Mastercard Product VP on Building and Monetizing Customer-Centric Data Products

What do product leaders need to consider when building customer-centric data products? In this webinar, Mastercard Product VP Vartika Rawat will explore the fundamentals of data as a product, delve into crucial considerations for successful development, and uncover the significance of understanding your end customers’ needs. From navigating challenges and mitigating risks to unlocking monetization strategies, gain actionable insights to thrive in the data-driven landscape. Additionally, learn about invaluable perspectives on ethical and regulatory dimensions to ensure responsible and compliant data product development.

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Show Notes:

1. Building customer-centric data products requires understanding user needs and goals to create holistic solutions.
2. Evaluating data sources, ownership, quality, and readiness is crucial for successful data product development.
3. User experience (UX) design is important for data products to drive adoption, engagement, and build trust in insights.
4. Partnering with privacy, design, and governance teams is key to embedding trust and transparency throughout the product lifecycle.
5. Understanding user personas informs how data products deliver value, experience, architecture, and delivery model.
6. Starting with user outcomes helps connect insights to actions and identify holistic solutions.
7. Leveraging strategic data can provide competitive advantages like increased revenue and profitability.
8. Direct and indirect monetization strategies include subscriptions, APIs, solutions, and insights as a service.
9. Continuous monitoring from data to insights ensures quality, performance, and avoids overinflating product worth.
10. Iterative development focuses on delivering clear value through the product.
11. Aligning capabilities and expectations with stakeholders sets realistic roadmaps.
12. Data quality is foundational but standards vary by use case impact and decision-making.
13. Transparency in how insights are computed builds trust in solutions.
14. Education reduces consent friction by explaining data value to customers.
15. Embedding governance manages privacy and compliance risks.
16. Accumulating insights over time can reduce product value by drifting from goals.
17. Actionability improves adoption by enabling outcomes through products and integrations.
18. Infrastructure, tools, and budgets impact AI implementation timelines.
19. Bias removal ensures fair, ethical models for regulated domains.
20. User privacy and consent are critical issues requiring transparency.