Data Products: Unlocking the Value of Data

In the rapidly evolving financial services landscape, data has become a critical asset. Modern-day financial institutions are increasingly leveraging data to drive innovation, enhance decision-making and maintain a competitive edge.

Central to this transformation is the concept of ‘data products’. Data products are curated, ready-to-use datasets, designed to meet specific business needs, and they can be easily consumed by various stakeholders within an organization. They are provided to end-users in a way that increases ease-of-use and improves decision-making. Data products often complement other contemporary developments in the data landscape, such as data marketplaces and data contracts. But, implementing data products in environments with complex IT landscapes and diverse enterprise data is more challenging than it may initially appear. This article will explain how organizations adopt data products by exploring the journey from origination to consumption, defining organizational roles and responsibilities, and addressing common pitfalls and their solutions.

Data Products: Foundational and consumer

Data products fall into two main categories, ‘foundational’ and ‘consumer’.

Foundational data products
Foundational data products serve as the building blocks for consumer data products. They are typically downstream of business domains, such as Finance, HR, or Sales. These products don’t necessarily address specific use cases but instead provide a robust data infrastructure.

Key characteristics:

• Provide core data infrastructure.
• Focus on data quality, consistency, and accessibility.
• Often involve data integration, storage, and management.
• Serve as a foundation for building other data products.

Consumer data products
Consumer data products are built upon foundational data products and are designed to meet specific business needs or use cases. They leverage the standardized data from foundational products to create value-added insights or functionalities.

Key characteristics:

• Designed for specific use cases or business problems.
• Focus on user experience and ease of use.
• Deliver actionable insights or data-driven functionality.
• Often involve data visualization, analytics, or machine learning

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