Data as a Product: Transforming Raw Data into Valuable Assets
Data-driven organizations are increasingly adopting the concept of "Data as a Product" (DaaP). This approach involves applying product management principles to the lifecycle of data, transforming it from a mere byproduct of operations into a valuable asset that is curated, recommended , and delivered to meet specific user needs.
DaaP represents a paradigm shift in how organizations perceive and handle data. Rather than viewing data as static resources, DaaP treats it as dynamic products that provide actionable insights.
Key characteristics of DaaP include:
User-centric design: Data products are designed with end-users in mind, ensuring they are accessible and actionable. This approach often integrates feedback loops to continuously refine the product based on user interactions.
Quality data : Rigorous data quality management processes, such as validation and cleansing, are essential to maintain high standards and foster trust among stakeholders.
Platforms like Data Product Marketplaces exemplify this approach by offering Data Product Build modules that streamline the creation and management of data products. This is a major tool that helps enable both technical and non-technical users to build high-quality data products efficiently, reducing manual effort across the development process.
The Business Imperative for Data as a Product
Adopting a DaaP strategy improves an organization's ability to leverage data effectively. By treating data as a product, businesses can:
Create Tangible Value: High-quality data helps enable better decision-making and drives business outcomes. For instance, major Fintechs and Consumer data-driven companies have successfully monetized their data products, contributing significantly to their market dominance.
Improve Discoverability: Designing data for self-service access allows users to obtain insights independently, streamlining operations and fostering innovation.
Building Data as a Product
Implementing DaaP involves several critical steps:
Shift in Mindset: Treat your data teams as customers to understand their needs and pain points. This requires applying product management thinking when building data products.
Define Ownership and Teams: Establish dedicated teams within each business domain consisting of data engineers, analysts, and domain experts who will be accountable for their specific data products.
Set Clear Objectives and Metrics: Define objectives you want to achieve with DaaP, such as improving decision-making or Improving customer insights. Establish KPIs to measure impact and identify gaps for improvement.
Develop Robust Data Architecture: Design a flexible architecture that supports the creation, management, and consumption of data products. This architecture should facilitate integration, storage, retrieval, and interoperability across the organization.
Implement Governance and Quality Standards: Establish comprehensive governance policies to protect your data while implementing quality management processes like validation and cleansing to maintain high standards.
User-Centric Data Catalogs: Develop detailed catalogs that provide information about each data product, help ensure they are user-friendly and searchable for quick access.
Invest in Training & Change Management: Facilitate training programs to help ensure organization-wide understanding and adoption of DaaP practices.
Integration with AI and Analytics: A significant trend is the integration of AI-driven insights into data products.
Challenges and Considerations
While the benefits of treating data as a product are significant, organizations may face challenges during implementation:
Technical Complexities: Developing high-quality data products requires collaboration across various teams while adhering to governance standards.
Quality vs. Quantity: Organizations often struggle with prioritizing the number of data products over their quality. Focusing on delivering minimal viable data products (MVDPs) helps ensure that each product meets essential quality criteria.
Data Governance as a Foundation: Effective data governance is critical for the successful implementation of DaaP. This underscores the necessity of establishing robust governance practices to help ensure data quality and compliance. Efforts such as the ones below are critical to DaaP success.
Unified Governance Models: Creating comprehensive governance solutions that advice/recommend policies across the enterprise.
Augmented Data Quality: Utilizing AI to improve data accuracy and consistency.
To overcome these challenges, organizations should adopt iterative development processes that incorporate stakeholder feedback while emphasizing collaboration across teams.
Future Outlook
As the landscape of data management evolves, the importance of DaaP will only grow. Emerging technologies such as artificial intelligence (AI) and machine learning (ML) will enhance the capabilities of data products, enabling organizations to derive deeper insights from their assets.Organizations must embrace this shift toward treating data as a product to remain competitive. By doing so, they improve operational efficiency while positioning themselves for sustainable growth in an increasingly complex business environment.
Challenges in Adoption: Despite the growing recognition of DaaP, many organizations face challenges in implementation. These include difficulties in hiring skilled personnel, establishing best practices, and integrating governance tools into existing tech stacks.
Future Outlook: Looking ahead, our research anticipates that organizations will increasingly adopt DaaP as they seek to leverage their data assets more effectively. The emphasis will be on creating user-friendly, high-quality data products that can drive business value.
Conclusion
In summary, treating data as a product represents a transformative approach that empowers organizations to unlock the full potential of their data assets. By focusing on quality, usability, and user satisfaction, businesses can create valuable information products that drive strategic decision-making and foster innovation. As we look ahead into 2025 and beyond, embracing DaaP principles will be crucial for organizations aiming to thrive in the digital age.
The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.
Aradhna Mangla
SCORE: EYG no. 001178-25Gbl
About the author:
Aradhna leads AI & Data Consulting for financial services firms. She has been advising Chief Data Officers (CDOs) and data leaders on topics such as financial technology, data management (data governance), regulatory reporting (SEC, SFDR, TCFD, GRI), sustainable finance, and adopting responsible use of AI and analytics.
She is passionate about building a sustainable future, mentoring future leaders, and crafting purpose-filled careers. Her commitment to mentorship aligns with the industry's need for skilled professionals who can navigate the complexities of data and AI integration in financial services.
Aradhna's work is at the forefront of addressing key challenges in the financial services sector, including the need for solving data integration and accessibility, sustainable investing and lending practices, and modernized risk modeling using AI capabilities. Her expertise positions her as a valuable resource for organizations seeking to leverage data for competitive advantage and positive societal outcomes.
With a Master's degree in Economics from Boston University, Aradhna combines academic knowledge with practical industry experience of 12+ years. She’s always happy to connect and mentor the future of work.