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In a context where data is becoming organizations’ most valuable asset, moving from passive management to an active strategy is a top priority. Structuring each dataset as a true product, distributing governance according to business responsibilities, and considering value creation within an external ecosystem are all levers to make full use of data. This article introduces the concepts of data products, data mesh, and the data economy, highlighting their concrete benefits. Each of these paradigms relies on governance, security, and interoperability principles that ensure robust, sustainable data exploitation.
Data Product: Ensuring Reliability and Usability of Every Dataset
Every dataset becomes an identified, documented, and versioned product. This approach guarantees data quality, traceability, and reuse across the enterprise.
The Data Product Concept
A data product is a structured dataset accompanied by metadata, service contracts, and SLAs. It’s treated like a traditional product: it has an owner, a roadmap, and a budget for future enhancements.
This product mindset clearly assigns responsibility to each team for the quality, availability, and security of the data they publish. It also simplifies prioritization of updates and fixes based on the generated business value.
Beyond basic collection, the data product includes automated cleaning, transformation, and documentation processes. Consumers know exactly what to expect when they use this dataset.
Implementing a Data Product Catalog
To roll out a data product approach, begin by inventorying your key datasets and defining clear schemas. A centralized catalog lists each product, its schema, its owners, and its end users.
Governance relies on continuous integration workflows for data: quality tests, consistency checks, and compliance verifications. Every change goes through automated pipelines that validate the product against defined standards.
The documentation, versioned like a code repository, dissolves the opacity often associated with data. Each data product version notes changes, new fields, and impacts on consuming applications.
Example: A Financial Services Firm in Geneva
At a Geneva-based financial institution, the risk management department structured internal transaction flows into data products. Each of these products integrates automated validation rules, ensuring over 99% reliability.
Deploying a central catalog enabled analysts to save more than 20% of time on their monthly reports. Business teams can now quickly identify and investigate discrepancies without constantly involving IT.
This setup was also extended to compliance data, reducing manual audits and mitigating regulatory risks while enhancing cross-functional collaboration.
Data Mesh: Empowering Business Teams for Greater Agility
Data mesh adopts a distributed architecture where each business domain becomes both producer and consumer of its own data. This decentralization accelerates innovation cycles and reduces technical dependencies.
Fundamental Principles of Data Mesh
Data mesh is built on four pillars: domain-driven ownership, data products, self-service platform, and federated governance. Each domain takes responsibility for its data from production through consumption.
An internal platform offers standard building blocks (ingestion, storage, cataloging, security) in a self-service model. Business teams use these services to deploy their data products quickly without managing the underlying infrastructure.
Federated governance ensures overall coherence while allowing each domain to define its own rules according to its needs. A cross-domain committee sets inter-domain standards and oversees best-practice compliance.
Operational and Organizational Impacts
By empowering business teams, data mesh eliminates the bottlenecks often seen in central IT. Developments can proceed in parallel with more frequent releases.
This approach also fosters innovation: each domain can swiftly test new metrics, analytical models, or data-driven services using its own data, without relying on a centralized BI team.
Finally, the model reduces vendor lock-in risk: by embracing an open-source, modular strategy, the architecture can evolve without major disruptions.
Example: An Industrial Group in German-Speaking Switzerland
A German-Swiss industrial group adopted data mesh to manage its production lines. Each plant now handles its IoT sensors as a data product with self-service automated alerts.
Operations teams can view equipment performance in real time and propose local optimizations without involving a central control center. Incident response time dropped from several hours to a few minutes.
This increased agility also enabled new predictive maintenance services, boosting machine availability and cutting unplanned costs.
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The Data Economy: Monetization, Sharing, and Value Creation
The data economy explores internal and external monetization models for data products. Selling, sharing, or trading data opens up new revenue streams and partnership opportunities.
Internal and External Monetization Models
Internally, value is realized through internal chargebacks or budget allocations based on data product consumption, encouraging domains to optimize flows and minimize unnecessary costs.
In the external economy, data marketplaces enable selling or swapping anonymized datasets with partners. Companies can generate additional revenue or gain cross-industry insights.
Transparent pricing (subscription, volume-based, user count) ensures predictability. Real-time consumption tracking feeds billing and revenue-sharing.
Data Partnerships and Ecosystems
Building data ecosystems requires defining exchange contracts that ensure confidentiality, compliance with nLPD, GDPR, and traceability. Every access is audited and scoped to business purposes.
Sectoral consortiums (finance, healthcare, supply chain) can pool certain data products to create benchmarks and shared metrics. Secure sharing fuels collective innovation.
Open APIs based on standards ease integration of external data and the creation of high-value services like cross-company dashboards or collaborative predictive analytics.
Example: A Swiss Healthcare Network
In a Romandy hospital network, anonymized patient follow-up datasets were made available through an internal marketplace. Academic and pharmaceutical partners access these data products under strict conditions.
This initiative launched several clinical studies at half the usual cost and timeline. Researchers’ feedback improved data quality, creating a virtuous cycle.
Revenues generated directly fund IT infrastructure, reducing hospitals’ budgetary burden and accelerating adoption of new analytics.
Governance, Security, and Interoperability as Pillars
An advanced data strategy demands a clear governance framework, robust security, and adherence to open standards to ensure interoperability. These elements build trust and scalability.
Agile Governance Framework
Agile governance relies on cross-functional bodies (business, IT, architecture, risk) that define and adjust rules iteratively. Periodic reviews reassess priorities, budgets, and risks.
Data contracts formalize quality and availability commitments. They’re monitored automatically, with alerts for any degradation.
Consolidated dashboards provide visibility into data product usage and quality, supporting strategic decisions and cost optimization.
Security and Compliance
Data security integrates encryption at rest and in transit, role-based access controls, and full query traceability.
Compliance with regulations (nLPD, GDPR, FINMA, ISO 27001) is validated through regular audits and alerting processes for unauthorized access attempts.
Open-source solutions are systematically assessed for maturity and vulnerabilities, ensuring a robust, evolving architecture.
Interoperability and Open Standards
Adopting standard formats and protocols (JSON Schema, OpenAPI, Apache Avro) simplifies data exchange across heterogeneous platforms.
Hybrid architectures combine open-source components with custom developments, avoiding proprietary lock-in while meeting business needs.
API-first design and event buses (Kafka, MQTT) enable real-time and asynchronous integrations vital for critical use cases.
Example: A Swiss Retail Chain
A national retail chain implemented federated governance for its inventory and order data, based on shared data products between stores and headquarters.
The platform uses REST APIs documented via OpenAPI, ensuring seamless integration with existing logistics and e-commerce systems.
This setup improved replenishment forecast reliability and customer insights while ensuring all sensitive data is consistently encrypted.
Leverage Your Data: From Management to Value Creation
Structuring datasets as data products, deploying a data mesh architecture, and exploring data economy models are the keys to an active data strategy. These approaches foster agility, reliability, and innovation while maintaining governance and security.
Implementing a catalog, empowering business teams, and opening up data partnerships illustrate the transformation required to turn data into a competitive advantage.
Regardless of your maturity level, you can adopt these principles to boost performance and anticipate future challenges. Our experts at Edana are here to guide you through this journey, from defining your roadmap to delivering your first data products.
