Data Sharing

Understanding Data Sharing: Characteristics, Challenges, and Ecosystem Archetypes

Characteristics of Data Sharing

Data sharing enables third parties, such as companies, individuals, and public institutions, to access data sets to develop new applications and services. The terms of data use and availability are determined by legal agreements between data providers, data consumers, and other involved roles, depending on the use case. Data sharing has many characteristics, highlighting that there is no single approach, as it depends on multiple factors, as shown below.


  1. Data marketplace

  2. Solutions


  1. Blockchain

  2. Cloud computing

Legal concepts

  1. Agreements

  2. Contracts


  1. Connecting

  2. Privacy-as-a-service

  3. Analytics

Customer Groups

  1. B2B

  2. C2B/B2C


  1. Data owner

  2. Data consumer

  3. Data provider

Data types

  1. Anonymous personal data

  2. Metadata

  3. Aggregated data

Challenges in Data Sharing: Not as Simple as it Seems

While the benefits of data sharing, such as scientific progress, transparency, and collaboration, are clear, the complexities and ethical concerns involved should not be overlooked.

  1. Ethical Concerns: Participants may not fully understand how their data could be accessed and used by others, especially by those with conflicting interests. This creates significant ethical issues, especially in areas involving sensitive or identifiable information.

  2. Confidentiality Issues: Anonymised data can sometimes be re-identified when combined with external information, posing risks to participant confidentiality.

  3. Impact on Business Integrity: Mandatory data sharing without safeguards can compromise business integrity, potentially affecting industries and regulatory policies.

What is the Data Ecosystem All About?

A data ecosystem integrates data from multiple sources to create value through processing. A successful ecosystem ensures these three priorities are met:

  1. Building Economies of Scale: Attract participants by lowering barriers to entry.

  2. Generating Customer Benefits: Establish clear benefits and dependencies beyond the core product to create high exit barriers over time.

  3. Fostering Collaboration: Motivate multiple parties with similar interests to collaborate and pursue shared objectives.

Scope of Data Ecosystems

Data ecosystems can be categorised into different archetypes based on their scope, data aggregation models, service types, and engagement methods:

  1. Data Utilities: Aggregate data sets to provide tools and services to other businesses (e.g., credit bureaus, consumer-insights firms).

  2. Operations Optimisation Centres: Integrate data within the business and across the value chain to achieve operational efficiencies (e.g., supply chain integration).

  3. End-to-End Cross-Sector Solutions: Integrate multiple partner data sets to offer comprehensive services through a single solution (e.g., car reselling platforms, testing platforms, partnership networks).

  4. Marketplace Solutions: Act as a conduit between suppliers and consumers or businesses, offering products and services (e.g., Amazon, Alibaba).

  5. B2B Infrastructure: Provide core infrastructure solutions on which other companies can establish their ecosystem businesses (e.g., data-management platforms, payment infrastructure providers).

The Blueprint of a Successful Data Ecosystem

Data ecosystems hold significant value, but entry barriers are typically high; therefore, companies must understand the potential obstacles. Success depends on finding the right business model to generate revenue and ensuring active participation. The next step is to prioritise strategic partnerships that offer compelling value propositions and deliver excellent customer experiences to attract end customers and collaborators.

Steps to Share Data Among Ecosystem Partners

The standard data-sharing mechanisms among partners typically follow three steps:

  1. Establishing a secure connection and trust.

  2. Exchanging data through cloud infrastructure and client marketplaces.

  3. Storing results is necessary.

These steps should be followed by adopting a decentralised and federated identity management approach, such as an open data-mesh architecture.

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