How to share data outside of your organisation

Data has become a pivotal asset for organisations in various economic sectors; and emerging data sharing models shape relationships within industries and also boost innovation and societal progress. Understanding how these models work, therefore, is crucial for businesses and other organisations.

Data sharing models

Broadly speaking, data can be shared as part of general business operations, pooled among various participants, or unilaterally transferred to other parties.

Data shared as part of general business operations

Most organisations already share some data with their suppliers and customers as part of business-as-usual operations, enabling relationships to function and contractual obligations to be fulfilled. This may be expressly or implicitly covered by contracts and enables closer engagement and greater efficiency. For example, an organisation might provide a subcontractor with operational information to streamline the supply of goods or services.

Data pooled among participants

Organisations combine their data assets using harmonised models and standards with participants acting as both producers and consumers of data. These initiatives are normally led by a consortium, main player, or regulator. For example, Catena-X is the first collaborative, open data ecosystem for the automotive industry, linking 144 members, including BMW Group, Bosch, Siemens, Stellantis, and SAP.

Catena-X aims to make it possible for data to flow from the smallest subcontractor across end-to-end value chains to automotive manufacturers. Depending on the use case, the benefits accumulate to:

• all participants, who control the personally identifiable information (PII) data they generate, such as within digital twins or across the supply chain;
• the shared final customer, who controls how customer data, whether personally identifiable information or not, is used at a granular level; and
• wider society, with all participants pooling their data in initiatives that focus heavily on the green transition.

  Unilateral data transfer

  In this model, data producers transfer their data to other parties, either freely or by charging for the service. Types of this approach include selling data sets and insights directly to third parties or via data marketplaces, and open data initiatives, which are designed to drive transparency and innovation by providing data freely under open license to citizens and businesses.

  Encouraging data sharing

  Each of these models for data sharing is driven by key regulatory, business and technological factors. Legislation at both the EU and national levels aims to promote an open European “data economy” to ensure competitiveness and decarbonisation. For example, the 2020 European Strategy for Data translates into specific, concrete regulations and initiatives, including the 2020 Data Governance Act, which seeks to increase trust in data sharing, strengthen mechanisms to increase data availability, and overcome technical obstacles to the reuse of data.

  In addition, the 2022 Data Act seeks to harmonise rules on fair access to and use of data, including data generated by ‘internet of things’ (IoT) devices. These efforts are supported by industry-specific initiatives to enable the better use of data to solve problems at a sector/system level.

  Emerging opportunities and regulations are accelerating the need to focus on turning data into value through external sharing. Organisations can also now access a full range of enabling technologies and innovative new approaches to underpin external data sharing. These include cloud technologies, data-sharing platforms, standardised data models, federated sharing, APIs, governance frameworks, and advanced cryptography solutions, which enable data owners to share data while maintaining control over it.

  Best practices for external data sharing

  Excelling at data sharing and collaboration starts with strategic decision-making and then relies on four key capabilities to build momentum and value. Steps in the process include:

• defining the strategic objective for external data sharing and collaboration;
• defining the level of acceptable risk around the value of opening data versus exposing it too widely;
• identifying potential data ‘ecosystems’ to create, develop, or enter and define the organisation’s role within it;
• identifying potential use cases and sources of value from sharing; consider any ethical risks that need to be understood to mitigate possible reputational or revenue challenges; and
• considering on what basis the proposed sharing of data might end and what will happen to the shared data in that circumstance.

Key data sharing capabilities

Organisations need to understand internal data management capabilities, including cataloguing current internal data landscape, competencies, and gaps. They must also analyse external data-sharing capabilities, assessing the current partner landscape and potential opportunities.

At this stage, an organisation can activate its ‘ecosystem’ by building partnerships that augment internal capabilities, assess scope of the minimal viable consortium, establish a value proposition for each partner, and define governing roles and responsibilities.

Organisations should continue to monitor value creation, especially to identify new and emerging uses for data sharing. External data sharing is at the heart of increasing competitive advantage and delivering business value, particularly around AI, compliance, and meeting sustainability targets. To achieve success, organisations must take an ecosystem approach, based on a considered understanding of strategic, regulatory, legal, and technical themes.

Co-written by Jeroen Schouten of Pinsent Masons.

Join expert speakers from Pinsent Masons and Arthur D. Little in Amsterdam on 10 October for a panel discussion and networking event on the benefits of data sharing.