Open Data & Innovation. Peter Parycek Danube University Krems, Department for E-Governance

Transcription

1 Open Data & Innovation Peter Parycek Danube University Krems, Department for E-Governance

2 Agenda OGD Value & Benefits

3 OGD$Value$

4 OGD Benefits & Values 1. Innovation and Economic Growth 2. Strengthening Democracy transparency and accountability participatory governance 3. Internal Value for the public sector itself Benefits, Adoption Barriers and Myths of Open Data and Open Government Marijn Janssen a*, Yannis Charalabidis b & Anneke Zuiderwijk Information Systems Management, Volume 29, Issue 4, 2012,

5 OGD value generation framework Data-Driven Innovation trough Open Government Data, Thorhildur Jetzek, Michel Avital, Niels Bjørn-Andersen

6 Conceptual model of the data driven innovation mechanism Data-Driven Innovation trough Open Government Data, Thorhildur Jetzek, Michel Avital, Niels Bjørn-Andersen

7 OGD Core Elements Innovation with open data: Essential elements of open data ecosystems Anneke Zuiderwijk, Marijn Janssen, Chris Davis

8 Innovation with open data: Essential elements of open data ecosystems Anneke Zuiderwijk, Marijn Janssen, Chris Davis

9 OGD Critical Factors

10 Research findings Comprehensive list of generic factors Factors in 9 categories of open data publication Factors in 6 categories of open data use 1. Legislation, regulation and licenses 2. Strategy and political support 3. Management support and publication processes within governmental agencies 4. Training of and support for civil servants 5. Evaluation of the open data initiative 6. Sustainability of the open data initiative 7. Collaboration 8. Open data platforms, tools and services 9. Accessibility, interoperability and standards 10. Legislation, regulation and licenses 11. Success stories 12. Incentives for open data use 13. Training of and support for open data users 14. Feedback and sustainability 15. Research and education Open Data Disclosure and Use: Critical Factors From a Case Study Zuiderwijk, Susha, Charalabidis, Parycek, Janssen

11 Research findings Context-specific critical success factors CSF categories most critical for open data publication in ENGAGE referred to: 1) legislation, regulation and licenses Case: focused on sharing datasets with various communities 6) sustainability of the open data initiative Case: temporal project 9) accessibility, interoperability and standards Case: homogenous access to heterogeneous datasets Open Data Disclosure and Use: Critical Factors From a Case Study Zuiderwijk, Susha, Charalabidis, Parycek, Janssen

12 Research findings Context-specific critical success factors CSF categories most critical for open data use in ENGAGE concerned: 10) legislation, regulation and licenses Case: focus on freely sharing datasets 11) success stories Case: create large user base (critical mass), attract more people to use the infrastructure Open Data Disclosure and Use: Critical Factors From a Case Study Zuiderwijk, Susha, Charalabidis, Parycek, Janssen

13 Research findings Context-specific critical success factors Additional factors identified from the case Open data publication data stewardship (assuring accuracy, validity, security, and preservation) and development of a management plan Open data use that open data users should know precisely and in a scientific manner the methodology of how the data were produced Open Data Disclosure and Use: Critical Factors From a Case Study Zuiderwijk, Susha, Charalabidis, Parycek, Janssen

14 OGD Cases

15 OGD Portal UN Public Service Award Source:

16 Legal Information System: Service

17 Legal Information System: Data

18 Real Time Public Transport Data Result: 1 Data-Set: 15 Apps Dataset: Source

19 Open Data Portal Companies, R&D & CSO Source:

20 OGD + OD for Law Enforcement

21 OD, OGD, Closed + Crowdsourced Data

22 Domain Solutions

23 OGD Collaboration

24 OGD Activities Publishing* Integra<on*of* APPs* Feedback*&** Quality* Management* Community* Building* (Hackathons,* )* Incen<ves** (Compe<<ons,* )*

25 Ongoing Publicationsprocess

26 Creat Camps & Hackathons Internal External Communities

27

28 Open Knowledge Austria

29 Providing Hacking Space

30 Competitions

31 Quality Management 1. Establishing quality processes and providing tools which can automatically inform data providers about quality issues, or automatically curate data; 2. Incorporating the opinions on data quality of data consumers; 3. Guide data providers during the process of data provision; 4. Monitor data quality over time.

32 OGDATWatcher:

33 O(G)D Data Ecosystem

34 Data Value Chain by Latif et al., TU Graz,

35 Source: Donau-Universität Krems, Centre for E-Governance Governance for Data Driven Organizations External* Open* Data* * Second*&* Third*Party** Data* * User* Generated* Content*! Applica1ons! Visualisa1ons! Data! Search!&!Analy1cs! Internal* Databases! File! Systems! E>Mail! Server! * Closed*internal*Data!! Seman1c! Processing! Predic1on!

36 Data Platforms! from closed to open External* Internal* Open*(Gov)*Data* Shared*Extranet*Data* (Internal!shared!data!between!different!organiza1ons)** Shared*Internal*Data* (Internal!shared!Data)** Closed*Internal*Data* (Department!owned!closed!but!located!data)** Classification of Data (from closed, shared, semi open to open) Source: Donau-Universität Krems, Centre for E-Governance

37 Data Analytics Roadmap Austria

38 Donau-Universität Krems. Die Universität für Weiterbildung. Peter Parycek Zentrum für E-Governance

39 Mehrwert durch Datenmischung Öffis* Zu*Fuss* Bike2Go* Uber* Car2Go* Mi[ahrApp* Taxi* * TransportN mioel* LuSwerte* Unfallsta<s<k* Verkehrsflus s* Kosten* Gesundheit* * WeOer* Faktoren*

40 Data Platforms! from closed to open External* Internal* Open*Data* Shared*Extranet*Data* (Internal!shared!data!between!different!organiza1ons)** Shared*Internal*Data* (Internal!shared!Data)** Closed*Internal*Data* (Department!owned!closed!but!located!data)** Classification of Data (from closed, shared, semi open to open) Source: Donau-Universität Krems, Centre for E-Governance

41 Conceptual model of the data driven innovation mechanism Data-Driven Innovation trough Open Government Data, Thorhildur Jetzek, Michel Avital, Niels Bjørn-Andersen

42 User participation dimensions 1. Users hands-on performance of activities 2. Responsibility 3. Communication with information systems staff and senior management 4. Relations with information systems

43 Data Analytics Techniques

44 Policy Cycle Agenda Setting Policy Discussion Implementation Policy Formation Provision of Means Policy Acceptance Continuous Evaluation

45 Open Budget Process Combination of all methods All policy phases Budget! Prepara1on! Info:!Data!and!Visualisa1on! Innova1on!Mile!&!PlaLorm! Policy!!Discussion!=>!Decision! Feedback!Loop! Statement!of! Budget!Account! Budget! Implementa1on! Ongoing!Evalua1on! Info:!Data!and!Visualisa1on! Innova1on!Mile!&!PlaLorm! Evalua1on! Info:!Contracts!,!PM,!invoices,!D>Proc.!! Innova1on!Mile!&!PlaLorm! Provision!of!Means!&!Implementa1on!

46 Cases$ Opera,onal$Layer$

47 Seamless Crowd Cooperation Innovation & Cooperation Mile Implementation Phase

48 Activities on the ground Innovation and Cooperation Mile Implementation Phase

49 1,607 Datensätze 274 Apps, Visualisierung Source: Donau-Universität Krems, Centre for E-Governance Data Source:

50 II. (Open) Data Analytics

51 Importance of the application domains based on the survey respons

52 II. Co-Production

53 VII. Technische Maßnahmen

54 OGD in der IT Architekturen berücksichtigen Power of Information Task Force blog, June 20

55 Standardisierung von internen & externen Anwendungsschnittstellen ( APIs ) Interne & externer Nutzen: Beschleunigung der Entwicklung von internen externen Applikationen

56 Daten Qualität & Schema

57 Mobil App für Veranstaltungen

58 VIII. Aktuelle Beispiele

59 Zielgruppenspezifische Öffentlichkeitsarbeit Kombinierte zentrale koordiniert aber dezentral durchgeführte zielgruppenorientierte Kommunikationsmaßnahmen

60 Open Government Data Innovation & Cooperation Platform Implementation Phase Result: 1 Data-Set: 15 Apps Dataset: Source

61 Open$Leadership$ Cultural$Layer$

62 Open Architecture Data 4 Participatory Design 3 5 Implicite Participation 6 Experiments Simple Starting Point 2 Interoperability and Standards 1 7 Best Practice Projects

63 Iterative Development Loop Build! experiments! Learn! persevere! Measure! metrics!!failure Culture The Lean Startup: Eric Ries, 2011

64 low Cultural Shift Impact high Public*Administra<on*N*new* Open! by!default! Data/Info:!Obliga1on!to!provide! Data!driven! Knowledge!focused!and! par1cipa1ve!(people!driven)! low Costs high Tradi<onal*Public* Administra<on** Closed! Data/Informa1on:!Debt!to!be! collected! Applica1on!driven! Silos!

65 The Open Organization: by Jim Whitehurst

66 Change your architecture

67 *Strategie* Offenheit* CommunityN Management* Technologie*

68 by Latif et al., TU Graz, Linked Data Value ChainValue Chain

69 Potentielle NutzerInnen Quelle: Chancen und Risiken der kommerziellen Nutzung von Linked Open Government Data, Amt24, 2011.

70 Open Data Wertschöpfungskette Business** User* Generierte* Daten* Externe*QV/QS* Nutzer* Feedback* Quelle: Chancen und Risiken der kommerziellen Nutzung von Linked Open Government Data, Amt24, 2011.

71 Öffnen des Politik-Prozesses Evalua<on* Agenda*Se`ng* Policy!! Cycle! Implemen<erung* Formulierung* Vgl. P. Müller

72 Policy Cycle Agenda Setting Policy Discussion Implementation Policy Formation Provision of Means Policy Acceptance Continuous Evaluation

73 Politische Freiheit - Mitbestimmung Evalua<on* Wirkungsorien1erung! Durch!BürgerInnen! Policy!! Cycle! Agenda*Se`ng* Ini1a1vrechte! Poli1sche!Pla_ormen! Soziale!Medien! Implemen<erung* Co>Crea1on! Co>Delivery! Co>Decision! Formulierung* Strategieerstellung! Normerzeugung! Mitbes1mmung! Vgl. P. Müller

74 Rechenschaftspflicht - Budgetprozess Budgeterstellung! Rohdaten! Visualisierungen! Verhandlungsprozesse! Rohdaten! Visualisierungen! Mitarbeit! Rechnungs> abschluss! Budgetvollzug!! Verträge! Rechnungen! Entscheidungsprozesse!

75 Immobilien Suche

76 Öffis* Bike2Go* Car2Go* Taxi* Zu*Fuss* Uber* LuSwerte* Verkehrsflus s* Gesundheit* WeOer* Unfallsta<s<k* * cc by tobi_digital

77 Data Governance External* Internal* Open* Data* Databases! Second*&* Third*Party** Data* Open!APIs!/!Cloud!Services!/!!!! File! Systems! Data! SOA!/!MDM! Data! Marts! First* Party** Data* Closed*internal*Data! User* Generated* Content*!! Business!Intell.! Data!Mining! Visualiza1on! Big!Data!Analy1cs!! Source: Donau-Universität Krems, Centre for E-Governance

78 Innova<on*&* Coopera<on*Market* Par1cipa1on! with! the! goal! of! finding! ideas,! aimed! at! a! broad! cycle! for! gathering!! or! working! on! solu1ons.! The! par1cipa1on! ini1ator! decides! about! the! best! solu1on.! Elite!Circle!! A!circle!of!par1cipants!!selected!by! the! government.!! Ini1ator! defines! the! problem! and! works! on! proposals! for! solu1ons,! the! provider!decides!about!solu1ons!.! Innova<on*&* Coopera<on*Pla[orm* Open! plalorm! where!! everyone! can! ini1ate! par1cipa1on,! propose! and! build! solu1ons! for! individual! purposes.!! Consor1um!! A!private!circle!of!the!par1cipa1on! ini1ator! defines! problems,! works! on! proposals! for! solu1ons! and! has! the!power!of!decision!necessary!to! carry!through!the!proposals.! Par1cipa1on! open! closed! Advantages:!Solu1ons!outside!the! usual!ideas!providers! Challenge:! Dealing! with! an! abundance!of!ideas! Precondi<ons:! The! possibility! to! verify!and!aggregate!many!ideas! Advantages:!Solu1ons!of!the!best! experts!in!a!field! Challenge:! Iden1fica1on! of! the! best!experts! Precondi<ons:! The! possibility! to! verify!and!aggregate!many!ideas! Governance! hierarchical! Advantages:! The! control! of! the! par1cipa1on!process!stays!with!the! par1cipa1on!provider/ini1ator.!! Challenge:! Asking!! the! right! ques1on.! Precondi<ons:! Strong! anchoring! within! the! user! community,! in! order! to! understand! their! problems! and! par1cipa1on! tools! for!distribu1ng!work! flat! Advantages:! The! challenge! to! generate! new! ideas! lies! within! the! community! Challenge:!Generate!ideas!that!are! of! advantage! for! the! par1cipa1on! provider/ini1ator! Precondi<ons:!Processes!and!tools! that! keep! par1cipants! on! the! intended!course!! Developed for Businesses: Pisano & Verganti

79 Main components of the Data-Services Ecosystem

80 Relationship of Data Analytics Techniques to application domains

81 for the publication and use of open data in a particular practical case?