A Structural Breakthrough in Thinking About Data Systems

Data systems lie at the heart of modern society, shaping public services, business operations, and innovation. However, traditional approaches to these systems often fail to reflect the dynamic and interconnected nature of today’s world. A structural breakthrough in thinking is required to ensure data systems become adaptive, responsive tools that truly serve societal needs.

The Limitations of Current Data Systems

Data systems are frequently designed as static structures, relying on centralisation and pre-defined frameworks. This approach creates significant issues:

1. Rigidity: Systems that attempt to account for all possible scenarios upfront become inflexible and unable to adapt to changing societal needs.
2. Loss of Context: Data often loses its meaning when detached from the environment in which it was created, rendering it less useful and insightful.
3. Privacy Risks: Centralised repositories of personal data increase the risk of breaches, misuse, and non-compliance with privacy regulations such as the GDPR.

Recognising data systems as evolving ecosystems, rather than static constructs, is essential to overcoming these limitations.

Contextuality and Complementarity: Foundations of Effective Systems

Developing effective data systems and services requires two critical principles:

1. Contextuality: Data and solutions derive their value from the specific context in which they are applied. For example, water is essential and refreshing in a swimming pool but poses a danger during a flood. The value of a system lies in its relevance to its immediate circumstances.
2. Complementarity: Collaboration across diverse perspectives and disciplines generates richer, more sustainable outcomes. Engaging multiple stakeholders with unique insights fosters innovation and resilience.

These principles emphasise the importance of designing systems that are adaptive, collaborative, and responsive to societal dynamics.

Navigating Complexity in a Networked Society

In today’s interconnected world, individuals and organisations operate within expansive ecosystems of relationships, defined by:

• One-to-Many Interactions: Individuals engage with numerous organisations, such as healthcare providers, financial institutions, and public services.
• Many-to-Many Exchanges: Groups and organisations collaborate, exchanging information and resources in complex networks.

Data systems must acknowledge these intricate relationships and address them through transparency, accessibility, and accountability.

The Product as a Multidimensional Abstraction

A product, whether digital or physical, is not simply an object but a representation of its users and their needs. Its structure can be understood through four dimensions:

1. Tasks (Events): The specific actions or events that give meaning within a given context.
2. Time-Bound Activities: The sequence and duration of actions occurring over time.
3. Information: The data that is used, created, or shared.
4. Rules: The guidelines and norms that govern the flow of activities and data.

By incorporating these dimensions, data systems become more meaningful and reflective of real-world needs.

The Risks of Centralised Data Lakes

The idea of centralising all citizen data in large-scale repositories, such as national data lakes, introduces serious risks:

1. Privacy Violations: Centralised sharing of personal data between organisations often breaches privacy laws and citizen rights.
2. Large-Scale Breaches: Centralised repositories become prime targets for cyberattacks and data leaks, risking widespread harm.
3. Loss of Trust: Overreach in data sharing erodes public confidence in organisations and governance structures.

Instead of centralised models, anonymised or context-specific data-sharing approaches can safeguard privacy while improving decision-making.

Flexible Alternatives: Dynamic Archiving as a Foundation

Innovative solutions like ArQiver provide a blueprint for flexible, decentralised, and scalable systems. These alternatives bypass the pitfalls of centralised data lakes and offer practical, adaptable frameworks. Key principles of such systems include:

• Immediate Relevance: Data remains tied to its specific purpose and context, ensuring usability.
• Accessibility: Direct access to relevant data eliminates unnecessary intermediaries.
• Agility: The system’s design allows it to evolve in response to societal and technological changes.

A Call to Action: Rethinking Data for a Dynamic Future

The limitations of rigid, centralised systems are increasingly clear. To meet the demands of a rapidly changing world, we must adopt a new mindset that recognises the dynamic nature of data systems. This involves:

1. Reimagining Data’s Role: Viewing data as a means to address societal challenges, not an end in itself.
2. Embracing Change: Accepting that systems must grow organically and adapt flexibly to new needs and contexts.
3. Championing Flexibility: Leveraging decentralised and modular tools to build systems that prioritise trust, transparency, and adaptability.

Now is the time for transformation. By adopting these principles, we can create a data-driven future that respects privacy, fosters trust, and addresses the complexities of modern life. The future of data systems lies in flexibility, contextuality, and collaboration—let us embrace this opportunity to reshape the role of data for the better.

Hans van Bommel

Founder, ArQiver