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Data Governance Models and the Environmental Context: Part 3
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August 3, 2020
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Part three of this series briefly explores some of the important design considerations for applying a collective data governance model to an environmental issue area.

Criteria for an Environmental Trend Data System

A trend analysis is a collection of data points over time, which depending on the time block and the object being monitored could be over seconds, minutes, hours, days, weeks, months or years. The value of trend analysis is to spot patterns and provide insight on how change is occurring over time. To arrive at a trend analysis we first have to imagine a data system that could provide these baseline data points. Thus a “good enough” environmental trend system could be comprised of the following qualitative and quantitative existing information streams supplied by nonprofits, community science groups, government, academia and private industry: biodiversity and species richness data, soil health, air quality, river and lake water quality, recreational use and access, agriculture, public testimony/comment and oral history.

These areas could inform a variety of community deliberative processes on: proposed new development, a judicial or legislative process that impacts how land is zoned, permit levels for a nearby industry, regional insurance rates, or even determining public access points for land and river recreation. However these trend points would need to be complimented by contextual narrative about local economics, politics and other issues taking shape. These contextual inputs could be provided in the format of a formal or informal public commentary process as articulated by law or congressional representative meetings on environmental issue areas.

Scope and Intent

An environmental trend data system should provide and amplify a way to identify emerging patterns (air quality, water quality, biodiversity richness) and then alert interested parties to these trends. While the independent verification of these trends is one intent of the system, what could make this useful to communities and elected officials is a contextual component that qualifies ideas and actions to mitigate the emerging environmental problems. For example, if pollution of a waterway is a growing problem as identified in the environmental trend data system, then offering a way for individuals within the community to exchange ideas on how they could mitigate this problem. This allows for the trend system to diagnose and prescribe simultaneously.

As discussed in the “understanding the environmental problem space” blog series, though there have been significant attempts at creating systems for data harmonization, storage and management, there are few long-term and successful examples, outside of use for educational purposes, that direct efforts towards impactfulness and usefulness of data.

Collaborative models of data governance are early on based in 1) intent for data use (for instance, is the data to be used to inform an Environmental Impact Assessment) and 2) solving for an explicit problem (community-input on water health for this Environmental Impact Assessment), but these collaborative models will need to have an additional layer of sociotechnical construction that supports the contributor/users in implementing the data for different scenarios. Though this sits outside of the construction of the governance models we’ve worked through, it is perhaps a unique need in spaces like environmental protection and management.


Here we offer principles that could guide the design of a new environmental trend data system within a data governance structure.

  • Transparency: To guarantee and nurture trust during the decision-making process and grow an informed civic body, transparency must be built into every part of an environmental trend data system.
  • Privacy and control: Because environmental problems are usually tied to a specific geographic region and identify the impacts of these problems down to individual households and/or identify where rare species can be found, there must be significant consideration for how privacy and control of data dissemination is handled within the environmental trend system. Conducting both an early and an ongoing assessment of the potential benefits and risks involved can help to solve for issues that might arise.
  • Independence: To ensure fair representation of all invested members, the structure should be independent/disinterested (not belonging to one entity) as designated by the governing structure.
  • Governance structure: Depending on the style of governance, an active member from each interested group should have a contractual duty to steward the independent entity. If there is not an association with a formal organization or entity, governance should weigh how individual input is managed to ensure equitable representation. Governance should additionally early on define guidelines for group management, decision making, conflict management, the roles and responsibilities of members and outline how members might be governed under different rules and laws (such as tribal water rights in Colorado).
  • Problem definition: Collaborative models of data governance are based on binding the group in solving an existing problem that has been articulated prior to the development of pooled resources. However, sometimes the environmental problem, especially at the start of an emerging issue, can be difficult to define thoroughly enough.
  • Impact: Any collaborative solution to improving upon existing environmental data models should be focused on how that data can be impactful and usable when environmental decisions are being made. Otherwise the ability for data to serve as a mediator in facilitating decisions that work for and on behalf of people providing the data, will be limited. Additionally, involvement outside of the core governance structure should happen early on with people who will be responsible for implementing data at a further point.


Currently technical platforms that emphasize community or local level environmental input and consideration are usually funded in part by private foundations with some local community support. These funding streams are inadequate and often these platforms die with the organization or initiative funding them. A core component to address is ways in which this environmental data model could be integrated, amplify or augment civic-funded initiatives like a public commentary system. Alternatively, approaches could be made to identify how market driven mechanisms for cooperative data models, like in agriculture or renewables, could dedicate a portion of their revenue stream to public environmental trend data models.

Risks and Challenges

In several of the case studies, there were clear risk components identified. For instance, though flood protection in climate sensitive municipalities such as New Orleans are important, the benefits of collaborative action have to be weighed against the potential for markets and thus individuals to be impacted both in terms of economic valuation of homes and cost of insurance. In the case of water rights in Colorado, though generally agreed that prior appropriation does not address the current realities of the climate crisis in the Colorado River basin, redistributing water rights that have been long held would have to be done with intentionality for how this might impact the many people in the region. In the case of urban air quality monitoring, being cautious of managing potential economic conflict based on air quality readings would have to be at the center of data governance design.

As we’ve previously written about in our “understanding the environmental problem space” blog series there is also a social element that provides a challenge to integrating collective governance structures. As identified in the case studies, most industries, regulators, assessors, etc. are operating in their definable community of practice in which approaches tend not to transcend or incorporate other sectors. To create collaborative structures in the environmental space, there will need to be a carefully curated epistemic cultural approach that can build on a diversity of methodologies and pathways to knowledge production in pursuit of creating shared practices within the collaborative structure. Creating an environmental data governance model that encourages collaborative agreement requires bridging a complexity of different cultures of how problems are approached. This can include: government officials and administrative bureaucracy, community implemented organizing, private technology companies and the profit bottom line, private landowners and individual rights, public lands and their history in military management.

Sometimes the environmental challenges identified as candidates for alternative data governance approaches require more social and cultural adjustment rather than just the creation of the technical system. Because of this, a risk in creating a data collaborative structure for an existing environmental problem, say cleaning up a historically polluted waterway, is that the new data collaborative structure simply creates a technical band-aid. As mentioned in our problems blog post, sometimes the most difficult troubleshooting must occur at the sociocultural level within public agencies that are required by law to protect the polluted waterways.