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The Emergence of Product Analytics, and Its Critical Place in the World of AI
- The role of data scientists in some companies has evolved to focus on machine-learning engineering for AI, while others concentrate on product analytics, emphasizing statistical methods to extract insights for business decisions.
- Product analysts play a key role in identifying trends, valuable features, and cleaning data early in the modeling process, reducing risks and informing opportunity identification before model selection.
- Product analytics complements machine learning by supporting AI development through prototype modeling, providing proofs of concept and showcasing the predictive value of features.
- By conducting experimentation, product analysts assess model performance, effects on customers, and overall viability, enabling customer segmentation and tracking effectiveness of strategies.
- Product analytics requires effective communication skills to explain technical concepts to non-technical stakeholders, communicate data stories, and bridge the gap between business technology and consumers.
- As AI usage grows, the importance of communicating its workings and implications also rises, making product analysts instrumental in clarifying data and AI interactions to stakeholders and the public.
- Starting every data project with a clear question and reason for exploration is crucial to avoid solutions in search of problems, with product analysts specializing in customer insights to guide teams in making informed and ethical decisions.
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