IntroductionLigo is an open source application that provides a framework for managing and executing administrative data linking projects. Ligo provides an easy-to-use web interface that lets analysts select among data linking methods including deterministic, probabilistic and machine learning approaches and use these in a documented, repeatable, tested, step-by-step process. Objectives and ApproachThe linking application has two primary functions: identifying common entities in datasets [de-duplication] and identifying common entities between datasets [linking]. The application is being built from the ground up in a partnership between the Province of British Columbia’s Data Innovation (DI) Program and Population Data BC, and with input from data scientists. The simple web interface allows analysts to streamline the processing of multiple datasets in a straight-forward and reproducible manner. ResultsBuilt in Python and implemented as a desktop-capable and cloud-deployable containerized application, Ligo includes many of the latest data-linking comparison algorithms with a plugin architecture that supports the simple addition of new formulae. Currently, deterministic approaches to linking have been implemented and probabilistic methods are in alpha testing. A fully functional alpha, including deterministic and probabilistic methods is expected to be ready in September, with a machine learning extension expected soon after. Conclusion/ImplicationsLigo has been designed with enterprise users in mind. The application is intended to make the processes of data de-duplication and linking simple, fast and reproducible. By making the application open source, we encourage feedback and collaboration from across the population research and data science community.

IntroductionThe Province of British Columbia, Canada has established a Data Innovation Program (DI Program) and a Data Science Partnerships Program (DSP Program) to use integrated public-sector data to drive insights into complex policy challenges and support the public good. These programs are a part of the province's new Integrated Data. Objectives and ApproachThe DI Program was built to enable policy decisions based on a more complete picture of the citizen journey across and throughout government programs. It provides a privacy and security framework for corporate data analytics and a cross-government secure research environment. The DSP Program provides analytics and/or project support for high-priority cross-government projects. The opportunity afforded by this approach to policy decision-making is that valuable data and evidence from multiple sectors can be utilized to make positive changes in the lives of citizens. ResultsThe IDO has partnered with cross government experts on a series of pilot projects that used linked data spanning social services, families and households, education, and health and clinical records. Research topics ranged from the prediction of risk of long-term unemployment, to the impact of the foreign home buyers tax, to the effectiveness of labour market programs. Throughout our presentation we will use these projects as case examples to address the benefits and opportunities provided through our citizen-centred, integrated approach. Conclusion/ImplicationsThe future of policy decision-making in terms of service delivery relies on mutually beneficial collaboration and the evidence-based insight available through integrated data. Moving forward, it is essential that researchers across government make the most out of integrated population-level data to solve pressing issues affecting the lives of citizens.