Research the landscape

Research into the data landscape, and especially the issues that are trying to be solved, is the foundation of implementing Smart Data.

Research is the first step when exploring how to implement functionality or enable innovation in a Smart Data ecosystem in a new sector. Much research has been done in this space to date, both at a broader cross-sectoral level, primarily by BEIS, and within individual Schemes, especially Open Banking. Nesta has also produced a significant amount of wider 'innovation' research, and such resources should be explored first.

Conducting additional research will often be necessary, especially in Smart Data Schemes with nascent data ecosystems or in new sectors. The double diamond methodology described below provides a useful design pattern for this type of exploratory research.

We envision different types of research activities to be led primarily, but not exclusively, by different stakeholder groups:

  • Government agencies and regulators should research the socio-economic impacts of policy (on competition, well-being, and the environment) inclusive of threats and risks of greater data portability

  • Businesses should invest in research regarding business models to deliver these services, partnering with relevant third sector organisations for exploring services, in particular those with benefits directed towards the environment or customers in vulnerable circumstances

  • Industry bodies and trade associations should fund research to understand the readiness, opportunities, risks and other impacts of data portability in their sector

Tap into existing research on smart data

Research by BEIS

Research about Open Banking

  • Open Banking is the most mature of the current Smart Data schemes, and therefore provides the most lessons learned from an implemented initiative. Open Banking, Preparing for Lift Off provides the first retrospective on the initiative and provides insights to other Smart Data schemes.

  • The Open Banking Consumer Manifesto and subsequent report, Consumer Priorities for Open Banking, provide a clear example of necessary research in the Smart Data space that keeps the focus on benefits to the end consumers. This will need to be emulated in additional sectors.

  • The Open Banking Annual Report 2020 provides the most up to date figures on this scheme, providing insights into everything from balance sheets to technical KPIs to market propositions.

Other research

Nesta compendium for innovation methods

Nesta works to uncover, develop and test new ways of supporting innovation from across sectors and around the world, and has been involved in Smart Data innovation since the launch of Open Banking. These techniques, tools and processes are collectively known as 'innovation methods'.

The Nesta compendium for innovation methods summarises Nesta's findings. Nesta has both studied and invested in some innovation methods, like seed accelerator programmes. For others, like challenge prizes, standards of evidence or public sector labs, Nesta has developed and helped globalise. Some of these innovation methods align with the innovation mechanisms we have researched over the course of the project, such as:

  • Accelerator programmes. Accelerators provide intensive and time-limited business support for cohorts of startups, aiming to get them ready for investment more quickly than traditional incubators.

  • Anticipatory regulation. Traditional ways of regulating are struggling to cope with the pace of change in technology. Anticipatory regulation is an emerging approach that is proactive, iterative and responds to evolving markets.

  • Challenge prizes. Competitive events that offer a reward to whoever can first or most effectively meet a defined challenge. Through a public competition, challenge prizes aim to tap into and engage the broadest possible community of innovators in the solving of a specific problem or challenge.

Other innovation methods from the Nesta Compendium may well be useful for accelerating innovation in the Smart Data space, however they have not been explored in-depth for this cross-sector innovation model research:

  • Crowdfunding

  • Experimentation

  • Futures

  • Impact investment

  • Innovation mapping

  • People Powered Results: the 100 day challenge

  • Prototyping

  • Public and social innovation labs

  • Scaling grants for social innovations

  • Standards of Evidence

Conduct new research to build off the existing

The Smart Data ecosystem is constantly evolving. This means there will always be room for new research to help improve the development and implementation of individual Smart Data Schemes, and the wider cross-sectoral components more broadly.

The main findings for new research have to expand beyond the competition, to research the needs of people and planet for new data-driven services, including for underserved groups and for climate action, to look at potential barriers towards implementation of the Smart Data initiative and to ensure these needs are addressed during development phases.

Double-Diamond Research Methodology

Double Diamond is the name of a design process model popularised by the British Design Council in 2005, and adapted from the divergence-convergence model:

  • Discover: Understand the issue rather than merely assuming it. This involves speaking to and spending time with people who are affected by the issues.

  • Define: The insight gathered from the discovery phase can help to define the challenge in a different way.

  • Develop: Give different answers to the clearly defined problem, seeking inspiration from elsewhere and co-designing with a range of different people.

  • Deliver: Involves testing out different solutions at small-scale, rejecting those that will not work and improving the ones that will.

The ODI Data Ecosystem Mapping tool can help an organisation to understand how data creates value. It identifies the data, data stewards and data users; the different roles they play; and the relationships between them.

You can use your map as a practical tool to plan and visualise a data ecosystem, or show opportunities for increasing value to particular parts of a data ecosystem.

A data ecosystem map can be used to:

  • Collaborate directly with other stakeholders for organisational/ecosystem change

  • Explore new sources of data to improve internal operations

  • Exploit existing data flows to drive new services or improve existing services

  • Inform a project to build a data-enabled service

  • Identify where changes are needed, and what effects they might have

It is part of the wider Data Landscape Playbook on which this guidebook is partially based

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