The term stakeholder is in the context of open data often used when identifying “barriers” (Barry & Bannister, 2013; S. Martin, Foulonneau, Turki, & Ihadjadene, 2013) or “impediments” (Zuiderwijk, Janssen, et al., 2012) to open data. The literature about barriers and impediments often makes only implicit reference to stakeholder roles or treats them very broadly, e.g. as users. Furthermore, the identified barriers are to some extent as structural, legal and technical and not directly related to specific stakeholder roles. However, the literature is very extensive and detailed in identifying impediments which pose a challenge specifically for users of open data (see esp. Zuiderwijk, Janssen, et al., 2012). This also gives some indication for requirements users have. The table below gives a summarised overview of the barriers found in the literature, drawing upon a modified classification of Zuiderwijk et al. (2012).

Category Barrier, Impediment etc. Sources
Availability  andQuality Completeness: Incomplete, partially available data; lacking data; unpublished data; non- original, processed data; insufficient meta data Blakemore & Craglia, 2006; Zuiderwijk, Janssen, et al., 2012; Zuiderwijk, Jeffery, et al., 2012
Timeliness: outdated, non-updated data M. Janssen et  al.,  2012Lee  &  Kwak, 2012
Relevance:   largely   irrelevant,   uninteresting data Blakemore & Craglia, 2006; M. Janssen et al., 2012; Zuiderwijk, Janssen, et al., 2012
Quality:   Bad,   inaccurate   or   indeterminable data quality, ontologies and meta data Conradie & Choenni, 2012; Huijboom & van den Broek, 2011; M. Janssen et al., 2012
Accessibility and Findability Search Barriers: finding the proper dataset; no advanced search facilities; missing, incomplete or incorrect meta data Conradie & Choenni, 2012; M. Janssen et al., 2012; Zuiderwijk, Janssen, et al., 2012
Access   Barriers:   Registration   requirements;requests necessary; exclusive, restrictive access Blakemore & Craglia, 2006; M. Janssen et al., 2012; Meijer & Thaens, 2009; Napoli & Karaganis, 2010; Zuiderwijk, Janssen, et al., 2012
Cost Barriers: fees due; inappropriate pricing Huijboom & van den Broek, 2011; K. Janssen, 2011; S. Martin et al., 2013; Zuiderwijk, Janssen, et al., 2012
Fragmentation:           fragmented           sources; duplicated data Conradie  &  Choenni,  2012;  Vickery  & Wunsch-Vincent, 2006
Usability Licensing:    Restrictive     licenses;    incoherent licenses; incomprehensible licenses K. Janssen, 2011; S. Martin et al., 2013; Zuiderwijk, Janssen, et al., 2012
Machine-Readability:     non-machine-readable formats; lack of good API Zuiderwijk, Janssen, et al., 2012
Reliability: no reliable long term preservation of data; unclear provenance of data and trustworthiness of source S. Martin et al., 2013; O’Riain, Curry, & Harth, 2012; Zuiderwijk, Janssen, et al., 2012
Manual      Effort:       various      arbitrary       data transformations necessary; data cleaning Ding     et     al.,      2011;     M.     Janssen, Charalabidis, & Zuiderwijk, 2012
Linking: difficulties to link data and meta data M. Janssen et al., 2012; King, Liakata, Lu, Oliver,  &  Soldatova, 2011; Zuiderwijk, Janssen, et al., 2012
Interoperability: open data infrastructures not interoperable with other systems; fragmentation of software and applications M. Janssen et al., 2012
Compatibility of Vocabulary and Structure: different definitions of data, terminologies; too much vocabularies; general lack of standards Conradie & Choenni, 2012; Huijboom &van den Broek, 2011; M. Janssen et al., 2012;  S.  Martin  et  al.,  2013;  Zhang, Dawes, & Sarkis, 2005
Understand- ability Meaning: domain knowledge necessary to understand data; jargon in data and meta data; meaning and meaningful interpretation of data are unclear; statistical expertise necessary M. Janssen et  al.,  2012;  King  et  al., 2011; S. Martin et al., 2013; Zuiderwijk, Jeffery, & Janssen, 2012
Validity: methods of data gathering unclear Zuiderwijk & Janssen, 2012
Support: no expert advice available; lack of service by data providers to use raw data; lack of dialogue between data producers and consumers M. Janssen et al., 2012; S. Martin et al., 2013
Visualisation:  data  and  meta  data  are  not visualised Zuiderwijk, Janssen, et al., 2012


The barriers listed in table 2.1 are largely expressed from a user point of view. However, they also give some indication of the multitude of other stakeholders involved in the more general decisions, e.g. regarding licensing, costs and fragmentation/consolidation of sources. As Huijboom and van den Broek (Huijboom & van den Broek, 2011) state, “whereas the drivers lie predominantly outside government, the barriers are within government organisations.” Whether or not this is entirely true, various barriers mentioned in the literature indirectly refer to the vertical and horizontal differentiation between different governmental levels. Especially regarding policy making in the context of open data, these levels’ roles are rarely appreciated. Accordingly, open data process models treat the public sector as a uniform entity. However, differences i.a. in regard to authority, amount of data, technical capabilities are notable among the local level, the regional level, the national level, the supranational level (e.g. the EU), the international level (e.g. the World Bank), and non-political levels (e.g. academic institutions). Predominantly the legal literature appreciates the role this administrative and political complexity plays (K. Janssen & Dumortier, 2003; K. Janssen, 2011).

One core element of open data is making data publicly available that is previously stored and kept internally by public agencies. Similarly, inter-agency information sharing involves making data available beyond the organisation’s boundaries, if only within the public administration. Here, previous research highlights how different values and cultures within public administration impact on information sharing practices (Yang & Maxwell, 2011). For example the kind of data that is collected and stored, how data are defined and how it shall be interpreted and used is affected by professional values  (Dawes,  Cresswell,  &  Pardo,  2009).  Making  data  available  thus  means  giving  up  the prerogative of definition and interpretation, because the data can be interpreted and used very differently in ways, what public agencies might view as detrimental (Pardo, Cresswell, Thompson, & Zhang, 2006). The completeness, accuracy of information and the misusing of the data by others which might even incur liabilities is considered as a further barrier for data providers (Yang, 2012). The lack of resources, especially staff shortage, is also mentioned as to inhibit data sharing. (Landsbergen Jr. & Wolken Jr., 2001; Yang, 2012). On the other hand trust has been shown to positively impact knowledge sharing practices (Willem & Buelens, 2006). These factors can be presumed to have a negative effect on open data as well, especially since most of them should be more  favourable  within  the  public  sector  then  beyond  sector  boundaries.  However,  how  these factors impact on implementing open data and what the results are, is so far barely understood.


References can be found here: OpenDataMonitor Project – Shared References