This book looks at open data practices historically and from the perspective of those currently involved in advocating for making government data freely available. Based on interviews with practitioners, users and evangelists across three Australian-based case studies illustrating contemporary open data practices, this book discusses how open data has evolved, why certain barriers to openness exist and what the future of open data might look like. It highlights both the challenges and approaches to 'best practice' in government departments and agencies as they adapt to changing data ecosystems and public expectations around access, transparency, risk and responsible stewardship.
Provenance refers to the place of origin or earliest known history of a thing. It refers to the custodial history of objects. It is a term that is commonly used in the art-world but also has come into the language of other disciplines such as computer science. It has also been applied in reference to the transactional nature of objects in supply chains and circular economies. In an interview with Scotland's Institute for Public Policy Research, Adam Greenfield suggests that provenance has a role to play in the "establishment of reliability" given that a "transaction or artifact has a specified provenance, then that assertion can be tested and verified to the satisfaction of all parities" (Lawrence). Recent debates on the unrecognised effects of digital media have convincingly argued that data is fully embroiled within capitalism, but it is necessary to remember that data is more than just a transactable commodity. One challenge in bringing processes of datafication into critical light is how we understand what happens to data from its point of acquisition to the point where it becomes instrumental in the production of outcomes that are of ethical concern. All data gather their meaning through relationality; whether acting as a representation of an exterior world or representing relations between other data points. Data objectifies relations, and despite any higher-order complexities, at its core, data is involved in factualising a relation into a binary. Assumptions like these about data shape reasoning, decision-making and evidence-based practice in private, personal and economic contexts. If processes of datafication are to be better understood, then we need to seek out conceptual frameworks that are adequate to the way that data is used and understood by its users. Deborah Lupton suggests that often we give data "other vital capacities because they are about human life itself, have implications for human life opportunities and livelihoods, [and] can have recursive effects on human lives (shaping action and concepts of embodiment . selfhood [and subjectivity]) and generate economic value". But when data are afforded such capacities, the analysis of its politics also calls for us to "consider context" and "making the labour [of datafication] visible" (D'Ignazio and Klein). For Jenny L. Davis, getting beyond simply thinking about what data affords involves bringing to light how continually and dynamically to requests, demands, encourages, discourages, and refuses certain operations and interpretations. It is in this re-orientation of the question from what to how where "practical analytical tool[s]" (Davis) can be found. Davis writes: requests and demands are bids placed by technological objects, on user-subjects. Encourage, discourage and refuse are the ways technologies respond to bids user-subjects place upon them. Allow pertains equally to bids from technological objects and the object's response to user-subjects. (Davis) Building on Lupton, Davis, and D'Ignazio and Klein, we see three principles that we consider crucial for work on data, darkness and light: data is not simply a technological object that exists within sociotechnical systems without having undergone any priming or processing, so as a consequence the data collecting entity imposes standards and way of imagining data before it comes into contact with user-subjects; data is not neutral and does not possess qualities that make it equivalent to the things that it comes to represent; data is partial, situated, and contingent on technical processes, but the outcomes of its use afford it properties beyond those that are purely informational. This article builds from these principles and traces a framework for investigating the complications arising when data moves from one context to another. We draw from the "data provenance" as it is applied in the computing and informational sciences where it is used to query the location and accuracy of data in databases. In developing "data provenance", we adapt provenance from an approach that solely focuses on technical infrastructures and material processes that move data from one place to another and turn to sociotechnical, institutional, and discursive forces that bring about data acquisition, sharing, interpretation, and re-use. As data passes through open, opaque, and darkened spaces within sociotechnical systems, we argue that provenance can shed light on gaps and overlaps in technical, legal, ethical, and ideological forms of data governance. Whether data becomes exclusive by moving from light to dark (as has happened with the removal of many pages and links from Facebook around the Australian news revenue-sharing bill), or is publicised by shifting from dark to light (such as the Australian government releasing investigative journalist Andie Fox's welfare history to the press), or even recontextualised from one dark space to another (as with genetic data shifting from medical to legal contexts, or the theft of personal financial data), there is still a process of transmission here that we can assess and critique through provenance. These different modalities, which guide data acquisition, sharing, interpretation, and re-use, cascade and influence different elements and apparatuses within data-driven sociotechnical systems to different extents depending on context. Attempts to illuminate and make sense of these complex forces, we argue, exposes data-driven practices as inherently political in terms of whose interests they serve.
