Assess the needs

Assess policies, capabilities and other data infrastructure that can properly support the innovation programme.

Assessing the data, commercial and policy landscapes of a sector or industry cluster is complex, and may not yield incontrovertible results, but it is a valuable set of activities for exploring the readiness and potential impact of Smart Data interventions. Though there are more aspects to consider, assessing policies, data infrastructures (including commercials models), data skills and literacies can help guide innovation from Smart Data.

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

  • Government agencies and regulators draft the policy and are therefore most accountable for its responsible implementation. Policy consultations are a typical way to do this, and if shared widely with ample time for small organisations to answer, are inclusive approaches that can underpin more quantitative measures.

  • Businesses, alongside industry bodies and trade associations, should work together to understand the data skill and literacy level across their sector or industry. This can be done with assessments led by trade organisations or independent consultancies to provide an indicative baseline from which Smart Data Schemes can help galvanise investment.

  • Regulators, industry bodies and trade associations are well poised to focus on exploring open or public data infrastructure, such as sector and cross-sector sandboxes that can help businesses of various sizes test out Smart Data-driven business models.

Policy to support Smart Data innovation

Data portability is often discussed as a way to increase innovation and competition in the economy. As mentioned in the section on policies and guidance, without policy interventions, it is not possible to implement Smart Data. Where possible, it can be helpful to test policy changes against a model in order to bolster a well-researched evidence base.

Consider the use of an agent-based model (ABM) of data sharing in a simple economy, to better understand how policy changes for greater data openness might affect innovation. Although the ABM is too simple to base policy decisions on alone, it is still useful to think through policy issues, and quantitatively bolstered policy consultations refine the assumptions about interventions in the real world.

The ODI expects data portability interventions for data sharing to increase innovation, by encouraging the development of similar products and services, as well as radically new products and services. The use of ABM in a simple economy scenario has indicated this would also be the case.

Besides data portability policy, innovation in Smart Data will also require supporting the development of legal frameworks, guidance, codes of practice and training that will help support ethical, legal and trustworthy sharing of data across a range of sectors, to achieve the agreed objectives and impact targets.

Data skills and literacies for Smart Data innovation

The right policy will promote innovation in Smart Data, but a data skill and literacy landscape is also needed in the data ecosystem in order to build valuable products and services from available data infrastructure.

Stewards of Smart Data Schemes, and those implementing other data innovation programmes, will need to assess the capability of the in-scope sectors. This includes researching current capabilities such as the mix of organisation sizes and business models, product offerings, geographic spread and workforce skills, capacity needs and existing plans for future growth and product/service development.

The ODI Data Skills Framework offers guidance on the skills needed within the initiative itself, or within the organisations and community who are stakeholders in the initiative. When assessing existing capabilities consider the following questions:

  • What are the skills needed to meet the strategic needs of the initiative?

  • What skills does your organisation currently have? What skills do your initiative partners and other stakeholders have?

  • Should your initiative support a skills development programme within the organisation, or for a wider community?

  • What are the key skills or knowledge gaps to be addressed?

  • Will the skill needs of the initiative change as new data infrastructure is created, or existing data infrastructure is changed?

Testing data infrastructure and commercial models for Smart Data innovation

Finally, with a data policy that allows for innovation in Smart Data, and the right skill and literacy landscape to create value from data, innovation programmes will need to test the data infrastructure - especially the accessible datasets and data-driven business models.

At the core of this is setting up a Smart Data/cross-sector-specific regulatory sandbox to test innovations to allow data providers, data users and other innovators to boost innovation by testing new products and services safely before releasing to the wider market. Open dialogue with regulators through a sandbox process would enable regulators to understand new ideas and help address both risks and benefits to consumers early on.

There is ample research and evidence in this space already. Those running the sandbox should leverage the findings of the FCA Cross-Sector Sandbox feasibility study, to support development of the sandbox. Include the ICO in sandbox development, and BRE for support (e.g. coordination, funding) of the long tail of smaller regulators.

Regulatory sandboxes have been very a successful means of testing new data-driven propositions recently. Organisations running and participating in these sandboxes have provided impact reports that can guide the future development of sandbox-styled interventions:

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