We are establishing a civic trust for data, working with a partnership of social economy actors including grant funders, social investors and social organisations that have accessed social investment or grant funding.

Why do we need data trusts?

Our world is digitizing faster than our ability to understand or govern it. The tools that we’ve used to build collective governance – organisations – appear and dissolve, taking the digital assets they create with them. We need better models to protect public interests in digital assets, and help those interests persist beyond the lifespan of a business case. We also need governance models that do better at:

  • Protecting data subjects – that is, the individuals and organisations that provide the data in large data sets
  • Encouraging data sharing between civil society organisations
  • Making it possible for corporates to make their private data sets available for public benefit
  • Finding means to make data accessible for use in ways that don’t require a default ‘open’ model, when ‘open’ wouldn’t be safe or practicable

The Social Economy Data Trust aims to respond to that need by drawing on an old model in a new context: a civic trust for data.

Digital Civic Trusts are legal agreements that embed governance mechanisms into the ownership infrastructure of digital assets. This approach starts with using trust agreements to appoint and structure the ownership of digital assets, which draws on common law’s oldest approach to protecting assets in the public interest. It draws on the model of civic trusts, which substitute the traditional trustee organization with an informal community governance structure. Digital Civic Trusts go a step further, embedding the community governance into the trust agreement itself, adding a layer of accountability for trustees, who have a legal responsibility for preserving the public interest.

Digital Civic Trusts could solve three key challenges commonly faced by civil society organizations:

  1. They move ownership away from the civil society organization, creating a safe space to experiment with different models of engagement;
  2. They embed governance at the asset level, ensuring that even if a civil society organization closes down, the underlying data is managed in the public interest; and
  3. They give data subjects and stakeholders a meaningful role in holding responsible parties accountable for decisions that affect their well-being, without relying on individual data subjects having to litigate to assert their rights.

You can read more about Sean McDonald’s work on data trusts here.

Why do we need a data trust for the social economy?

Data has been under-resourced, poorly collected and little shared within the social economy.

There is an opportunity cost attached to the paucity of data available within the ecosystem that seeks to address the chronic under-capitalisation of social businesses. In the absence of effective information use, the sector will continue to be limited in its ability to demonstrate the ways in which an alternative economy supports a fairer, healthier and more environmentally friendly society. It will also be harder to invest wisely, with a good understanding of what has worked in the past, the local economic context within which social businesses operate, and the additional support needs that may need to be met alongside finance.

Immediate pressures, and opportunities, make this problem acute.

  • Place-based strategies are of increasing importance in the social sector. This recognises the cumulative set of disadvantages that are affecting parts of England, the likely impact of Brexit, and the continuing pressure on wages and rising cost of living for the least well off. Whilst interest in place-based funding is high, approaches to developing effective place-based strategies are still tentative. The benefits of effective, reliable, cost-efficient and iterative approaches to data use in this area are likely to be considerable.
  • There is a resurgence of interest in the social economy and its benefits for securing standards of living in areas struggling to sustain their economies and workforce. This includes a renewed commitment to exploring the means of equalising access to returns from capital as wages continue to stagnate, and in many cases fall in real terms. Exploiting this opportunity demands a more active engagement with data and evidence of a kind that will be compelling to policy makers and investors.

In addition to this context of investment that demands more informed decision-making, there is a specific set of questions regarding effective data use that can be tackled by the civic data trust structure.

  • Data use across the social economy is of variable quality, with experienced social lenders and grant-makers holding large quantities of data that needs cleaning and analysing to serve a useful purpose. They have rarely been in a position to dedicate appropriate resource to the work of cleaning and analysing. There are, however, positive developments in this area, and real enthusiasm for reviewing data and making better use of what is held by organisations. This includes the work already being done on the 360Giving data standard and its GrantNav search engine and database, the Open Contracting data standard and the publication of government data using unique identifiers, and innovations such as data releases under the open banking rules coming into operation in 2018.
  • Data is increasingly being understood as a collective asset that should be managed by collaborative groups, rather than a private good secured by legal action and citizen activism. This requires advocacy and training as well as appropriate governance models.
  • Data access is best brokered through a collaborative partnership rather than through individual organisations competing to develop bespoke systems. This is for two key reasons:
    • Value for money – particularly in funding access to privately held data
    • Effective governance – this is needed both as part of more reflective data ethics, but also to manage access to data sets that are currently inaccessible in the absence of fair and agile systems for managing their use

The project

This project will establish a civic trust for data working with a partnership of social economy actors including funders, social lenders and end user businesses that have accessed social investment and / or grant funding.

The core pieces of this work are outlined below:

  • Developing a membership portal for end users that reduces time-consuming duplication in applying for funding. This would operate on a set of data permissions, allowing members to share their data directly with the Data Trust, and receive benchmarked reports to inform their own development. Social lenders and trusts and foundations would be able – subject to appropriate permissions – to access this data and contact potential grantees and investees when new funds are launched. This core member data could be merged with standardised social sector diagnostics to encourage reflective practice alongside robust quantitative data use. Read more about the diagnostics work here.
  • Developing a partnership approach to data use across the social economy, held within a civic trust that represents all beneficiaries equally in its governance structure. This will involve the establishment of the first civic trust for data in the UK, through shared partnership agreements, data sharing agreements and representative governance.
  • Development of a data standard for social investment. Social investment is currently hard to track. Big Society Capital operates as a central clearing house of data, and produces useful open source information on a range of deal features that allow for high level mapping of the sector. This data could be made more useful through a shared standard with unique non-proprietary IDs for each organisation accessing investment. Read more about this here.  
  • Retrospective data analysis. This retrospective work creates a data set that will demonstrate what good, better and best looks like in social investment through analysis of loan size, sector, and success. Benchmarking of a reasonable sized data set should support a reduced burden of due diligence, and cheaper access to finance for end user social businesses.
  • Brokering access to intermediate data sets to inform place-based investment by taking the strain of data collection off end user social organisations, and sharing the cost and effort of developing data sharing agreements and technical specifications across a partnership of intermediaries including trusts and foundations, social lenders and commercial data companies.
  • Developing consistent and cost-effective data analyses across data sets to inform investment and grant-making, and track social and financial performance at deal and fund level. This ‘end-to-end’ approach to developing a funding strategy, monitoring that strategy and evaluating its effects, has significant potential for the social economy.