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<h2 id="major-components">Major components</h2> <p>According to "Computational thinking in the era of data science":<a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a> </p> <ul><li>Data thinking involves understanding that solutions require both data-driven and domain-knowledge-driven rules.</li> <li>Data thinking evaluates whether data accurately represents real-life scenarios and improves data collection where necessary.</li> <li>The framework highlights the importance of preserving domain-specific meaning during data analysis.</li> <li>Data thinking incorporates statistical and logical analysis to identify patterns and irregularities.</li> <li>Data thinking involves testing solutions in real-life contexts and iteratively improving models based on new data.</li> <li>The process requires evaluating problems from multiple abstraction levels and understanding the potential for biases in generalizations.</li></ul> <h2 id="major-phases">Major phases</h2> <h3>Strategic context and risk analysis</h3> <p>Analyzing the broader digital strategy and assessing risks and opportunities is a common step before beginning a project. Techniques like <a href="/facts/Coolhunting/pLLobKSW">coolhunting</a>, trend analysis, and scenario planning can be used to assist with this.<a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a> </p> <h3>Ideation and exploration</h3> <p>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 <a href="/facts/Persona/lMy54oWx">personas</a> and customer journey mapping, are applied.<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a> </p> <h3>Prototyping</h3> <p>A <a href="/facts/Proof_of_concept/1YzUH8LJ">proof of concept</a> is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance.<a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a> </p> <h3>Implementation and monitoring</h3> <p>Solutions are tested and monitored for performance and continual improvement.<a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a><a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a> </p> <h2 id="implementing-data-thinking">Implementing Data Thinking</h2> <p>The following resources explain more about data thinking and its applications: </p> <ul><li>"Data Thinking: Framework for data-based solutions" by StackFuel<a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a></li> <li>"What is Data Thinking? A modern approach to designing a data strategy" by Mantel Group<a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a></li> <li>"Data Science Thinking" by SpringerLink<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a></li></ul> <p>These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes. </p> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/Statistical_thinking/HfokKQnX">Statistical thinking</a></li> <li><a href="/facts/Analytical_thinking/GWeB7aU3">Analytical thinking</a></li> <li><a href="/facts/Computational_thinking/oxJPyBys">Computational thinking</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>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 href="https://doi.org/10.1145/3545109" target="_blank">https://doi.org/10.1145/3545109</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>"Why do companies need Data Thinking?". 2020-07-02. <a href="https://detecon.com/en/knowledge/why-do-companies-need-data-thinking" target="_blank">https://detecon.com/en/knowledge/why-do-companies-need-data-thinking</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>"Data Thinking - Mit neuer Innovationsmethode zum datengetriebenen Unternehmen" [With new innovation methods to the data-driven company] (in German). <a href="https://www.etventure.de/data-thinking/" target="_blank">https://www.etventure.de/data-thinking/</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>"Data Thinking: A guide to success in the digital age". <a href="https://www.unbelievable-machine.com/en/data-thinking/" target="_blank">https://www.unbelievable-machine.com/en/data-thinking/</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Herrera, Sara (2019-02-21). "Data-Thinking als Werkzeug für KI-Innovation" [Data Thinking as a tool for AI innovation]. Handelskraft (in German). <a href="https://www.handelskraft.de/data-thinking-als-werkzeug-fuer-ki-innovation/2019/02/" target="_blank">https://www.handelskraft.de/data-thinking-als-werkzeug-fuer-ki-innovation/2019/02/</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>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 href="https://doi.org/10.1145/3545109" target="_blank">https://doi.org/10.1145/3545109</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>Schnakenburg, Igor; Kuhn, Steffen. "Data Thinking: Daten schnell produktiv nutzen können". LÜNENDONK-Magazin "Künstliche Intelligenz" (in German). 05/2020: 42–46. <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>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. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>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. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> <li id="fn:10"><p>"Why do companies need Data Thinking?". 2020-07-02. <a href="https://detecon.com/en/knowledge/why-do-companies-need-data-thinking" target="_blank">https://detecon.com/en/knowledge/why-do-companies-need-data-thinking</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></p></li> <li id="fn:11"><p>"Data Thinking: A guide to success in the digital age". <a href="https://www.unbelievable-machine.com/en/data-thinking/" target="_blank">https://www.unbelievable-machine.com/en/data-thinking/</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></p></li> <li id="fn:12"><p>"Blog article". <a href="https://stackfuel.com/en/blog/data-thinking-data-driven-solutions" target="_blank">https://stackfuel.com/en/blog/data-thinking-data-driven-solutions</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></p></li> <li id="fn:13"><p>"Blog article". <a href="https://mantelgroup.com.au/what-is-data-thinking-a-modern-approach-to-designing-a-data-strategy/" target="_blank">https://mantelgroup.com.au/what-is-data-thinking-a-modern-approach-to-designing-a-data-strategy/</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></p></li> <li id="fn:14"><p>"Paper Link". <a href="https://link.springer.com/chapter/10.1007/978-3-031-24758-3_3" target="_blank">https://link.springer.com/chapter/10.1007/978-3-031-24758-3_3</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></p></li> </ol>