 | This article contains wording that promotes the subject in a subjective manner without imparting real information. (May 2021) |
Data thinking is a product design framework that emphasizes the integration of data science into the design process. It incorporates elements from computational thinking, statistical thinking, and domain-specific knowledge to guide the development of data-driven solutions. In product development, data thinking is used to explore, design, develop, and validate solutions based on data. It merges data science with design thinking,[1] focusing on both user experience and data analytics, including the collection and interpretation of data.
This framework aims to improve data literacy within organizations and individuals, promoting the use of data to make informed decisions. By adopting data thinking, organizations can develop products that are more closely aligned with user needs through evidence-based insights. It also allows individuals to derive conclusions grounded in data, potentially reducing the influence of external biases.[2][3][4][5]
- ^ Mike, Koby; Ragonis, Noa; Rosenberg-Kima, Rinat B.; Hazzan, Orit (2022-07-21). "Computational thinking in the era of data science". Communications of the ACM. 65 (8): 33–35. doi:10.1145/3545109. ISSN 0001-0782. S2CID 250926599.
- ^ "Why do companies need Data Thinking?". 2020-07-02.
- ^ "Data Thinking - Mit neuer Innovationsmethode zum datengetriebenen Unternehmen" [With new innovation methods to the data-driven company] (in German).
- ^ "Data Thinking: A guide to success in the digital age".
- ^ Herrera, Sara (2019-02-21). "Data-Thinking als Werkzeug für KI-Innovation" [Data Thinking as a tool for KI-innovation]. Handelskraft (in German).