Bridging the Contradictions of Open Data
Ronald Meijer 1, Sunil Choenni 1, 2, Roexsana Sheikh Alibaks 1 and Peter Conradie 2 1 Research and Documentation Centre – Ministry of Security and Justice, Den Haag, The
Netherlands 2 Rotterdam University of Applied Sciences – Creating 010, Rotterdam, The Netherlands
r. f. meijer @ minvenj. nl r. choenni @ minvenj. nl; r. choenni @ hr. nl r. sheikh. alibaks @ minvenj. nl p. d. conradie @ hr. nl
Abstract: For a successful public value strategy, the elements“ public values / strategic goals”, authorizing environment” and“ operational capability” should be coherently aligned. In this paper, we discuss how we have aligned these elements in the context of Open Data. We focus on the relationships between Open Data and public values, in particular, trust, transparency, privacy and security. Several contradictions exist between these values. To succeed, Open Data policy has to reconcile these values. For reconciliation purposes, we introduce the notion of precommitment, which is a restriction of one’ s choices. Precommitment is conceptualized as a policy‐instrument whereby an organization imposes some restraint on its policy in order to restrict the extent to which values may conflict and stakeholders have to worry about the trustworthiness of that policy. We demonstrate how precommitment ‐ implemented as a data request procedure – combined with a proper data infrastructure for Open Data may reconcile potentially conflicting values.
Keywords: open data, trust, privacy, precommitment, data infrastructure
1. Introduction
Open data( OD) is gaining importance in recent years. This increase of importance is taking place in the context of a growing demand for openness. Governments and governmental organizations plead for more openness, e. g. the Obama Administration and the European Union( Zuiderwijk et al. 2012)( Kulk et al. 2012)( ROB 2012). But also the scientific community is calling for more openness with its own research data. Openness is viewed as a means to contribute to transparency and via transparency, it is assumed to contribute to trust of civilians and other stakeholders amongst the government and in science( Zuiderwijk et al. 2012)( Kulk et al. 2012)( ROB 2012)( Schuyt 2012)( Rajamäki 2012). On the other hand, openness may lead to privacy breaches and security violations( Braak, et al. 2012)( Gutmann et al. 2008)( Kalidien et al. 2010)( Kulk et al. 2012).
In line with the increase of the importance of OD, several OD initiatives are going on at the moment( Conradie and Choenni 2012). These OD initiatives all have in common that they are to a certain extent operating with more or less defined goals and objectives, some of which can be categorized as“ public value”( PV). The“ open” side of OD provides access for the public eye. This clearly underlines the idea that OD is about creating“ public” value. OD, as shall be demonstrated in this paper, is not only about creating PV, but also to an important extent, about conserving and maintaining“ public values”. In fact, an OD policy has to reconcile multiple seemingly conflicting values.
The concept of PV is increasingly popular within both academic and practice settings( Williams and Shearer 2011). Some believe that this concept will be the next“ Big Thing” in public management( Talbot 2009). For a successful PV strategy the three elements of Moore’ s famous“ strategic triangle”, i. e.“ public values / strategic goals”,“ authorizing environment” and“ operational capability” must be brought into coherent alignment( Moore 1995)( Williams and Shearer 2011).
In this paper, we discuss how we have aligned the before‐mentioned PV elements for an OD policy in the context of judicial research and registration data at a government research institute. We focus on the relationships between OD and public values, in particular trust, transparency, privacy, and security. Several contradictions exist between these values as will be demonstrated below. In our case, these values form the first of the three strategic triangle elements. The“ authorizing environment” in our case is mainly formed by general policy instructions, and privacy laws and regulations. The“ operational capability” in our case consists of data infrastructure and staff.
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