgios Theodoropoulos• Giorgio Pedrazzi• Giovanni Erbacci• Giovanni Righini• Giulia Adembri• Giulio Iannello• Giuseppe Fiameni• Jean-Patrick Péché• Samuel Javelle• Juan M. Vara• Kathleen M. Carley• Leonardo Sacconi• Ludovico Silvestri• Maria Chiara Pettenati• Maria Teresa Pazienza• Mladen Vouk• Nesrine Zemirli• Paolo Frasconi• Paolo Nesi• Paolo Tubertini• Patrik Hitzelberger• Renaud Cornu-Emieux• Renaud Gaultier• Rick Edgeman• Roberta Turra• Sanzio Bassini• Senese Francesca• Sergey Belov• Simone Tani• Stavros Sindakis• Svetlana Maltseva• Yves DenneulinGroup chair: Maria Chiara Pettenati20 papers, 47 authors
  3. 3. Group 2 geographyFR, IT, USA (Washington DC, PA, MD, NC), Saudi Arabia, Russia, Spain,UK, Luxembourg, Denmark, Thailand
  4. 4. Table of ContentA.B.C.D.E.State of the artCurrent WorkNear future WorkChallenges and issuesRecommendations
  5. 5. STATE OF THE ARTA.
  6. 6. How organizations view Big DataSchroeck, M., Shockley et al (2012) Analytics: The real-world use of big data How innovativeenterprises extract value from uncertain data18%16%15%13%13%10%8%5%Greater scope of informationNon traditional forms of mediaNew kind of data and analysisReal time informationLarge volumes of dataThe latest buzzwordData influx from new technologiesSocial Media Data 1144 respondents
  7. 7. Three elements• Big data is seen as data(different kinds of), storage ofdata and analysis of the data1• Social aspects of data areminor2• Big data is a hyped term3
  8. 8. Schroeck, M., Shockley et al (2012) Analytics: The real-world use of big data How innovativeenterprises extract value from uncertain data
  9. 9. Data is there and we need to make the best out of it
  10. 10. We produce and consume Data for a specific purpose
  11. 11. “Big Data is the set oftechnicalcapabilities, processes, strategiesand skills forcontinously convertingvast, fast, varied datainto Right Data toobtain actionableinsights andforesights”Adapted from: Beacon Report – BigData Big Brains – 2013
  12. 12. CURRENT WORKB.
  13. 13. Marco Pospiech and Carsten Felden ( July 29, 2012). AMCIS 2012 Proceedings.Big DataA Stateof theArtGroup 2:improving finalusersexperience inBig Data – newmethodologies,theories, technologies, neweducationalcurricula
  14. 14. Big Data – Group 2 Survey1•ApplicationDomains2 •Use / consumption3 •Functional Areas
  15. 15. SURVEY RESULTS(17/20 RESPONDENTS)
  16. 16. Group 2 – Big Data production& utilization32%68%Data Production &UtilizationData Utilization
  17. 17. Group 2 – Application domains727852 261 1250123456789Application domain
  18. 18. 12 1262 281028012345678910111213Group 2 - Functional Areascovered
  19. 19. Group 2 - Type of AnalyticsGroup 2 – Type of Analytics35%30%13%22%Type of analyticsDescriptiveShallow PredictiveDeep PredictivePrescriptive
  20. 20. CHALLENGES & ISSUESB.C.D.
  21. 21. Government /Healthcare /Soc. SciencesInformation SciencesData-intensiveSciencesApplication DomainsState ofthe ArtNearFutureWorkChallenges andissuesEnterprisesEducation
  22. 22. Government /Healthcare / Soc.SciencesInformation SciencesData-intensiveSciencesApplication Domains (1/2)State of theArtNear FutureWorkCrisismanagement, Pollutionmonitoring, SentimentAnalyisis, ServicePersonalization, Traceability management,Organizational learning andenterprise intelligenceBig Data ProductionLaboratory-restrainedprocessing functionsScalable temporal andnetwork approximationAlgorithms for informationextraction and text miningData modellingData sharingRemote processingOpen Data(beginning), DecisionSupportSystems, LabourPlanning Healthcare,Social networkanalytics, MashupsWeb of DataUnstructured contentanalysisInnovation dynamicsand organizationalambidexteritySocial media analyticsGeo-networkanalytics
  23. 23. Government /Healthcare / Soc.SciencesChallengesand issuesApplication Domains (1/2)Knowledge discovery and sharing, data storage, format conversion, datafusion, multi-media content metadata analysisMetadata std., Datagathering, Analyticsevolution, Exascalecomputing, Advanced Open and LinkedData, Policy changesto support use of bigdata,cross-border datatransfer, data supplychainInterdisciplinarity (skilled professionals), large-scale Information visualisation, easilyaccessible frameworks for developing intelligent applications, address a common/shared semantics, system of systems optimizationUser-friendly databrowsing and informationretrievalHigh performancecomputing requirementsUsers (scientists) trainingInformation retrievalmodels, scalingalgorithms, Informationrelevance /utility, limitedaccess to social media data, multi-lingual data, userparticipation, law, security, privacy, ethics, smart socialuser profiles/semanticmodeling, inferencealgorithm, pervasivetechnologyInformation SciencesData-intensiveSciences
  24. 24. EnterprisesEducationApplication Domains (2/2)State of theArtLot of professionals dealing with datamanagement, a relatively small numberof professionals building models, and alarge number of users who aredownstream from those models whohave to make decisionsUniversity of MilanoEcole Central LyonThe North Carolina Virtual ComputingLab (VCL)Grenoble Ecole de Management (GEM)and Grenoble INP – EnsimagUniversty of Roma Tor VergataNear FutureWork Living LabDesign ThinkingOrganizational design to supportefficacy, robustness and resilienceData-driven decision makingBusiness model sustainabilityBusiness Process ManagementSystemsKnowledge, information & dataanalytics – more computationallyintensiveProcess Mining, Discovery
  25. 25. Challengesand issuesApplication Domains (2/2)Educating to Big DataBig Data to support EducationMining techniques, Analytical toolsReference modelsExascale computing challengesMemory and StorageReliability and resiliencyConcurrency and ProgrammingmodelsSystem SoftwareCo-design of hardware andapplicationsSustainable Enterprise Excellence(SEE)Data Governancelarge-scale Information visualisation, easily accessible frameworks for developingintelligent applications, address a common/ shared semantics, system of systemsoptimizationEnterprisesEducation
  26. 26. RECOMMENDATIONSE.
  27. 27. Organizational EducationalStrategy &PolicyRecommendationsExpectedbenefitsFunctions &TechnologyExpectedbenefits
  28. 28. Organizational EducationalStrategy &PolicyRecommendations (1/2)1) Support curricula design in MngtScience, Analytics, Data Sc., Eng. andOperations Res. with interdisciplinarycompetencies (Math, ComputerScience, Economics andEngineering, Social and HumanSciences)2) Integrate mathematics didactics withtopics addressing value extractionfrom data3) Complement traditional mathbranches (Calculus, Geometry andAlgebra) with OperationsResearch, Data Mining and MachineLearning4) Address educating to Big Data aswell as Big data in education1) The message must come from outside(companies, public institutions, fundingagencies)2) Make the case for the value of Big Dataand put in place the appropriate businessprocess3) Open real valuable (big) data and alignprocesses and policy4) Sustaining cultural shift: Right Data ismore valuable than Big Data5) Direct research funds to interdisciplinaryresearch in Analytics and Data Science andEngineering6) Provide open governance on decisionmaking tools and meansExpectedbenefitsCollapse efforts, realize economic, ecologicand societal potential ofinformation, Promotesustainability, efficiency, innovation, opendemocracy through transparency. Addresssocietal challenges with scientifictechniques instead of using a techno-centricNew generations of experts trained toaddress big societal challenges withappropriate tools, skillset and mindsetMore efficient educational system
  29. 29. Organizational EducationalFunctions &TechnologyRecommendations (2/2)1) Translate Big Data, SmarterPlanet, Analytics and Optimization intobetter recognizable keywords likeOperations Research/ManagementScience, ComputationalMathematics, Statistics2) Start «analytics» programs (learning aswell as academic analytics)3) Develop new metrics of quality of theeducational process and theeducational institutions4) Мodeling tools foreducation, Integrated with Big Dataanalytics5) Provide access to large datasets foreducational purposes1) Invest in (technological) capacitybuilding2) Implement good informationprocessing practices3) Define new frameworks andarchitectures for big dataanalysis4) Address languages diversity5) Support careful analysis andmassive data warehousing ofheterogeneous and distributedpieces of data.6) Contribute to standardardizationExpectedbenefitsImprove Big Data accessibility forseveral applications, support betterdeployment, easier access to big andopen data, easier identification ofprocesses that need optimisationSmarter educationSmarted administrationInnovation in Education & Research
  30. 30. Improving final usersexperience in Big Datawith newmethodologies, theories,technologies, educationa
  31. 31. UNIVERSITY/PUBLICDRIVEN APPLICATIONSHow to access, organize, and develop valuesfrom Big Data to meet societal challenges, tooptimize public utility usages and to reducewaste?

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