Data thinking

Data thinking is a product design framework that combines data science with the design process. It draws on computational thinking, statistical thinking, and domain-specific knowledge to steer the creation of data-driven solutions. Data thinking guides the exploration, design, development, and validation of data-driven solutions in product development. By combining data science with design thinking, ^[1] data thinking emphasizes 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. ^{[2] [3] [4] [5]}

Major components

According to "Computational thinking in the era of data science": ^[1]

• Data thinking involves understanding that solutions require both data-driven and domain-knowledge-driven rules.
• Data thinking evaluates whether data accurately represents real-life scenarios and improves 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.
• Data thinking involves 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

Strategic context and risk analysis

Analyzing the broader digital strategy and assessing risks and opportunities is a common step before beginning a project. Techniques like coolhunting, trend analysis, and scenario planning can be used to assist with this. ^[6]

Ideation and exploration

In this phase, focus areas are identified, and use cases are developed by integrating organizational goals, user needs, and data requirements. Design thinking methods, such as personas and customer journey mapping, are applied. ^[7]

Prototyping

A proof of concept is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance. ^[8]

Implementation and monitoring

Solutions are tested and monitored for performance and continual improvement. ^{[2] [4]}

Implementing Data Thinking

The following resources explain more about data thinking and its applications:

• "Data Thinking: Framework for data-based solutions" by StackFuel ^[9]
• "What is Data Thinking? A modern approach to designing a data strategy" by Mantel Group ^[10]
• "Data Science Thinking" by SpringerLink ^[11]

These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes.

See also

• Statistical thinking
• Analytical thinking
• Computational thinking

References

1. 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.
2. "Why do companies need Data Thinking?". 2020-07-02.
3. "Data Thinking - Mit neuer Innovationsmethode zum datengetriebenen Unternehmen" [With new innovation methods to the data-driven company] (in German).
4. "Data Thinking: A guide to success in the digital age".
5. Herrera, Sara (2019-02-21). "Data-Thinking als Werkzeug für KI-Innovation" [Data Thinking as a tool for AI innovation]. Handelskraft (in German).
6. Schnakenburg, Igor; Kuhn, Steffen. "Data Thinking: Daten schnell produktiv nutzen können". LÜNENDONK-Magazin "Künstliche Intelligenz" (in German). 05/2020: 42–46.
7. 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.
8. 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. hdl: 10986/6068 .
9. "Blog article".
10. "Blog article".
11. "Paper Link".