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Open Data: Barriers, Risks, and Opportunities


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Despite the development of Open Data platforms, the wider deployment of Open Data still faces significant barriers. It requires identifying the obstacles that have prevented e-government bodies either from implementing an Open Data strategy or from ensuring its sustainability.
This paper presents the results of a study carried out between June and November 2012, in which we analyzed three cases of Open Data development through their platforms, in a medium size city (Rennes, France), a large city (Berlin, Germany), and at national level (UK). It aims to draw a clear typology of challenges, risks, limitations and barriers related to Open Data. Indeed the issues and constraints faced by re-users of public data differ from the ones encountered by the public data providers. Through the analysis of the experiences in opening data, we attempt to identify how barriers were overcome and how risks were managed. Beyond passionate debates in favor or against Open Data, we propose to consider the development of an Open Data initiative in terms of risks, contingency actions, and expected opportunities. We therefore present in this paper the risks to Open Data organized in 7 categories: (1) governance, (2) economic issues, (3) licenses and legal frameworks, (4) data characteristics, (5) metadata, (6) access, and (7) skills.

Sébastien Martin 1, Muriel Foulonneau 2, Slim Turki 2, Madjid Ihadjadene 1
1 Université Paris 8, Vincennes-Saint-Denis, France
2 PRC Henri Tudor, Luxembourg, Luxembourg

Published in: Technology, Education
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Open Data: Barriers, Risks, and Opportunities

  1. 1. Open Data: Barriers, Risks, and Opportunities Sébastien Martin1, Muriel Foulonneau2, Slim Turki2, Madjid Ihadjadene1 1 Université Paris 8, Vincennes-Saint-Denis, France 2 PRC Henri Tudor, Luxembourg, Luxembourg ECEG 2013 13-14 June Como, Italy
  2. 2. Context & Objectives Study carried out between June and November 2012 • obstacles preventing public bodies either from implementing an Open Data strategy or from ensuring its sustainability • how barriers were overcome and how risks were managed 7 categories of identified risks and barriers 12/07/2013 Presentation Tudor 2 Medium size city Rennes, France Since Oct. 2010 Large city Berlin, Germany Since Sep. 2011 National level United Kingdom Since Jan. 2010
  3. 3. 1 – Governance Open Data vs. Open Government: a misunderstanding • (in)adequate interpretation of de-contextualized data > hostile attitudes and reluctance of civil servants. • [CA] mobilize internal and external stakeholders, civil society organizations; identify reasons of reluctance. Reluctance of civil servants • Lack of perseverance in political behaviors > re-users doubts about Open Data policy sustainability. • [CA] Encourage cultural shift in administrations Inconsistency of public policies • Choices at local level regarding reuse conditions and formats, datasets > risk of fragmentation • [CA] Find a balance between different political levels Relevant administrative level • [CA] Facilitate communication between providers and re-users. Lack of dialogue between data providers and re-users
  4. 4. 2 - Economic issues • Implementation costs: hardware, software and human resources. • [CA] Small communities: mutualize expenses or rely on national infrastructures. • [CA] Assessing the costs of not opening • [CA] Share part of the costs with other Open Data platforms Cost of opening data • Debate on how to calculate RoI > risk for the sustainability of Open Data initiatives. • [CA] Adopt a realistic approach to costs and benefits • [CA] Encourage stakeholders who use Open Data to indicate that use Benefits and RoI • Distortion in competition between companies /risk for business models of companies already using public data. Sustainable business models for data production 12/07/2013 Presentation Tudor 4
  5. 5. 3 - Licenses and legal frameworks • Mutually incompatible reuse licenses and conditions > risk of fragmentation • [CA] Sensitization of stakeholders • Strengthen the role of the agency that organizes Open Data Heterogeneous licenses across datasets • [CA] Release data complying with definition of openness • [CA] Collect re-users concerns and modify licenses if barriers are too constraining Licence is not open enough • Several organizations claim ownership or control over datasets and opening conditions • [CA] Governance choices Stacking of rights over individual datasets • [CA] Data anonymisation Privacy 12/07/2013 Presentation Tudor 5
  6. 6. 4 - data characteristics • Dependence of data producers on public funding > suspicions on data accuracy (sensitive to political pressure) • [CA] Clarify context of data creation process Data accuracy • Data quality as result of high quality production process • [CA] Stabilize funding for creation of data and promote crowdsourcing sensitivity of the datasets to financial aspects • Proprietary formats incompatible with each other > conversion difficulties • [CA] Publish datasets in various formats Data available in heterogeneous formats 12/07/2013 Presentation Tudor 6
  7. 7. 5 - metadata • Metadata often formatted according to the Dublin Core and DCAT vocabularies • [CA] Join harmonization initiatives Lack of single standard to describe datasets • Lack of metadata, of mechanisms to ensure quality of metadata, and lack of traceability of aggregated datasets. • [CA] Gather metadata needs from re-users; • [CA] implement mechanisms to trace the provenance and use of datasets. Incomplete metadata 12/07/2013 Presentation Tudor 7 6 - access • [CA] Provide all the data through an API capable of reporting access and use Balance between free access and data use monitoring
  8. 8. 7 – skills of users and re-users • Services created from data published from different countries, sufficiently understood by re-users to be retrieved and used without any risk of misinterpretation. • [CA] Publish data in multiple languages ​​and / or fix the issue through metadata Language barrier • [CA] Development of data visualisations to ease understanding and interpretation of phenomena described in datasets. • [CA] Training to re-users during events around Open Data Skills related to information literacy and domain knowledge • [CA] Assessing metadata formats known by users Re-users are unfamiliar with metadata 12/07/2013 Presentation Tudor 8
  9. 9. Ishikawa diagram summarising risks and barriers related to data opening 12/07/2013 Presentation Tudor 9
  10. 10. Conclusion & perspectives Myths accompanied of Open Data development • pragmatic approach grounded in demonstrated benefits and assessment of risks associated with implementation of an Open Data strategy Analyzing barriers and potential benefits, prevention measures and contingency actions which can be taken are proposed. Not all types of data raise same risks and opportunities. Future work • Analyse different types of datasets and services developed and • How to optimize RoI of Open Data initiatives by selecting relevant datasets • Understand process by which successful open data driven services can be built 12/07/2013 Presentation Tudor 10
  11. 11. Open Data: Barriers, Risks, and Opportunities Sébastien Martin, Muriel Foulonneau, Slim Turki, Madjid Ihadjadene ECEG 2013 13-14 June Como, Italy Thank you for your attention.