Data thinking

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Data thinking is a product design framework with a particular emphasis on data science. It integrates elements of computational thinking, statistical thinking, and domain thinking. [1] In the context of product development, data thinking is a framework to explore, design, develop and validate data-driven solutions. Data thinking combines data science with design thinking and therefore, the focus of this approach includes user experience as well as data analytics and data collection. [2] [3] [4] [5]

Contents

Data thinking is a mindset that promotes data literacy and encourages both organizations and individuals to make data-driven decisions. By incorporating data thinking into the product development process, organizations can create more user-centered products that are informed by data and insights, rather than intuition. Meanwhile, individuals can make data-based conclusions and avoid external bias.

Major Components of Data Thinking

According to Mike et al.: [1]

Major Phases of Data Thinking

Even though no standardized process for data thinking yet exists, the major phases of the process are similar in many publications and could be summarized as follows:

Clarification of the Strategic Context and definition of data-driven risks and opportunities focus areas

During this phase, the broader context of digital strategy is analyzed. Before starting with a concrete project, it is essential to understand how the new data and AI-driven technologies are affecting the business landscape and the implications this has on the future of an organization. Trend analysis / technology forecasting and scenario planning/analysis as well as internal data capability assessments are the major techniques that are typically applied at this stage. [6] [4]

Ideation/Exploration

The result of the earlier stage is a definition of the focus areas which are either the most promising or are at the highest risks for or due to data-driven transformation. At the Ideation/exploration phase, the concrete use cases are defined for the selected focus areas. For successful Ideation, it is important to combine information about organizational (business) goals, internal/external use needs, data and infrastructure needs as well as domain knowledge about the latest data-driven technologies and trends. [7] [3]

Design thinking principles in the context of data thinking can be interpreted as follows: when developing data-driven ideas, it is crucial to consider the intersection of technical feasibility, business impact, and data availability. Typical instruments of design thinking (e.g. user research, personas, customer journey) are broadly applied at this stage.[ citation needed ]

In addition to user needs, customer and strategic needs must also be considered here. Data needs, data availability analysis, and research on the AI technologies suitable for the solution are essential parts of the development process. [8]

To scope data and the technological foundation of the solution, practices from cross-industry standard processes for data mining (CRISP-DM) are typically used at this stage. [9]

Prototyping / Proof of Concept

During the previous stages, the major concept of the data solution was developed. Now, a proof of concept is conducted to check the solution's feasibility. This stage also includes testing, evaluation, iteration, and refinement. [10] Prototyping design principles are also combined during this phase with process models that are applied in data science projects (e.g. CRISP-DM). [6]

Measuring business impact

Solution feasibility and profitability are proven during the data thinking process. Cost benefits analysis and business case calculation are commonly applied during this step. [11]

Implementation and Improvement

If the developed solution proves its feasibility and profitability during this phase, it will be implemented and operationalized. [2] [4]

See also

Related Research Articles

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References

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  2. 1 2 "Why do companies need Data Thinking?". 2020-07-02.
  3. 1 2 "Data Thinking - Mit neuer Innovationsmethode zum datengetriebenen Unternehmen" [With new innovation methods to the data-driven company] (in German).
  4. 1 2 3 "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 KI-innovation]. Handelskraft (in German).
  6. 1 2 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. ISSN   0169-023X. S2CID   53096729.
  8. Fomenko, Elena; Mattgey, Annette (2020-05-12). "Was macht eigentlich … ein Data Thinker?". W & V. German.
  9. Marbán, Óscar; Mariscal, Gonzalo; Menasalvas, Ernestina; Segovia, Javier (2007). Yin, Hujun; Tino, Peter; Corchado, Emilio; Byrne, Will; Yao, Xin (eds.). "An Engineering Approach to Data Mining Projects". Intelligent Data Engineering and Automated Learning - IDEAL 2007. Lecture Notes in Computer Science. 4881. Berlin, Heidelberg: Springer: 578–588. doi:10.1007/978-3-540-77226-2_59. ISBN   978-3-540-77226-2.
  10. 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 . ISSN   1020-797X.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  11. "Data-Thinking – das Potenzial von Daten richtig nutzen". t3n Magazin (in German). 2018-09-08. Retrieved 2020-07-08.