SAAN KIM

About Data collaboratives/collaboration

Increasingly wicked and grand challenges are invading people’s lives. Crisis may come at any time, requiring real-time response and long-term decision making. We need to make use of data to create collective value, we need data to improve our learning outcomes and assist us in better understanding what is going on. The data, however, could be so giant and diverse across multiple sectors involving multiple actors. This highlights the needs for forming some data partnerships to address some large and complex problems.

A term, “data collaboratives,” was raised (mainly by the govlab) describing a form of collaboration between various participants from different sectors in which data are shared and exchanged for public good. Several synonyms exist, data collaborative, data partnership, open data partnership, data donation, data philanthropy, big data collaboration, PPP, big data for statistics. 【for a detailed explanation, could refer to the reports by the Gov Lab and a series of publications by a scholar named Susha】In one 2019 paper published by Susha et al., the authors argued that those terms somehow share socio-technical nature and proposed a new concept, “data driven social partnership”:

“Data driven social partnership is a collaboration between actors in one or more sectors to leverage data from different parties, at any stage of its lifecycle, for public benefit in policy or science.“

“leveraging data” here refers to a combination of “data sharing and access” and “exchanging data for resources.” Besides, the definitions of these terms could be generalized with some core elements: actors, activities, object of exchange, purpose, infrastructure, and conditions. 【When i was screening the related article, it seems that the purpose of such partnerships or collaborations are usually bonded with social innovation driven, or public good, or sth. Given that data collaboratives sounds like voluntary data sharing, an approach balancing the mandated or voluntary partnership should be sought from my personal view. 】

Cross-sector partnerships/collaboration

The whole data collaboratives theme might build on data sharing and collaboration. We might need to know sth about cross-sector partnerships. The idea of “collaborative advantage” frequently lies in the practice of the related intervention strategy. A key theme in such partnership is about producing some beneficiaries, achieving some goals. Institutional spheres ususally are: state (public), market (firms, private), and civil society

Terms often brought up also include complexity and systems theory, multi-stakeholder decision-making processes, systematic change, transformative change, institutional change grounded in the institutional theory, theories of change, resource dependence model, social issue model, societal sector model., collaborative governance theory, information sharing.

Two rationales for interorganizational relations: resource-dependence model (attract resources to solve an existing organizational problem/needs ) + system change model (to address a shared concern or opportunity)

Partnership model: In one paper discussing how “different interests, goals, and contextal factors come into play and shape different organizations define the terms of engagement in a partnership on a societal issue,” the authors mentioned a framework that could be used to classify dimensions of platforms for cross-sector social partnership that managers could use it to identify where they are. Reference paper are the paper by J.W. Selsky, B. Parker,2010, … : depending on the primary interest (voluntary, self-interest, mixed, mandated), contextual factors (pressure), source of problem definition, orientation (transactional, integrative), dependencies, time frame, conceptualization of sectors, prospective sensemaking themes, the partnership model could be divided into resource-dependence, social issue, and societal actor.

Data sharing

Some elements in data collaborative governance: organizational; political and policy; data and technical.

Driving force/factors (夹杂着总的factor): interests, motivations, expectations

contextual factors—–legislation, political pressure (to share data), organizational readiness, technical artefacts

Factors influencing inter-organizational information sharing in the public sector in the paper by Yang and Maxwell, 2011: 3.

Susha et al. 2023: government vision and leadership, organizational gains, pressure and urgency

Barriers and challenges:

Susha et al. 2019: 1) regulatory (legal provisions, data sharing policies, ethical guidelines, informed consent of data subjects); 2) organizational (incentives, value proposition, coordination of roles, resources, and activities, resource constraints, data accessibility and discovery, organizational norms, cultures, practices, terminologies, data stewardship, trust, etc.); and 3) data-related ( privacy, data bias, data quality, data security, flawed data analysis, data misuse, data maching, lack of tools methods expertise, legitimacy of new sources of data, consistency of data and resources, data archival, control over data); 4) Societal (measure impact and value, data ownership, digital divide, power shift, uneven distribution of data capacities, public perception, etc. )

Susha et al. 2023: Challenges of data sharing vary with the partnership model. data confidentiality, costs/investments, finding the right partner, tight timeline and lack of prior agreements, cost-benefits in the long term, data interoperability, lack of data sharing skills, defining roles of intermediaries 【actually I found it quite interesting, 在一些区域会存在一些第三方中介 但由于种种 users会信任他们吗 会有驱动力分享数据吗 难】, sustaining data sharing efforts.