Data thinking

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Data Thinking is a framework that integrates data science with the design process. It combines computational thinking, statistical thinking, and domain-specific knowledge to guide the development of data-driven solutions in product development. The framework is used to explore, design, develop, and validate solutions, [1] with a focus on user experience and data analytics, including data collection and interpretation

Contents

The 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]

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:

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

See also

References

  1. 1 2 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. 1 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. 1 2 "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".