Data Products
The Data Product module is a major innovation developed at DataGalaxy to transform data into usable, documented and governed products. This module allows you to describe, publish and share datasets, pipelines or analyses as finished products, ready to be consumed via an integrated Marketplace.
The aim is to build a bridge between technical teams and business units by making data understandable, accessible and useful for decision-making.
Main objective
To give visibility and business value to company data through a ‘data as a product’ approach.
Secondary objectives
To structure and document Data Products in a dedicated module.
To enable their publication and discovery via a Marketplace.
To make data actionable for business users.
To create a business-centric experience to encourage adoption.
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In many organisations, data is abundant but difficult to exploit in a concrete and measurable way. Two major issues emerge:
For business users:
Difficulty identifying which data can meet their needs
Lack of context to understand the value and use of the data
Lack of a clear vision of concrete applications
For data teams:
Lack of a framework to structure and document their data assets
Difficulty promoting their work to business users
Lack of a standardised publication system
How might we …
Structure, document and distribute our Data Products via a Marketplace to facilitate their discovery and consumption by business units?
UX Phase
1. Discovery & User Interviews
A series of qualitative interviews were conducted with five major clients to understand their needs and their level of maturity with regard to the concept of Data Products.
Heterogeneous maturity across organisations
Some organisations acknowledge that they are ‘not yet ready to document Data Products’, while recognising their strong long-term value.
Others show advanced maturity with specific governance needs.
Critical need for business context
We are not yet mature enough to embark on this process, but we can see the potential for better structuring our business needs.
It is really the metadata surrounding the data that matters.
High expectations expressed
A marketplace approach rather than a domain-based approach to facilitate discovery
A granular view of quality rather than an overall score that is difficult to act upon
Clear and standardised documentation on datasets
Complete traceability of data and its uses
2. Competitor audit
In-depth analysis of Atlan, a major competitor in the data catalogue market, to identify:
Industry best practices
Opportunities for differentiation
Proven interface patterns
3. Design principles
Based on the insights gathered, we established 4 guiding principles:
Business-centric
Think first for business users, not just data stewards
Full user journey
Deliver a complete experience from discovery to use, not just a simple feature
Data consistency
Ensure logical links between objects (domains, contracts, use cases)
Scalability
Lay the foundations for an evolving Marketplace that integrates future module
4. Wireframes
Creation of wireframes to materialise key user journeys:
Discovery and search for Data Products in the Marketplace
Consultation of a complete Data Product sheet
Publication of a new Data Product
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Marketplace
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Data product page
Integration of Output Ports: making data consumable
A well-documented Data Product is not enough if the user does not know how to access it in practice. That is why we have integrated Output Ports directly into the Data Product pages.
What is an Output Port?
It is a standardised output point that describes:
Where to retrieve the data (Snowflake database, API, file, Kafka topic, etc.)
What format and data contract are available (schema, version, expected quality)
How to distinguish between multiple outputs from the same product according to your needs (aggregated view, real-time feed, raw export, etc.)
This feature has proven essential in moving from a simple document catalogue to a true distribution system for ready-to-use data.
Project developments
As the project progressed, two critical needs emerged, leading to changes in the initial scope to enhance the value of the Data Product module.
Creation of the Strategy module: giving business meaning to Data Products
Initial user tests revealed a major limitation:
A Data Product alone remains abstract. We need to understand the business context in which it is used.
This observation led us to create the Strategy module, a dedicated space for documenting business use cases and linking them to Data Products.
What a Use Case allows you to do:
Describe the business goal (automate reporting, predict demand, etc.)
Identify the target audience and teams involved
Share an example of real-world use
Measure the expected result (time savings, reduction in errors, better decision-making)
Although not included in the initial V1, the Strategy module quickly became indispensable for giving meaning to data products. It creates a direct link between technical data and its business value, facilitating adoption and understanding by business users.
UI Phase
1. High-fidelity mock-ups
Design of final interfaces incorporating:
The DataGalaxy design system to ensure consistency
Feedback from the wireframing phases
Technical constraints identified with the engineering team
Key components designed:
Data Product sheets with context, quality, and output ports sections
Marketplace interface with advanced filters and search system
Integrated Strategy module to link Use Cases to Data Products
Output Ports system to describe data output points
2. Prototyping
Development of interactive prototypes to:
Test complete end-to-end flows
Identify friction points before development
3. Demo
Presentation of prototypes to pilot customers to:
Validate the product approach and ergonomics with users
Collect iterative feedback
Adjust development priorities
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Marketplace
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Data Product page
Analyze impact
The Data Product & Marketplace module has bridged the gap between technical and business teams, making data not only accessible, but above all understandable and actionable.
The integration of the Strategy module has strengthened traceability between data and business results, thereby supporting the maturation of organisations' approach to ‘data as a product’.
Results
Increased adoption by business users thanks to a more intuitive and business-centric approach
Better visibility of the real value of data through documented use cases
Gradual formalisation of data products and data contracts in client organisations
Reduction in search time and increased confidence in data
Keep reading
More examples of design that drives results.
