OPEN DATA INTERMEDIARIES IN DEVELOPING COUNTRIES François van Schalkwyk, Michael Cañares, Sumandro Chattapadhyay & Alexander Andrason Open Data Research Symposium, Ottawa, Wednesday 27 May 2015
INTRODUCTION » Emerging Impacts of Open Data in Developing Countries (ODDC) » 17 case studies » One of the overarching findings from across the case studies: “ Intermediaries are vital to both the supply and the use of open data. ... Intermediaries can create data, articulate demands for data, and help translate open data visions from political leaders into effective implementations. Traditional local intermediaries are an important source of information, in particular because they are trusted parties.” “ Civil society and entrepreneurs interacting with government through open data projects can help shape government data practices. This makes it important to consider which intermediaries gain insider roles shaping data supply.” 2
RESEARCH QUESTION How do open data intermediaries promote and/or enable the flow of open data in developing country contexts in order to increase the probability of open data use and impact? 3
DEFINITION OF OPEN DATA INTERMEDIARIES An open data intermediary is an agent 1 positioned at some point in a data supply chain that incorporates an open dataset, 2 positioned between two agents in the supply chain, and 3 facilitates the use of open data that may otherwise not have been the case. 4
THEORETICAL FRAMEWORK Bourdieu’s theory of space, fields, situations, habitus and capital as an explanatory framework of interactions that characterize actors, including intermediaries, in social systems. 5
CAPITAL » Not just economic capital » But a wider system of exchanges whereby assets of different kinds are transformed and exchanged » Capitals correspond to the accumulation and/or convertability of advantages and are capable of discriminating agents because of their distinct positions in a field 6
SPECIES OF CAPITAL 1 Economic capital: economic assets (e.g. monetary value) 2 Cultural capital: forms of knowledge, taste, language (e.g. competencies, qualifications) 3 Social capital: institutionalised connections or social networks with which an individual is bestowed (friends, acquaintances, affiliations, memberships) 4 Symbolic capital: any other form of capital that can be exchanged (e.g. respect, reputation and fame) 5 New forms: technical capital (Zhang 2010) and scientific capital (Langa 2006) that are field-dependent. 7
HYPOTHESIS Using Bourdieu’s ideas as a narrative model for intermediaries, the following can be postulated: The general environment in which data systems and their transmission take place in developing countries (with the structures found in a state, power relationships, exiting individuals, physical and social arrangements, etc.) can be viewed as a relatively autonomous field. Each particular case of transaction constitutes a situation s in this general frame, in which two (or more) agents are involved: an agent α (possibly dominant due to possession of an asset in the form of data) with a particular habitus and capital (represented by a dynamic function f( α )) and another agent β (possibly dominated due to a lack of material or symbolic resources expressed in general terms as a deficit) also with a determined habitus and capital (function f( β )). Both functions solve for the two agents, possibly predicting their most likely paths in the field and responses to its structure and possible situations in which they can actually engage. However, the relation between the two agents is possible in the situation s only (or principally) because an intermediary actor γ (with his or her own habitus and capital, and path f( γ )) emerges and affords for this situation in which the habitus of the agents α and β can meet and a transfer or conversion of capitals can take place. The more the path f(y) intersects with the path f( α ) and f( β ) – i.e. the more proximate it is to the both sides of a transaction – the more likely it is that such a transaction will be successful. 8
agent α asset [data] intermediary γ fi eld situation s with capital z agent β defi cit 9
METHOD 1 17 ODDC case studies 2 Data were collected on 32 open data intermediaries: 27 from Asia and 5 from Africa. 3 Textual analysis to establish the two agents between which an intermediary is located, followed by an estimation of how the intermediary is able to connect the two agents – in other words, deducing what types of capital the intermediary possesses to allow for a connection to be made. 4 Limitations: using secondary data 10
FINDINGS Types of capital possessed by open data intermediaries in order to facilitate data flow Type of capital (n=32) Economic Cultural Social Symbolic Technical 9% 6% 31% 3% 97% 11
FINDINGS ctd. » Our findings point to the value of different types of capital in connecting data supply and use. » They also point to the limits of an overreliance on technical capital in connecting users with open data. 12
FINDINGS ctd. » These cases point to what we believe is an often overlooked and critical dimension in open data intermediation: intermediation does not only consist of a single agent facilitating the flow of data in an open data supply chain; multiple intermediaries may operate in an open data supply chain, and the presence of multiple intermediaries may increase the probability of use (and impact) because no single intermediary is likely to possess all the types of capital required to unlock the full value of the transaction between the provider and the user. 13
agent α asset EXAMPLE GOV open data DATA D intermediary γ 3 IDS CS CHET technical capital OD intermediary γ 1 OD intermediary γ 2 social capital symbolic capital HE open data closed data planners agent β 14 deficit
CONCLUSION » It is hoped that these two insights not only provide fertile ground for further research but that they will make funders, policy-makers and advocates who work in the area of open data more attuned to how open data intermediation needs to be arranged to ensure the realisation of the oft-lauded benefits of open data. 15
THANK YOU François van Schalkwyk francois@compressdsl.com @francois_fvs2 The funding for this work has been provided through the World Wide Web Foundation 'Exploring the Emerging Impacts of Open Data in Developing Countries' research project, supported by grant 107075 from Canada’s International Development Research Centre (web.idrc.ca). Find out more at www.opendataresearch.org/emergingimpacts 16
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