Evolving the ecosystem
In the long run, the Smart Data ecosystem will require a robust framework for Monitoring, Evaluating and Learning in order to consistently deliver value, and should expand in new sectors and regions.
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In the long run, the Smart Data ecosystem will require a robust framework for Monitoring, Evaluating and Learning in order to consistently deliver value, and should expand in new sectors and regions.
Last updated
Smart Data is an initiative driven by government and regulators, therefore measuring the success and monitoring the risks will ultimately be the responsibility of those bodies. Collaboration will still be required by industry to ensure that the indicators and risks put forward by the public sector are fit for purpose. Industry participation will also be required in encouraging expansion to new sectors, sub-sectors and countries.
Measure the success of the initiative and where necessary make further interventions to help deliver on targets. In particular, consider who is benefiting and whether benefits are being spread equitably. This may also involve reviewing and extending standards and specifications. One way to do this planning in a structured and understandable way is by using ‘logic models’.
Logic models are tools designed to help you plan impactful projects and communicate those plans to others. They provide a structured way of thinking about how to build a programme that will help to address a specific problem or challenge.
A logic model is made up of four parts: inputs, outputs (activities and participation), outcomes, and impact.
What we invest – the resources that are necessary for an initiative to carry out its planned work, such as funding, access to existing datasets or use of specific platforms and technologies.
It is also helpful to record the situation in the logic model as well, which is the originating problem, or issue, set within a system of socio-political, environmental and economic circumstances that you are seeking to address by building or improving data infrastructure. The situation is the beginning point of logic model development.
There are many activities common to data initiatives. This toolkit provides three real-world examples of logic models for successful data access initiatives, which you can use as inspiration when filling out your logic model.
Adopting an open standard – OpenActive wants to adopt a new standard for data about sport and exercise classes in the UK, in order to increase activity with the ultimate aim of improving wellbeing.
Launching a challenge prize – the snakebite project aimed to reduce the mortality and morbidity of snakebites in India through a challenge prize that rewarded innovative solutions.
Building a data publishing initiative – the Offshore Renewable Energy Catapult wants to improve sector efficiency by stewarding operational data to support research, projects and product development through the Platform for Operational Data.
Build a monitoring framework for assessing whether risks – such as privacy infringements, discrimination or fraud – have manifested either in the in-scope sectors or other sectors that use the data, with interventions as appropriate.
Within this framework it is important to periodically assess progress against the objectives set out in the logic model, and to look for any unintended impacts. Tools like the ODI Data Ethics Canvas and Consequence Scanning can help mitigate harms from unintended consequences.
Continue to explore adding new sectors and sub-sectors into scope, such as other utilities, transportation, healthcare, retail and social media. Conduct research on which new sectors will best connect with existing schemes.
Grow internationally where possible, connecting potential data portability initiatives in relatively mature markets - those that have data portability and protection laws, consent mechanisms, open banking initiatives and open banking readiness - such as:
