In this report we explored, through practical prototyping, how peer-to-peer accommodation platforms could use, integrate with and curate available open data sources to enhance the experience they offer their users.
While we focussed on the peer-to-peer accommodation sector in this instance, we would like to point out that the same challenges and benefits would apply to any service wanting to integrate with open data, regardless of sector.
The prototypes illustrate how this integration can be achieved, at least technically, in a very short amount of time. It also helps us illustrate some of the challenges such an integration would create.
On the one hand, useful local data exists and much is available as open data for anyone to access, use and share.
But there are costs. Using open data requires an investment of developer time to deal with the incoming third party data, particularly when working to integrate a lot of different third party sources that have not been built according to the same standards and use different APIs.
Additional work is needed to integrate feedback loops between data providers and platform users. They may not need to collect or curate the data themselves, but integrating other datasets could divert platforms from their core functionality and require them to become knowledgeable in domains they might not be comfortable with or benefit from right away.
However, for some platforms, using data to invest in more targeted business models could give them the edge over bigger platforms. It could help them find and cater to niche users and create a loyal audience that can rely on their richer offer. Curation of information and delivering to targeted audiences could be a way to overcome competition in the long term and change the peer-to-peer landscape over time.
If done well, displaying and curating data may alleviate some fears we’ve heard concerning peer-to-peer platforms not taking into account local communities or creating a bubble of tourism in certain areas. While our prototypes focused on sports and physical activity data, interacting with other local council data (regarding bins, crime statistics and noise statistics for example) and local area data (local restaurants, artisans, small shops, neighborhood activities, etc.) could help with those issues.
Drawing data from collaborative platforms like Open Street Maps could give local businesses the opportunity to ensure they have more control over the data they show, and that that information is up to date. Local councils and business information districts could offer support to do this, while other services provide mechanisms for community groups to provide data, such as the accessible locations data provided and crowdsourced through Wheelmap.
However, a greater variety of data sources brings challenges which place a heavy burden on platforms that want to make use of this data, such as:
- handling multiple data formats and API approaches
- integrating data that uses different identifiers
- dealing with differences in responses to corrections and updates
Our prototypes used only open data and therefore did not have to contend with the additional compliance challenges that would arise if different datasets were made available under different terms and conditions. The technical challenges are hard enough.
Intermediaries can thrive in such a complex environment, offering pre-cleansed, standardised, integrated and reliable data, and adding sufficient value to data that is provided for free at source that services further up the data value chain are prepared to pay for it.
From this prototyping exercise, we can highlight a number of recommendations for data publishers, user-facing services and intermediaries:
- Make it easy for people who use your data to get in touch with you and talk about their plans – this will help you ensure the data is being provided in a useful way and give you good examples of the benefits of the investment you have made
- Use open data certificates to indicate that data is being published openly and in a way which follows best practice guidance
- Use common open standards for data and identifiers to make it easier for data users to use the data you provide
- Include clear licences and provide clear guidance on how to attribute data that you provide, so that people who view it on other platforms can understand its provenance
- Provide both data dumps to initialise local databases and form the basis of analyses, and feeds of updates and corrections to enable data users to keep local copies up to date
- Building and maintaining robust, searchable APIs comes with a cost. Consider business models that offer differing levels of support and availability. You may for instance provide an API for exploration only, and/or a fully supported API with expectations of reliability as a commercial service
- Provide APIs that enable tools to provide corrections to the data you make available, and provide clear indications of the process involved in these corrections showing up in that data
- There are many open data sources that could be integrated into your service – talking to the providers of that data may help you assess its suitability and create a mutually beneficial relationship
- Provide attribution back to the data you integrate into your services. Design interfaces in collaboration with users that help them understand who is responsible for the accuracy of the data you display
- Provide mechanisms to enable people to provide corrections to data. Design interfaces in collaboration with users that help to set their expectations about how these corrections are dealt with and when they might surface.
- Ensure that it’s possible for the users of your service to trace the provenance of the data you provide back to its original source, so that they can indicate it to their users
- Help drive improvements to the upstream dataset by directing people to contribute and correct the data at source. If this is not possible, provide a mechanism to correct a local version of the dataset and engage with upstream publishers to clarify feedback mechanisms and make them automated if possible
- Directly engage with editing and correcting collaborative datasets. For example, Transport for London are helping improve OpenStreetMap by contributing detailed layouts of tube stations
This prototyping exercise allowed us to think about a number of questions, for which the answers will often depend on the goals, business models and specificities of platforms aiming to integrate local open data in their products. We hope that it helped highlight the existence of a number of quality open datasets waiting to be used, and pointed those aiming use them in the right direction.