Discover what a data product is, its benefits, its lifecycle and its role in the data mesh to create reliable and useful data for businesses.
Data product: transform your data into strategic assets
Adopt a data product approach to unlock the value of your raw data, accelerate decision-making, and build a data-driven organization. Discover how data mesh, data governance, and data marketplaces enable the creation of reliable, useful, and sustainable data products.
Why consider a data product for your organization
Companies today generate enormous volumes of data. Yet, only a minority truly leverage it to create value. The concept of a data product changes this logic: it involves designing each dataset as a fully-fledged data product, with its own lifecycle, governance rules, and clear business objectives.
This approach enables the transformation of raw data into actionable, reusable, and measurable data products. Each data product thus becomes a strategic component of the business, supporting business processes and informed decision-making.
From raw data to data product
Historically, data was seen as a mere by-product of business activities. Today, data product thinking makes it an asset in its own right.
Creating a data product means defining:
- a clear business objective (optimize a customer journey, improve a forecast, reduce a cost);
- a documented life cycle;
- a measurable quality;
- and interoperability with other data products.
Each data product must be documented, tested and versioned to ensure smooth operation by data scientists and business teams.
The goal: to make the implementation of data products as rigorous as that of software, while ensuring reliable and actionable data.
The tangible benefits for decision-making and professions
The main advantage of the data product lies in its ability to transform data into a strategic decision-making tool.
Thanks to this approach, data departments can:
- accelerate the transition from data to insight;
- make business dashboards more reliable;
- automate processes via machine learning;
- and promote user autonomy through self-service.
This approach reinforces the data-driven culture and improves the alignment between technology and business value.
Implementing a data product
Key stages: lifecycle, documentation, testing, usage
Implementing a data product requires a structured approach:
- Identify business needs and high-impact use cases.
- Model the data and define the key indicators.
- Document the product in a data catalogue to ensure its reuse.
- Industrialize production (testing, monitoring, updates).
- Measure the value created for the business.
This method ensures that each data product remains aligned with the strategy and operational objectives.
The role of data scientists and business teams
Data scientists, data engineers and business managers collaborate to design, maintain and evolve the products.
This approach promotes a common language and shared responsibility for data quality and use.
Teams become co-producers of value, which accelerates the data-driven transformation and sustainably embeds the data culture.
Data mesh architecture serving the data product
What is data mesh and why adopt it?
Proposed by Zhamak Dehghani, the data mesh is a decentralized architecture where each business domain manages its own data products.
Unlike monolithic architectures, it distributes responsibility and encourages team autonomy.
Each domain becomes a provider of data products, ensuring fresh, relevant, and accessible information.
This organization reduces dependencies, improves scalability, and strengthens data governance.
Distributed governance and long-term scalability
The data mesh introduces distributed governance, in which domains adhere to shared standards while retaining their autonomy.
The rules for quality, safety and documentation are standardized to facilitate interoperability.
This approach guarantees organic growth of the data asset, ensuring long-term sustainability and continuous evolution without technical disruption.
To learn more about these principles, see the article "Data, a product like any other?" on the Smile website.
Data marketplace and data economy
Data catalog, self-service and monetization
Advanced organizations are setting up internal or external data marketplaces to exchange or monetize data products.
These platforms promote discovery, reuse, and transparency.
Thanks to a centralized data catalog, users can access validated datasets, APIs, or analytical models on a self-service basis.
This approach creates a data-centric economy, where each dataset becomes a resource in its own right, generating opportunities for revenue and innovation.
Data security, compliance and governance
A successful marketplace relies on “by design” governance.
This implies:
- strict access control,
- complete traceability of usage,
- pseudonymization and encryption of data,
- and GDPR compliance.
This rigor strengthens user confidence and guarantees the security of exchanges.
To delve deeper into these aspects, check out the article "Data Governance: Definition and Challenges" published by Smile.
Governance, quality and security for a reliable data product
Reliable data: process, quality, metadata
A data product is only valuable if it is based on reliable and well-governed data.
This requires the implementation of data quality processes, traceability and clear metadata cataloging.
Good governance promotes trust and reuse.
Open source tools and open standards, widely used by Smile , make it possible to build interoperable and sustainable solutions, reducing technological dependence.
Managing a sustainable data-driven strategy
Beyond the tools, success relies on a long-term strategic vision.
Companies need to invest in training, standardization, and measuring the value generated by their data products.
Management dashboards allow you to track product performance, data quality, and the impact on business decisions.
This transparency fuels a virtuous cycle of continuous improvement.
Transform your organization into a “data-product organization”
Adoption of profession, culture and long term
The transition to a data product organization requires a profound cultural change.
Decision-makers must promote a culture of shared data, based on responsibility, measurement and use value.
This involves supporting professions, training and the implementation of tools that promote inter-team collaboration.
Key metrics, dashboards, return on investment
To firmly establish the data product approach, results must be measured:
- reduced data access times,
- quality improvement,
- product reuse rate,
- and direct contribution to business objectives.
These indicators provide a clear view of the value created by data and justify future investments.
Conclusion and call to action
Data product thinking and data mesh are redefining how companies leverage their data.
They enable the transformation of raw data into value, making organizations more agile, responsible, and data-driven.
Contact our Smile experts to set up your data products and structure your data mesh strategy and data governance.
Together, let's transform your data into strategic and sustainable assets.