Boosting Data Adoption with Data Product Marketplaces
This session explores how implementing a data product marketplace streamlines data discovery, governance, and data adoption across organisations.
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In this session, Priyanshi discusses how organisations can boost data adoption using a data product marketplace. She highlights the inefficiencies in traditional data project lifecycles, where data discovery and preparation are time-consuming. The data product marketplace approach focuses on building reusable, well-governed, and discoverable data products that can be leveraged across multiple use cases, significantly accelerating insights and reducing redundant work.
Key features of the marketplace include:
- Searchable and well-documented data products with built-in quality, governance, and contextual information.
- Enhanced discoverability through filters, favourites, and domain/use-case mapping.
- Compatibility with external tools (e.g., Excel, Tableau, APIs) to increase adoption.
- Feedback loops to keep data products relevant and up-to-date.
- Visual lineage and semantic models to help users understand data relationships and trustworthiness.
She showcases an implementation of the data product marketplace, emphasising how users can quickly find, evaluate, and use data products for various business and technical needs. The session concluded with a call to adopt use-case-driven, well-governed data products to revolutionise data usage across organizations.
Who is this course for
01
Data Product Managers
02
Data Scientists
03
Business Analysts or Business Stakeholders
What you’ll learn
What is a data product marketplace and why is it important?
A data product marketplace is a platform where reusable, well-governed, and discoverable data products are made available, enabling organizations to accelerate insights and avoid redundant data preparation.
How does a data product marketplace improve data adoption and efficiency?
By providing searchable, well-documented, and quality-assured data products, the marketplace allows users to quickly find and reuse data for multiple use cases, significantly speeding up analytics and decision-making.
What features make data products more usable and trustworthy?
Key features include built-in governance, quality checks, detailed documentation, compatibility with external tools, feedback loops for continuous improvement, and visual lineage to understand data origins and relationships.
Priyanshi Durbha
Priyanshi Durbha is a Data Product Evangelist and Principal of Advanced Analytics. With over a decade of experience spanning analytics leadership, solution engineering, and data science, she specialises in turning complex data challenges into actionable insights that drive business impact.
Learn more
Enroll for free today!
- Data adoption boosters
- "Purchase" stages and XP
- Implementation of Data Product Marketplace
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