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

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

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

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

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

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

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

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

Ishikawa diagram summarising risks and
barriers related to data opening
12/07/2013 Presentation Tudor 9

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

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.