What distinguishes this from the first prototype is that the platform considers itself more integrated in the local data ecosystem. We think this is more likely if either the platform has a more local focus and has built good relationships with the people who provide the data, or if there were good data communication standards and a technological process in place to manage amendments.
This way the platform and users become part of the local data ecosystem for everyone’s benefit: the users can input their own local knowledge, the platforms benefit from more accurate information, and the open data becomes more accurate and useful for everyone.
The user experience is much the same, but in this case when the user finds an error, the platform provides an interface to enable the data to be corrected, thereby effectively crowdsourcing quality data.
This way the correction is captured at source, in the moment, and sent directly to the third party: the platform acts as a bridge between the user and data providers.
The platform can choose how to track the integration of the correction and communicate it to the user.
Prototype two walkthrough: https://t0v9qj.axshare.com/#c=2
Summary
Benefits of feeding back corrections
- Develops relationships between data providers and platforms, encouraging dialogue and creating an incentive for the data provider to keep providing quality data
- By feeding corrections back, the platform ensures that the data at the source remains usable in the long run. The alternatives are much less desirable. Simply hiding incorrect data misses the opportunity of it being corrected, and correcting only the data in the local copy held by the platform means that the two sets will diverge over time. This means that the platform cannot benefit from updates from the source, and has to maintain a parallel, but flawed, version of the dataset forever.
- Multi-way conversations lead to convergence on standards and integration, which benefits the data ecosystem as a whole and can encourage further innovation by other platforms
- Both the platform and users are involved in a transparent way, increasing understanding of data’s role in delivering a service
- Shared responsibility for data quality reduces the costs involved for any single party in keeping data up to date and relevant
Challenges to feeding back corrections
- Integrating with third parties necessitates building additional interfaces between the platform and third party data providers, and this takes additional resources
- The ability to feedback corrections requires data providers to supply feedback interfaces. This is likely to make additional work for them and may only be worthwhile if useful and accurate corrections are likely to be provided through the platforms
- Feedback may take time to be integrated into the third party data but users may expect changes they suggest to be displayed immediately. Platforms may need to amend their local copy of the data while waiting for the correction to work its way through into the original
- In some data ecosystems, there may be intermediaries between the platform and the original data source. To feedback corrections, feedback interfaces would have to be created at every step of the data value chain
