Sharing Data — benefits & opportunities, blockers & constraints

Benefits

Improved data sharing informs better decisions, leading to efficiencies in operations as well as growth in user demand for data, products and value-added services (including services relating to the growing data market).

From an economic perspective, as we’ve covered before, there is value in moving from Closed data to Shared and Open data, enabling other actors to re-use the data to provide new public and commerical services at the national levels and beyond. In terms of improved efficiency and effectiveness, there are the following benefits:

• Data sharing as a driver of improved data quality;
• Data is more readily discoverable, available, accessible and usable for internal users (as well as external users);
• Reduced overhead (human effort and transaction costs) of managing data exchange through standard licenses rather than multiple, separately negotiated (and hard to enforce) Memorandums of Understanding (MoU);
• Increased availability of data in machine-readable formats increases opportunities for the automation of tasks, releasing skilled staff for other activities which fully utilise their skills — improving both efficiency and staff engagement;
• Improved recognition for the stewardship role undertaken by an institution arising from the implementation of terms and conditions in standard licences for data use which mandate the acknowledgement of data providers;
• Improved data resilience resulting from data sharing; and
• Improved user requirements for processing systems based on a sound understanding of data.

Opportunities

A number of new/ improved opportunities are likely to result from improved data sharing. Some of these will arise from greater ‘ease of working’ for an organisation and its stakeholders (including its customers and partners). Meanwhile, the creation of a network of active data users will drive data innovation. Opportunities include:

• Growing demand for data — and growing evidence of that demand and of the value of the data;
• Growing a network of active users of data who can help to iterate the use case, prioritise datasets and maintain the usefulness of the data — prioritising the most widely requested data in the right format and at the highest quality, and enabling ‘collective learning and capacity building’ for everyone in the network (including staff within implementing institutions);
• Development of new partnerships that can meet the user needs; and
• Development of new services relating to data e.g. training sessions on how to use data.

Blockers & constraints

There are a number of potential blockers and constraints to the implementation of a model for improved data sharing These fall under a number of headings. Issues include, but are not limited to:

Human

Fear of:

• Data quality not being good enough — fear of embarrassment;
• Losing control of the data;
• Giving data to ‘unauthorised’ users, especially in the case of sensitive data, such as that linked to national security or intellectual property;
• Not being recognised, nor reimbursed, for the role of stewardship and the associated investment of resources (time, skills, finances).

Perception of users’ lack of awareness/ appreciation/ understanding of:

• What the data is, where it comes from and how it is produced (and what other datasets may have been used to produce it); and
• How to use the data ‘properly’ — what can/ cannot be done with the data.

Lack of, or inappropriate, incentives to deliver innovation.

Commercial

• Lack of incentives for regulated utilities to invest, develop/ support open data systems;
• Business cases for sharing specific data, or understanding of its value, are not obvious;
• Desire to maintain competitive advantage relating to the provision of value-added services;
• False expectation that significant revenue can be obtained from selling data which is often not the case (especially for data at low temporal and spatial resolutions), contributing to a reluctance to share data; and
• Investment needs to protect the national interest, security, data rights and/or commercial sensitivities.

Regulatory/ legal

• Uncertain, evolving (cross-sector) regulatory landscape regarding data;
• Compliance concerns and law — including the risk of unnecessarily constraining activities as a result of incorrectly applying a law/ regulation ‘wholesale’; and
• Management of consent, risks and liabilities for sharing data.

Technical

• Lack of resources to supply/ receive data;
• Lack of a secure, controllable environment supporting effective governance;
• Lack of the ability to discover appropriate data according to requirements;
• Lack of access to data in a standardised and structured way;
• Concerns over data quality and a lack of trust in their provenance; and
• Management of structural change and local variation across users.