包阅导读总结
1. 关键词：数据科学、产品管理、业务价值、用户体验、决策
2. 总结：本文强调数据科学产品管理的关键，包括明确目标客户、价值主张和战略业务价值，指出数据科学产品是解决方案一部分，要以解决客户问题和影响关键业务指标为优先，领先的产品经理应先考虑用户或客户体验的最终状态来构建产品，最终目标是为优质决策提供信息。
3.
– 数据科学产品管理的关键在于确定目标客户、价值主张和战略业务价值，其带来的业务价值常涉及决策、生产力和竞争优势。
– 数据科学产品包含数据可视化、预测模型和LLMs等。
– 如Amplitude的VP Ibrahim Bashir所说，AI只是手段而非产品，若不能解决客户问题或对关键业务指标无积极影响，不应优先。
– Noname Security的CISO Karl Mattson称，领先的产品经理应先考虑用户或客户体验的最终状态，要理解基于数据产品的决策本质，而非先纠结技术手段，最终目标是助力优质决策。
思维导图：
文章地址：https://www.infoworld.com/article/3479075/5-things-great-data-science-product-managers-do.html
文章来源：infoworld.com
作者：InfoWorld
发布时间：2024/8/12 9:00
语言：英文
总字数：1969字
预计阅读时间：8分钟
评分：81分
标签：产品管理,数据科学,人工智能/机器学习,协作,业务价值
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A key product management discipline is identifying an initiative’s target customer, value proposition, and strategic business value. Business value from data science initiatives often involves improved decision-making capabilities, increased productivity, and sustained competitive advantages. The data science product, including the product’s data visualizations, predictive models, and LLMs, are part of the solution.
“AI is the ‘how’ and not the product, so if using AI doesn’t solve a customer problem, you shouldn’t do it,” says Ibrahim Bashir, VP of product management at Amplitude. “If an AI-driven feature doesn’t positively impact a key business metric, such as time-to-value or retention, it shouldn’t be a priority.”
Karl Mattson, CISO at Noname Security, says that leading product managers first consider the end state of the user or customer experience and work backward to build the product. He says, “For data science initiatives, the end goal is informing quality decisions. We truly have to understand the nature of the decisions to be made on our data product and not be obsessed first over the technical how.”
