Experiencing Data w/ Brian T. O’Neill  (UX for AI Data Products, SAAS Analytics, Data Product Management) cover image

Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)

134 - What Sanjeev Mohan Learned Co-Authoring “Data Products for Dummies”

Jan 9, 2024
Former Gartner analyst and Co-Author of Data Products for Dummies, Sanjeev Mohan, discusses the evolution of data products, implementing practices for business value, and new approaches to organizational structure. They also explore challenges of product adoption and responsibility for user experience. A conversation with different perspectives on the data product space.
46:52

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Shifting priorities in data products, prioritizing use cases and business value first, leads to delivering business advantage.
  • Organizational shift required for data products, with a designated owner or manager and closer alignment with business requirements.

Deep dives

Data Products for Dummies: 4 Key Insights

1. Shifting Priorities: Traditionally, data platforms and management took precedence over use cases, but data products prioritize business value and use cases first, followed by infrastructure and data management. This change in approach is key to delivering business advantage.

2. Business Value of Data Products: Data products provide specific, defined business value and are built with product management concepts. They are self-contained, reusable artifacts with version control and are easy to discover. By investing in data products, organizations can measure their productivity and determine the value of their investment in data.

3. Organizational Shift: Data products require an organizational shift where a data product owner or manager takes responsibility for the product's lifecycle and addresses any defects. This shift includes moving the data product team closer to the domain to better understand business requirements and ensure accountability.

4. Adoption and Metrics: Low adoption has been a challenge in the data product space. However, by aligning data products with user needs, providing self-service data infrastructure, and measuring metrics, businesses can drive higher adoption rates. The Chief Data Officer (CDO) plays a crucial role in measuring the success of data products and quantifying their economic value.

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