Data-Informed Product Building. A series on how to build both… | Sequoia

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Original article

Key takeaway

For entrepreneurs: Data science and analytics are now essential for product development and growth, not optional extras.
For investors: Companies that strategically collect and analyze data are better positioned for rapid growth and success in today's fast-paced market.

Summary

Sequoia's "Data-Informed Product Building" series emphasizes the critical role of data science in modern product development. As products generate more data than ever, companies must leverage this information to drive decisions and growth. The series covers a wide range of topics, from measuring product health and analyzing metric changes to understanding engagement and building world-class data science teams. It aims to provide a comprehensive guide for entrepreneurs and product managers to create data-informed products and build successful data science organizations.

Insights

Data science has become a must-have for product development, not an afterthought.
Successful products evolve through stages, each requiring specific metrics and analysis.
Engagement is a key driver of product stickiness, retention, and growth.
Understanding and analyzing metric changes is crucial for product success.
Two-sided marketplaces require a unique approach to engagement analysis.
Building a data-informed company culture is as important as the technical aspects of data science.
There are six different types of data scientists, each with specific roles and skills.
Data organizations evolve alongside product growth, requiring adaptable infrastructure and teams.

Implications

Companies need to invest in building strong data science teams to remain competitive.
Product managers must become proficient in data analysis and interpretation.