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 to what degree the different data fields and identifiers correspond across political integrity datasets as well as other datasets associated with relevant information flows. 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—this is a thematic area that doesn't currently have relevant data standards, although Transparency International has been working to develop some—but on governments increasing the usefulness of this data through thoughtful coordination.
This indicator thus calls for a meta-analysis of the five political integrity datasets already identified, plus a meta-analysis across the relevant datasets of the Barometer's company information, land, public finance, and public procurement modules.
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, public consultation, 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 assure 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, land, public finance, and public procurement.
Starting points
What to look for?
Look for evidence that can answer the following questions:
- Do the political integrity datasets share common identifiers that facilitate mapping flows across the data ecosystem?
- Do the relevant political integrity datasets share common identifiers for public officials?
- Do lobbying data and political finance data share common identifiers for lobbyist clients and party and campaign donors?
- Do lobbying registers and public consultation data share common identifiers for regulations?
- Do asset declarations and political finance disclosures share common identifiers for interests, assets, and liabilities?
- 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?
- Do the political integrity datasets and relevant company information datasets share common identifiers?
- Do the political integrity datasets and relevant land datasets share common identifiers?
- Do the political integrity datasets and relevant public finance datasets share common identifiers?
- Do the political integrity datasets and relevant public procurement datasets share common identifiers?
National and sub-national considerations
This question investigates the interoperability of datasets that operate within the same level of government, although best practice involves not only interoperability across the same level of government but across national and sub-national levels.
In some countries, political integrity data may be generated and published at the sub-national level, carried out by individual states, regions, or cities. To assess countries where political integrity data is organized sub-nationally, researchers should select the strongest example of sub-national practices across the different dimensions of political integrity data, assess these datasets for interoperability, and then explain in the indicator's justification box whether this sub-national example is an outlier or an example of widespread practice.
Show/hide supporting questions
Existence
- There is evidence that datasets share common identifiers.
- The datasets do not share common identifiers.
- The datasets use a limited number of common identifiers.
- The datasets of this theme consistently use common identifiers.
- The datasets of this theme consistently use common identifiers and share common identifiers with relevant datasets in other themes.
Elements
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Interoperability across political integrity datasets:
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The key datasets for this theme share common identifiers that facilitate mapping flows across the data ecosystem. (No, Partially, Yes)
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The different political integrity datasets use common identifiers for public officials. (No, Partially, Yes)
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Lobbying data and political finance data share common identifiers for lobbyist clients and party and campaign donors. (No, Partially, Yes)
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Lobbying registers and public consultation data use common identifiers for regulations. (No, Partially, Yes)
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Asset declarations and political finance disclosures share common identifiers for interests, assets, and liabilities. (No, Partially, Yes)
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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.
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Interoperability across other relevant datasets:
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The key datasets for the political integrity and company information modules share common identifiers that facilitate mapping flows across the data ecosystem. (No, Partially, Yes)
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The key datasets for the political integrity and land modules share common identifiers that facilitate mapping flows across the data ecosystem. (No, Partially, Yes)
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The key datasets for the political integrity and public finance modules share common identifiers that facilitate mapping flows across the data ecosystem. (No, Partially, Yes)
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The key datasets for the political integrity and public procurement modules share common identifiers that facilitate mapping flows across the data ecosystem. (No, Partially, Yes)
Extent
- To what degree do the datasets associated with this theme use consistent identifiers and identification systems for elements that appear in more than one dataset?
- There is no consistency of identifiers or identification systems.
- There is minimal consistency; at least one category of identifiers is consistent across two datasets.
Supporting questions: Please briefly explain what is consistent and what is not.
- There is partial consistency; several categories of identifiers are consistent across multiple datasets or whole identification systems are consistent across at least two datasets.
Supporting questions: Please briefly explain what is consistent and what is not.
- There is strong consistency; all of almost all of the element categories that appear in more than one dataset use consistent identifiers and identification systems.
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 datasets associated with relevant information flows.