Capability: Political integrity interoperability¶
To what extent is political integrity data interoperable across different political integrity datasets, as well as other datasets associated with relevant information flows?
Definitions and Identification
This indicator looks at the interoperability of key political integrity datasets, both at the basic level of detailed documentation and consistency of formats and standards, as well as regarding the correspondence across datasets of data fields and identifiers. (An identifier is simply “the name for an object or concept in a database” (Open Data Handbook; for examples, see entry; available in multiple languages).) The lack of interoperability across these datasets has been a longstanding issue for researchers, journalists, and civil society organizations.
The focus here is not on matching a universal standard—political integrity data doesn't currently have relevant data standards, although Transparency International has been working on their development—but on governments increasing the usefulness of this data through thoughtful coordination.
This indicator thus calls for a meta-analysis of the four political integrity datasets already identified (on political finance, interest and asset disclosure, lobbying, and RTI performance), plus a meta-analysis across the relevant datasets of the Barometer's company information, public finance, and public procurement clusters.
Examples
- In Estonia, most datasets and registries are interoperable through the country's digital infrastructure, supported by X-Road, a distributed data exchange layer. Individuals are linked via the “isikukood” (personal identity code), while businesses are connected through unique identifiers from the business registry. Services like www.teatmik.ee leverage this interconnected data for enhanced functionality.
This indicator asks primarily for a meta-analysis of datasets you have already identified and assessed, so we expect it to require minimal additional work with regard to searches or consultation.
Start from the data already located for political finance, interest and asset declarations, lobbying, and RTI performance. You're looking to determine whether these key datasets share common identifiers that facilitate mapping flows across the larger data ecosystem. You may want to look first for evidence of a system in use to ensure and validate the interoperability of these specific datasets; if found, spot check across several datasets to understand its application in practice. If you can find no evidence of a system for validating interoperability, assess the fields and metadata definitions of the datasets themselves to identify correspondences and differences; spot check across datasets to determine how consistent any correspondences are in practice.
After comparing the use of common identifiers across the key political integrity datasets, then compare them across the relevant datasets of company information, public finance, and public procurement.
Starting points
What to look for?
Look for evidence that can answer the following questions:
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Are the key political integrity datasets interoperable at a basic level? For example, are they accompanied by detailed documentation and metadata? Have they consistently been published in the same structured format? Do they share data vocabularies and standards?
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Do the political integrity datasets share common identifiers that facilitate mapping flows across the data ecosystem?
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Do the relevant political integrity datasets share common identifiers for public officials?
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Do lobbying data and political finance data share common identifiers for lobbyist clients and party and campaign donors?
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Do asset declarations and political finance disclosures share common identifiers for interests, assets, and liabilities?
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Do the various datasets share common identifiers for legal persons (companies, nonprofits, and other legal entities) associated with donations, interests, assets, liabilities, and lobbying activities?
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Do the political integrity datasets and relevant company information datasets share common identifiers?
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Do the political integrity datasets and relevant land datasets share common identifiers?
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Do the political integrity datasets and relevant public finance datasets share common identifiers?
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Do the political integrity datasets and relevant public procurement datasets share common identifiers?
Show/hide supporting questions
Existence
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Is there evidence that the datasets associated with political integrity are interoperable? For example, evidence may include detailed data documentation and metadata; consistency in structured publication formats; use of common identifiers, data vocabularies, and standards; etc.
- There is no evidence of interoperability across the political integrity datasets.
- There is isolated evidence of interoperability across the political integrity datasets.
Supporting questions: Please explain your response.
- There is some evidence of interoperability across the political integrity datasets.
Supporting questions: Please explain your response.
- There is widespread evidence of interoperability across the political integrity datasets and other relevant datasets, supported by a long-term interoperability strategy.
Supporting questions: Please explain your response.Please provide a URL(s) where this strategy can be found.
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Extent of existence:
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To what extent do the datasets associated with political integrity use consistent identifiers and identification systems for elements that appear in more than one dataset? (There is little to no consistency in identifiers or identification systems, with only minimal alignment (e.g., just one category of identifiers is consistent across only two datasets)., There is partial consistency; several identifier categories are consistent across multiple datasets or entire systems are consistent across at least two datasets, but some categories that occur across multiple datasets lack consistent identifiers., There is strong consistency: all or nearly all categories that occur across multiple datasets use consistent identifiers and identification systems.)
Supporting questions (conditional)
If There is little to no consistency in identifiers or identification systems, with only minimal alignment (e.g., just one category of identifiers is consistent across only two datasets). or There is partial consistency; several identifier categories are consistent across multiple datasets or entire systems are consistent across at least two datasets, but some categories that occur across multiple datasets lack consistent identifiers. or There is strong consistency: all or nearly all categories that occur across multiple datasets use consistent identifiers and identification systems.: Please briefly explain what is consistent and what is not.
If There is partial consistency; several identifier categories are consistent across multiple datasets or entire systems are consistent across at least two datasets, but some categories that occur across multiple datasets lack consistent identifiers. or There is strong consistency: all or nearly all categories that occur across multiple datasets use consistent identifiers and identification systems.: Please provide supporting URL(s) for shared identifier documentation or other evidence of shared identifiers.
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Existence summary:
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Please summarize your answers to the preceding existence sub-questions, including the extent of existence. [Open Text] Drawing on the research you have conducted and the evidence you have gathered for this section, describe what you have found (or not found) when answering the existence sub-questions for this indicator.
Supporting questions
Please provide the URL(s) for the evidence that supports the summary provided.
Elements
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Interoperability across political integrity datasets:
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The different political integrity datasets use common identifiers for public officials. (No, Partially, Yes)
Supporting questions (conditional)
If Partially or Yes: Please explain your response.
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Lobbying data and political finance data share common identifiers for lobbyists’ clients and political finance donors. (No, Partially, Yes)
Supporting questions (conditional)
If Partially or Yes: Please explain your response.
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Interest and asset declarations and political finance disclosures share common identifiers for interests, assets, and liabilities. (No, Partially, Yes)
Supporting questions (conditional)
If Partially or Yes: Please explain your response.
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The various datasets share common identifiers for legal persons associated with donations, interests, assets, liabilities, and lobbying activities. (No, Partially, Yes) The category of legal persons includes companies, corporations, nonprofits, and similar entities that the law recognizes as being able to undertake actions such as entering into contracts, suing (or being sued), or owning property.
Supporting questions (conditional)
If Partially or Yes: Please explain your response.
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Interoperability across other relevant datasets:
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The key datasets for the political integrity and company information clusters share common identifiers that facilitate mapping flows across the data ecosystem. (No, Partially, Yes)
Supporting questions (conditional)
If Partially or Yes: Please explain your response.
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The key datasets for the political integrity and public finance clusters share common identifiers that facilitate mapping flows across the data ecosystem. (No, Partially, Yes)
Supporting questions (conditional)
If Partially or Yes: Please explain your response.
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The key datasets for the political integrity and public procurement clusters share common identifiers that facilitate mapping flows across the data ecosystem. (No, Partially, Yes)
Supporting questions (conditional)
If Partially or Yes: Please explain your response.
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Elements summary:
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Please summarize your answers to the preceding element sub-questions. [Open Text] Drawing on the research you have conducted and the evidence you have gathered for this section, describe what you have found (or not found) when answering the element sub-questions for this indicator.
Supporting questions
Please provide the URL(s) for the evidence that supports the summary provided.
SDG 16 calls for governments around the world to "promote peaceful and inclusive societies for sustainable development; provide access to justice for all; and build effective, accountable, and inclusive institutions at all levels," with targets 16.3, 16.4, 16.5, 16.6, 16.7, and 16.10 focusing on specific matters of integrity and accountability. Similarly, the United Nations Convention Against Corruption (UNCAC) commits countries to combat corruption in both the public and private sectors.
Corruption often doesn't involve only a single act, type of act, or actor, but rather entails networks and flows. Data can be a critical tool in tracking illicit financial flows and otherwise fighting corruption, but when the relevant data types aren't interoperable, it may offer only a fragmentary picture. However, making such data interoperable—for example, using the same unique identifiers across different types of datasets—makes it increasingly useful.
This indicator thus investigates the interoperability of data across different political integrity datasets, as well as across other datasets associated with relevant information flows.