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Making Data Flow

June 28, 2019 |

By John Finisdore of Sustainable Flows and Matthew Ling of the UN Environment World Conservation Monitoring Centre (UNEP-WCMC)

“A universal complaint in the natural capital community is data; access to it, cleaning it, sharing it, and making it useful for analysis that was due yesterday. Some in the community see this bottleneck as intractable, others point to data analytics as a complete solution. Experience from professionals in health, economics, chemistry and other fields, shows that targeted interventions to address these bottlenecks allow data managers to be truly effective. If implemented by the community, these interventions would make analysis cheaper, quicker, and more accurate.

To better understand the data-related issues facing the community, UNEP-WCMC, on behalf of the Natural Capital Coalition, delivered a report in February as part of the Data Information Flow project. Through engagement with the community—including the private sector—this project unpicked some of the bottlenecks. Four elements were identified that influence the availability of robust data: accessibility, infrastructure, quality, and capacity. Barriers to any one of these elements will prevent the efficient and effective flow of data throughout the community, from data producers through to end users.

To overcome these bottlenecks, the Data use in natural capital assessments report identified and assessed the key barriers and put forward a range of potential solutions. These solutions include developing:

1) References lists and directories of key searchable datasets, to reduce the time spent discovering data

2) Guidance and resources to help with data access, address data gaps and related management challenges, reduce time and cost sinks, and improve data quality

3) Improved data measurement methods, so that data are more accurate and interoperable

4) Developing an ontology with standard terms and definitions, providing a foundation for data interoperability

The idea of an ontology is not new. The community has been developing and iterating lists, groupings, categories, and ontologies since at least 1997. Most of these efforts have fallen short of employing best practices in classification systems (CS). Specifically, having a formal nested hierarchy that can accommodate all ecosystem services. Hierarchies must not overlap or have duplication within them and must have logical consistency throughout. Other fields realized this need and adopted classification systems, such as the health field’s International Statistical Classification of Diseases and Related Health Problems, the International Standard Industrial Classification System used in economics, and the Linnaean classification system use in wildlife biology.

These classifications systems drove the unified use of terms, definitions, measurement methods, and data markers. Professionals in these other fields can easily define variables and tag data, facilitating data discovery by peers and reducing the need for institutions to recreate classifications. If the community adopted classification systems with nested hierarchies, it would gain 18 benefits (Table 1) according to a new Sustainable Flows working paper (e.g. improved collaboration, and facilitated data integration).

The Common International Classification of Ecosystem Services (CICES) and the National Ecosystem Services Classification System Plus (NESCS Plus) are the only ecosystem services classification systems (ES-CS) that embody best practices. They are being used in full, but also in part, by employing the components with which these hierarchies were built. The components include final ecosystem services thinking and having “use” and “users” of the ES in the definition of the ES itself. Amidst this implementation, the United Nations Statistical Division System of Environmental-Economic Accounting is developing “Logic Chains” that aim to define several ES using consistent terms including the components that make-up the hierarchy. And UNEP-WCMC have led the development of an asset classification, a component of a full ES-CS. The more the community applies this ‘classification knowledge’, the more data-flow bottlenecks are likely to be overcome, and the more likely the 18 benefits (Table 1) will be realized.

However, there are no clear pathways for incorporating this classification knowledge into corporations, government agencies, research organizations, and NGOs. Even if full adoption of an ES-CS is an organization’s goal, it may be difficult to undertake all at once; what intermediate steps could be taken? Are some of these steps appropriate for organizations that have not committed to full adoption? Are there changes to databases that are required for a transition? The Natural Capital Coalition, UNEP-WCMC and Sustainable Flows are exploring options to contribute to this work and ensuring that the needs of the public and private sectors are met. Done successfully, having common terms, definitions, metrics, and analytic techniques will improve data interoperability, reduce costs and time for data management, and improve accuracy of natural capital assessments.”

Please contact John Finisdore or Matthew Ling with feedback.

Table 1: The 18 Benefits of using ecosystem services classification systems (ES CS) in relation to the solutions presented by the Data use in natural capital assessments report.

The ’Data use in natural capital assessments’ report calls for the following improvements to data:

The callout boxes detail how ecosystem services classification systems overlap with the potential solutions presented in the report.

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