How Product and Business Teams Should Participate in Building Data Metric Systems
The article explains how product and business teams should collaborate with data teams to build and promote data metric systems, emphasizing mutual empowerment, joint methodology, pilot testing, and scaling, while also announcing DataFun's 5‑year anniversary activities and upcoming big‑data and AI publications.
Q1: How should product or business colleagues participate in establishing a data metric system?
Product and business colleagues need a data‑empowerment mindset and should use the methodology of a data metric system to guide the data team in shaping a complete set of metrics, rather than merely raising questions. Building such a system requires joint effort; the data team must understand the business, and the business team must empower the data team, working together to create the metric system.
Q2: How can the data metric system be promoted within business departments?
If a mature data metric product does not exist, the data team should communicate with the business department to understand its pain points and rebuild a suitable metric system. If a product already exists, the metrics should be organized and shared with business staff, starting with small pilots to demonstrate value; successful pilots can then be expanded across the entire workflow.
— Xu Yao, Chief Technical Advisor at Zhiguang Technology, Guest Speaker
"How to Build a Good Data Metric System?" (source article)
DataFun is celebrating its 5‑year anniversary. From December to January, a series of technical articles will be published, focusing on hot topics in big data and artificial intelligence, featuring contributions from leading community experts who will summarize past technological evolution and forecast future trends.
On January 7, 2023, DataFunTalk will release the industry's first data‑intelligence knowledge map. Interested participants are invited to schedule the live broadcast.
DataFunTalk
Dedicated to sharing and discussing big data and AI technology applications, aiming to empower a million data scientists. Regularly hosts live tech talks and curates articles on big data, recommendation/search algorithms, advertising algorithms, NLP, intelligent risk control, autonomous driving, and machine learning/deep learning.
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