Data Hub: Best Practices Review CITY FUTURES RESEARCH CENTRE, UNSW SYDNEY UNIVERSITY PARTNERS Contents INTRODUCTION 4 METHODS 7 Framework 7 Literature search 11 BEST PRACTICE SUMMARY 11 THE BEST PRACTICES 13 People and culture 13 1. Establish an organisational strategy to implement a knowledge management system 13 2. Provide support for training and upskilling 13 3. Engage with internal staff and wider community to establish trust 13 4. Address leadership: establish information governance 14 Process 15 5. Conduct and communicate a data inventory 15 6. Establish data agreements 15 7. Publish standards and tools 15 8. Document and publish project information 15 9. Establish protocols addressing security and privacy concerns 16 10. Seek client feedback on the IM system and its implementation 16 Technology 16 11. Adopt open data formats 16 12. Adopt standards for metadata 18 13. Make datasets discoverable via catalogue 20 14. Adopt 5-Star Open Data Scheme 21 15. Publish web services for data visualisation and analysis 21 Page 2 CASE STUDIES 22 1. USA – Roadmap to GeoPortal.gov 22 2. Australian Spatial Data Infrastructure 25 3. New South Wales Government Information Systems 27 4. Western Australia’s Open Data Initiative 30 5. Australian Urban Research Infrastructure Network – AURIN 32 6. Australian Healthcare Data Linkage Systems 34 7. City of Greater Geelong 36 8. European Union – INSPIRE 38 9. London Datastore 40 CONCLUSION 42 REFERENCES 43 APPENDIX: ISO/TC211 GEOGRAPHIC STANDARDS 45 Data Hub: Best Practices Review - City Futures Research Centre, UNSW Sydney Page 3 INTRODUCTION In the age of smart cities, governments are investing in the growth of data-driven platforms, services and The last two decades have seen an expansive analytics in order to improve the design, planning, proliferation of spatial data and more recently big management and evaluation of policies and data. This is set to continue with the emergence of projects for sustainable community development. the internet of things (IoT) where there is expected It is in support of this endeavour that agencies like to be more than 50 billion IoT devices by 2020. Data UrbanGrowth NSW need to play a critical role. proliferation and the digitisation of our cities is driving the Smart Cities agenda (Kitchin 2013). In order to achieve the aforementioned smart city goals, governing bodies must exercise organisational The aim of a smart city is to “make better use of will and capacity to harvest intelligence from data public resources, increasing the quality of the services and to translate this resource into policy formation offered to citizens, while reducing the operational and delivery, and new organisations of government. costs of the public administrations” (Zanella et al Effective data and information management (IM) is 2014). The goals for smart cities are articulated integral to this exercise, and perhaps the keystone to further by Batty et al (2012 p.481): governance in the digital age. • Develop new understanding of urban problems It is useful to apply Lee’s (1988) definition of Information Resource Management to describe the • Develop effective and feasible ways to coordinate collection of activities and assets that comprise IM urban technologies at an organisational level: • Set models and methods for using urban data [It] is the planning, organising, controlling, across spatial and temporal scales securing, and integrating of the organisation’s information resources, including; internal and • Develop new technologies for communication external information; software; hardware; and dissemination facilities; information systems budget; and information systems policies, procedures and • New forms of urban governance and organisation methods. • Define critical problems related to cities, transport, energy With the addition of information communication and dissemination to relevant external parties, • Identify risk, uncertainty and hazards in the this definition captures the various aspects of IM smart city relevant to the scale and operation of agencies like UrbanGrowth NSW. Page 4 Purpose of Review a project that supports UrbanGrowth NSW’s City The business of establishing an IM system can be Transformation Lifecycle agenda by establishing a a challenge considering the variety of information clear understanding of the digital ecosystem and assets and data formats, and the practical and legal user requirements needed to inform future forward conditions under which they may be acquired, shared, investment in data delivery services. analysed, re-distributed and communicated. It requires The Connected City Data Hub Scoping Study an orchestration of the organisation’s assets (e.g. ICT and Roadmap is a collaborative project between resources, personnel capacity and know-how, budget, UrbanGrowth NSW, University of New South Wales, and facilities) and operational procedures and policies. University of Western Sydney, and University of This review is a complementary activity to the Wollongong. The following diagram contextualises the Connected City Data Hub Scoping Study and Roadmap, project in relation to the components of UrbanGrowth’s City Transformation Lifecycle initiative. Figure 1: Context of the UrbanGrowth Data Hub in UrbanGrowth’s City Transformation Lifecycle initiative Infrastructure Data NSW Analytics Centre Industry Greater Sydney Commission University of NSW Urban Growth NSW Western Sydney University Data University of City Transformation Wollongong Visualisation Mapping Data Hub University of Technology Mapping Sydney Thinking Cities University of Newcastle Funding University CIties of Sydney Macquarie University Building Cities Living Cities City Data Hub seeks to review existing datasets The diagram below maps a vision for an IM system against UrbanGrowth’s information management to support UrbanGrowth’s operations. The map needs and assist in identifying where data agreements summarises the components and functions of an and partnerships with custodians may be beneficial IM enterprise. for UrbanGrowth. Data Hub: Best Practices Review - City Futures Research Centre, UNSW Sydney Page 5 Portfolio Projects CoP filesharing news publications Collaboration Social media Information Knowledge brokers standards Brokering City Transformation Data Hub metadata Body of Data Knowledge Open Restricted Visualisation Analytics Virtual Reality Mapping 3D City Dashboards Figure 2: Components of UrbanGrowth’s information management vision This review will present case studies of IM system The next section provides a description of the implementations deployed internationally and within framework and method used to produce this review. Australia. It seeks to summarise best practices in This is followed by a summary of best practices, and information and data governance that would be case studies that exemplify such practices in their IM applicable to UrbanGrowth NSW. implementations. Page 6 METHODS 3. Technology This dimension addresses requirement of ICT Framework instrumentation to be able to collect spatial data, as well as the integration of data and Dimensions of Information Management system implementation metadata formats, and collaborative platforms/ The review categorises activities related to the software. The technology used to store, implementation of IM systems into three dimensions: catalogue and make data accessible must allow People/Culture, Process and Technology. This for the data asset’s interoperability between categorisation is adopted as a summary of the different platforms, across different agencies IM facets discussed in literature concerning the and perhaps even across different time periods. integration of ICT into an organisation’s business and The system architecture must be able to provide operations (Weippert & Kajewski 2004; Cabrera et al contingencies for making existing data accessible 2001; Gold et al 2001):be transformative, as can be and usable by other existing systems and other seen in the table below. potential uses of the data in the future, as well as have mechanisms to allow the integration of data 1. People/Culture from various sources and accommodation for new types of data. This dimension addresses the ‘human capital’ part of IM; it includes the technical skills and Capability Maturity Model competencies required, the fostering of good data The review will apply the capability maturity model management habits and sense of responsibility in recommending the order of best practices to for data, and the provision of incentives for such apply. This kind of framework has been used behaviour; as well as presence of a collaborative by businesses in the IT industry to assess their culture at work to promote knowledge transfer. organisational performance, as well as in other project Komninos (2011) describes this aspect of IM management contexts. The authors believe that this as the dimension where ‘amplification’ can kind of open-ended framework may be advantageous take place; where mechanisms are put in place for organisations that are seeking a roadmap to to ensure that members of the enterprise incrementally optimise their IM program. or community that run, support and use the Given that the urban data landscape is subject knowledge infrastructure have the skills or can to disruption in digital delivery services and that upskill to be able to contribute and co-create enterprises face risks associated with digital information—thereby amplifying the capabilities obsolescence—an open-ended framework that of the information and data infrastructure.