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 combines data science with the design process. It combines principles 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 user experience and data analytics, including the collection and interpretation of data.
This framework aims to apply data literacy and inform decision-making through data-driven insights. By adopting data thinking, organizations can more closely align their products with user needs, derive evidence-based conclusions, and proactively address potential biases in their analyses, ensuring more reliable and user-centric outcomes.[2][3][4][5]
Major Components of Data Thinking
editAccording to Mike et al.:[1]
- Data thinking involves understanding that solutions require both data-driven and domain knowledge-driven rules.
- It evaluates whether data accurately represent real-life scenarios and improving data collection where necessary.
- The framework highlights the importance of preserving domain-specific meaning during data analysis.
- Data thinking incorporates statistical and logical analysis to identify patterns and irregularities.
- It includes the importance of testing solutions in real-life contexts and iteratively improving models based on new data.
- The process requires evaluating problems from multiple abstraction levels and understanding the potential for biases in generalizations.
Major Phases of Data Thinking
editStrategic Context and Risk Analysis
editAnalyzing the broader digital strategy and assessing risks and opportunities is necessary before starting projects. Techniques like cool-hunting, trend analysis, and scenario planning can be used.[6]
Ideation and Exploration
editIn this phase, focus areas are identified, and use cases are developed by integrating organizational goals, user needs, and data requirements. Design thinking methods like personas and customer journey mapping are applied.[7]
Prototyping
editA proof of concept is created to test feasibility and refine solutions through iterative evaluation.[8]
Implementation and Monitoring
editSolutions are operationalized and monitored for performance and continual improvement.[2][4]
See also
editReferences
edit- ^ a b 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.
- ^ a b "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).
- ^ a b "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 AI innovation]. Handelskraft (in German).
- ^ Schnakenburg, Igor; Kuhn, Steffen. "Data Thinking: Daten schnell produktiv nutzen können". LÜNENDONK-Magazin "Künstliche Intelligenz" (in German). 05/2020: 42–46.
- ^ Nalchigar, Soroosh; Yu, Eric (2018-09-01). "Business-driven data analytics: A conceptual modeling framework". Data & Knowledge Engineering. 117: 359–372. doi:10.1016/j.datak.2018.04.006. S2CID 53096729.
- ^ Brown, Tim; Wyatt, Jocelyn (2010-07-01). "Design Thinking for Social Innovation". Development Outreach. 12 (1): 29–43. doi:10.1596/1020-797X_12_1_29.