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Objective and Approach At the 2022 IPDLN Conference, we presented work led by the North Carolina Department of Health and Human Services (NCDHSS), in partnership with university-based researchers, to build legal and ethical processes for routine data sharing. This session provides significant updates on the methods, results, and implications of these efforts–namely, that NCDHHS implemented an enterprise-wide data governance process and a legal framework that has enabled timely, impactful use of cross-sector data. We relied on Participatory Action Research and Deliberative Dialogue methods to engage a diverse range of partners in a data landscape overview and the co-creation of new data sharing processes that better enables the enterprise to adapt to a changing world. Results Four key actions were taken as a result of the participatory research process: NCDHHS developed a data strategy, created a data sharing guidebook, staffed their Data Office, and implemented a new legal framework. In addition to describing how these actions support data use across a large US state health and human services agency, we provide three use cases demonstrating the impact of this work. Conclusions Establishing routine data sharing presents legal, technical, and cultural challenges, particularly in large agencies. Through a collaborative, participatory approach, the NCDHHS successfully established enterprise-wide data governance and a legal framework to support data-driven policymaking and, ultimately, improve health outcomes for residents. Implications This research presents a successful, actionable, and replicable framework for developing and implementing processes to support intradepartmental data access, linkage, and use.

Objective and Approach Cross-sector data sharing and linkage can transform information about individuals into actionable intelligence to build stronger, healthier, and more just communities. Yet, the use of cross-sector data can also reinforce legacies of racist policies and produce inequitable resource allocation, access, and outcomes. To avoid this, we must embed data equity practices throughout the data life cycle and provide mechanisms for community voice and input. However, few integrated data systems are doing this well. To combat this challenge, AISP designed a 30-month technical assistance program–the Equity in Practice Learning Community (EiPLC)--to collaboratively develop guidance and models for centering racial equity in data integration. This session provides an overview of our EiPLC Scope & Sequence, a curriculum we designed to guide this work. Currently, 10 jurisdictions in the U.S. are receiving coaching around this curriculum and shifting their data integration practices to advance equity. Results To date, progress has been nonlinear, with positive results. Work in action across the cohorts includes changes to cross-agency data governance, collaborative research agendas, legal agreements, staffing, and community participation practices including. Conclusions While these efforts are nascent, progress is evident and site team members indicate growth as individuals and as site teams. Key features of success include a focus on building relationships, establishing working norms, grappling with racialized histories, and interrogating the role of structural racism and systems of power. Implications The EiPLC provides a successful model for supporting sites to embed data equity principles throughout the data life cycle.

Public agencies routinely collect administrative data that, when shared and integrated, can form a rich picture of the health and well-being of the communities they serve. One major challenge is that these datasets are often siloed within individual agencies or programs and using them effectively presents legal, technical, and cultural obstacles. This article describes work led by the North Carolina Department of Health and Human Services (NCDHHS) with support from university-based researchers to establish enterprise-wide data governance and a legal framework for routine data sharing, toward the goal of increased capacity for integrated data analysis, improved policy and practice, and better health outcomes for North Carolinians. We relied on participatory action research (PAR) methods and Deliberative Dialogue to engage a diverse range of stakeholders in the co-creation of a data governance process and legal framework for routine data sharing in NCDHHS. Four key actions were taken as a result of the participatory research process: NCDHHS developed a data strategy road map, created a data sharing guidebook to operationalize legal and ethical review of requests, staffed the Data Office, and implemented a legal framework. In addition to describing how these ongoing streams of work support data use across a large state health and human services agency, we provide three use cases demonstrating the impact of this work. This research presents a successful, actionable, and replicable framework for developing and implementing processes to support intradepartmental data access, integration, and use.

Introduction This paper presents a Four Question Framework to guide data integration partners in building a strong governance and legal foundation to support ethical data use. Objectives While this framework was developed based on work in the United States that routinely integrates public data, it is meant to be a simple, digestible tool that can be adapted to any context. Methods The framework was developed through a series of public deliberation workgroups and 15 years of field experience working with a diversity of data integration efforts across the United States. Results The Four Questions - Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)? - should be considered within an established data governance framework and alongside core partners to determine whether and how to move forward when building an Integrated Data System (IDS) and also at each stage of a specific data project. We discuss these questions in depth, with a particular focus on the role of governance in establishing legal and ethical data use. In addition, we provide example data governance structures from two IDS sites and hypothetical scenarios that illustrate key considerations for the Four Question Framework. Conclusions A robust governance process is essential for determining whether data sharing and integration is legal, ethical, and a good idea within the local context. This process is iterative and as relational as it is technical, which means authentic collaboration across partners should be prioritized at each stage of a data use project. The Four Questions serve as a guide for determining whether to undertake data sharing and integration and should be regularly revisited throughout the life of a project. Highlights • Strong data governance has five qualities: it is purpose-, value-, and principle-driven; strategically located; collaborative; iterative; and transparent. • Through a series of public deliberation workgroups and 15 years of field experience, we developed a Four Question Framework to determine whether and how to move forward with building an IDS and at each stage of a data sharing and integration project. • The Four Questions—Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)? —should be carefully considered within established data governance processes and among core partners.

Introduction Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation while ensuring that data are secure and privacy is protected. As a contribution to broader efforts to improve data governance and management, in 2020 members of our team published 12 minimum specification essential requirements (min specs) to provide practical guidance for organisations establishing or operating data trusts and other forms of data infrastructure. Approach and Aims We convened an international team, consisting mostly of participants from Canada and the United States of America, to test and refine the original 12 min specs. Twenty-three (23) data-focused organisations and initiatives recorded the various ways they address the min specs. Sub-teams analysed the results, used the findings to make improvements to the min specs, and identified materials to support organisations/initiatives in addressing the min specs. Results Analyses and discussion led to an updated set of 15 min specs covering five categories: one min spec for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. Multiple changes were made to make the min specs language more technically complete and precise. The updated set of 15 min specs has been integrated into a Canadian national standard that, to our knowledge, is the first to include requirements for public engagement and Indigenous Data Sovereignty. Conclusions The testing and refinement of the min specs led to significant additions and improvements. The min specs helped the 23 organisations/initiatives involved in this project communicate and compare how they achieve responsible and trustworthy data governance and management. By extension, the min specs, and the Canadian national standard based on them, are likely to be useful for other data-focused organisations and initiatives.

Objectives This presentation describes a successful framework for developing and implementing processes to support intradepartmental data access, integration, and use in the US. This case study describes on-going streams of work, 2019-present, to implement a new legal framework and data governance approach across a large health and human service agency. Approach This work is conducted in partnership with a state agency and university-based researchers. The case study relies upon participatory action research as the primary approach, using mixed methods to inform inquiry, including interviews and deliberative dialogue to support action. An initial Data Landscape Overview was conducted from 2019-2020 through on-going meetings, document review, a survey of legal agreements, weekly calls with the Department’s Data Office, and structured interviews with 44 individuals. Deliberative dialogue has informed on-going action steps from 2020 to today as we have co-created a data governance roadmap, specifically focused on data governance and a new legal framework. Results This work was performed in the context of a data strategy across five pillars: data governance & legal framework; workforce development & data literacy, data quality, data infrastructure, and data use. We focused on the first pillar—governance and legal frameworks. Drawing upon departmental exemplars and through deliberative dialogue with legal counsel, we built a new legal framework and successfully executed a three-tier legal approach using an Intradepartmental Memorandum of Understanding, Data Sharing Agreement, and Data Use Agreement. To date, we have co-created a department wide Data Sharing Guidebook, which includes a range of data pathways and data request processes; processes for developing high value data asset inventories; parameters for legal use; and clearly defined department-wide data governance—all operationalized within the established legal framework. Conclusion Our work provides a model for large agencies to collaboratively develop and implement data governance (even during a pandemic). We are grounded in the understanding that data sharing is hard, and more relational than technical. Our goal is long-term sustainability, and therefore people have been central through all work streams.

Objectives • Present a conceptual data model for understanding the impacts of COVID on children 0-5 • Outline administrative datasets with relevant indicators that are commonly collected, integrated at the individual level, and include relevant 2-generation linkages • Provide specific considerations for research using the conceptual model Approach This session presents findings from a paper that was collaboratively generated by a workgroup of five integrated administrative data systems (IDS) in communities across the US with expansive data holdings in early education. Contributors have decades of experience in utilizing administrative data for social policy planning and analysis. We started with a thorough literature review, used a national survey data of IDS data holdings collected in 2020, and co-created a conceptual data model for administrative data reuse based upon current practices. Specific care was taken to include community-level changes and adult factors that serve as mediators for children’s outcomes. Results The COVID-19 pandemic made its mark on the entire world, yet little is known about children, aged 0-5, whose lives have been exponentially affected. This conceptual data model presents five developmental resilience pathways (i.e. early learning, safe and nurturing families, health, housing, and financial/employment) that include direct and indirect influencers related to COVID-19 impacts, and the contexts and community supports that can affect child-level outcomes. We then overview commonly available administrative datasets with relevant indicators to be linked at the individual and household level, with discussion of data specific considerations (e.g. access, insufficiency, availability, quality, and linkage). The US response creates a natural experiment of policy and implementation decisions across boundaries, and while US-centric, this approach could be applied internationally. Conclusion The development and use of IDS for research has great potential to inform solutions to our most pressing social problems. Cross-site research studies and maintaining sustainable capacities for long-term research is paramount to understanding the implications of response interventions and to study impacts on child development over time.

The COVID-19 pandemic made its mark on the entire world, upending economies, shifting work and education, and exposing deeply rooted inequities. A particularly vulnerable, yet less studied population includes our youngest children, ages zero to five, whose proximal and distal contexts have been exponentially affected with unknown impacts on health, education, and social-emotional well-being. Integrated administrative data systems could be important tools for understanding these impacts. This article has three aims to guide research on the impacts of COVID-19 for this critical population using integrated data systems (IDS). First, it presents a conceptual data model informed by developmental-ecological theory and epidemiological frameworks to study young children. This data model presents five developmental resilience pathways (i.e. early learning, safe and nurturing families, health, housing, and financial/employment) that include direct and indirect influencers related to COVID-19 impacts and the contexts and community supports that can affect outcomes. Second, the article outlines administrative datasets with relevant indicators that are commonly collected, could be integrated at the individual level, and include relevant linkages between children and families to facilitate research using the conceptual data model. Third, this paper provides specific considerations for research using the conceptual data model that acknowledge the highly-localised political response to COVID-19 in the US. It concludes with a call to action for the population data science community to use and expand IDS capacities to better understand the intermediate and long-term impacts of this pandemic on young children.

Background Public agencies hold important, yet largely unused, administrative data on the families and communities they serve. Integrated Data Systems (IDS) provide the governance process, legal framework, technology, and human capacity to connect these families and communities across data siloes. By securely linking administrative data across siloes, IDS are able to support data-informed decision making. Introduction For 10+ years, AISP has helped jurisdictions through the developmental process of building IDS. We operate a network of 22 U.S. states and counties with fully-functioning Integrated Data Systems, and provide technical assistance to 18 jurisdictions at various stages of IDS development. Objectives and Approach This session presents the outcomes of an independent evaluation of our Learning Community initiative (2019) and presents a new developmental framework that outlines key dimensions of quality and readiness for IDS building and implementation. Results As of 2020, 20 sites have received formal 18-month cohort based technical assistance. This presentation will discuss site-based approaches to facilitate data sharing, including common challenges and solutions, and progress to date, including findings of an independent evaluation (2019). We will also present a framework developed based on the deep knowledge developed through technical assistance efforts, and findings from a national survey of data integration efforts conducted in 2020. The framework uses purpose, partnership structure, technical architecture, and organizational model—with respect to the strengths and challenges of each—to categorize and synthesize data integration efforts for social policy and program improvement. The developmental approach to our work emphasizes that we seek to understand methods for sustainability in diverse ways. Conclusion / Implications While there is broad agreement in the value of integrating data across domains, developing the capacity and skills necessary to link administrative data for policy evaluation and research remains an elusive goal. Initial results indicate that an individualized yet collaborative technical assistance approach is successful in developing data integration capacity.

Data integration by local and state governments is undertaken for the public good to support the interconnected needs of families and communities. And though data infrastructure is a powerful tool to support equity-oriented reforms, equity is rarely centered as a core goal for data integration. This raises fundamental concerns, as integrated data increasingly provide the raw materials for evaluation, research, and risk modeling. This session presents findings from a toolkit collaboratively generated by a workgroup convened by Actionable Intelligence for Social Policy at the University of Pennsylvania. Introduction While data sharing occurs within a legal framework, an emphasis on equity is often peripheral. Generally, institutions have not adequately examined and acknowledged structural bias in their history, or the ways in which data reflects systemic inequities in the development and administration of policies and programs. Meanwhile, the public are rarely consulted in the development and use of data systems. Objectives and Approach This toolkit was collaboratively generated by a workgroup of civic data stakeholders from across the US. Results The toolkit aims to support agencies seeking to acknowledge and compensate for the harms and bias baked into data and practice. It is organized across six stages of the administrative data life cycle—planning, data collection, data access, use of algorithms and statistical tools, analysis, and reporting and dissemination. For each stage, the toolkit includes promising and problematic practices for centering equity in administrative data reuse, with site-based examples of work in action from across the US. Conclusion / Implications The workgroup concluded that centering equity within data integration efforts is not a binary outcome, but rather a series of small steps towards more equitable practice. There are countless ways to center equity across the administrative data reuse life cycle, and this report provides concrete strategies for agencies, organizations and collaboratives to begin and grow that work in practice.

Background with rationale Data integration is undertaken for the public good, yet institutions rarely address structural bias in their history, or the ways data are biased due to systemic inequities in the administration of policies and programs. Meanwhile, the public are rarely consulted in data use. Though data infrastructure can be a powerful tool to support equity-oriented reforms, equity is rarely a stated goal for data integration. This raises fundamental concerns, as integrated data increasingly provide the raw materials for evaluation, research, and risk modeling that inform policy, practice, and resource allocation. Actionable Intelligence for Social Policy (AISP) is an initiative of the University of Pennsylvania that focuses on the development, use, and innovation of integrated data systems (IDS). We convene a network of IDS across the United States while supporting developing sites, and as such are uniquely situated to convene experts to develop guidance for centering equity within integrated data infrastructure. Main Aim This project aims to generate guidance for agencies supporting data sharing infrastructure to ensure an emphasis on equity and public engagement for ethical use. Methods/Approach A variety of data collection methods are being used, including expert panel convenings and interviews with sites piloting or exemplifying strategies for public engagement and equitable data access and use. An extensive literature review is also in progress and will inform a suite of forthcoming products, including a white paper, communications and training materials. Results The results will provide strategies for centering equity across the spectrum of data integration activities, including inclusive governance, staffing considerations, decisions about data quality, and the ethical use of data models and algorithms. Initial findings indicate there are few exemplar sites that routinely center equity within data integration efforts, yet there are promising incremental steps that sites can take to ensure ethical use. Conclusion While centering equity within data integration is an emerging focus, initial findings indicate the importance of such efforts, particularly in acknowledging and mitigating the risks of unacknowledged bias across use of administrative data for research and evaluation purposes.

Efforts to collect, manage, transform, and integrate data across administrative systems into actionable knowledge to inform better policy decisions are becoming more common. However, the technical processes, procedures, and infrastructure they employ vary substantially. Variety in approaching data infrastructure, transfer, linking, and security is expected in this emerging field, but both established and developing efforts would benefit from cohesive guidance regarding the technical considerations of data integration, with focus on presenting a range of options that can be weighted based on context specific restrictions (e.g. cost, staffing, or existing infrastructure). Actionable Intelligence for Social Policy (AISP), MetroLab Network, and the National Neighborhood Indicators Partnership (NNIP) with support from the Annie E. Casey Foundation, are convening a working group to shape and develop guidance on information architecture and technical approaches for data integration efforts such as those in the AISP and NNIP networks and the AISP Learning Community. This guidance will help newly emerging efforts as well as established ones looking to update their current approach. It will also inform policymakers and researchers who need a primer to better understand the technical components and considerations at play for data sharing and integration. This presentation will present findings, best practices and recommendations from this brief that will be released in Fall 2018.

Introduction Since 2008, our team has studied sites with strong track records of integrating cross-sector data to address complex social problems. We have identified best practices and are supporting 16 sites with individualized technical assistance and participation in a learning community to facilitate data sharing that leads to actionable intelligence. Objectives and Approach This presentation will provide an overview of our findings in studying established integrated data systems from across the United States, and a summary of our efforts in supporting the development of new integrated data systems (IDS). Specifically, we will answer: • What are the core features of integrated data systems (IDS) needed to support data linkage across government agencies? • How can sites develop these core features to facilitate data sharing, and ultimately, actionable intelligence? • What are the common roadblocks to successfully sharing data across agency siloes? • How can these roadblocks be addressed to create a sustainable data sharing ecosystem? Results Agencies collect vast amounts of data, yet struggle to assess program impacts due to data siloes. The ability to link data across agencies to develop potential policy and service improvements is difficult, and at times, impossible. This technical assistance supports a different approach. Our Integrated Data System (IDS) Learning Community initiative is an 18-month engagement for sites selected through a competitive process. As of September 2018, sites will be 11 months into the IDS development process. This presentation will discuss site-based approaches to facilitate data sharing, with an emphasis on governance, foundational agreements, data management and security, and stakeholder driven processes that ensure ethical use of data. This discussion will include progress to date, common challenges and solutions to developing an IDS. Conclusion/Implications While there is broad agreement in the value of integrating data across domains, developing the capacity and skills necessary to link administrative data for policy evaluation and research remains an elusive goal. Initial results indicate that an individualized yet collaborative technical assistance approach is successful in developing data integration capacity.