Data thinking
 | 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 emphasizes the integration of data science into the design process. It incorporates elements 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 both user experience and data analytics, including the collection and interpretation of data.
This framework aims to improve data literacy within organizations and individuals, promoting the use of data to make informed decisions. By adopting data thinking, organizations can develop products that are more closely aligned with user needs through evidence-based insights. It also allows individuals to derive conclusions grounded in data, potentially reducing the influence of external biases.[2][3][4][5]
Major Components of Data Thinking
[edit]According to Mike et al.:[1]
- Data thinking is the understanding that a solution to a real-life problem should not be based only on data and algorithms, but also on the domain knowledge-driven rules that govern them.
- Data thinking asks whether the data offer a good representation of the real-life situation. It also addresses how data were collected and asks, "Can the data collection be improved?".
- Data thinking is the understanding that data are not just numbers to be stored in an adequate data structure, but that these numbers have a meaning that derives from the domain knowledge.
- Data thinking is understanding that any process or calculation performed on the data should preserve the meaning of the relevant knowledge domain.
- Data thinking analyses the data not only logically but also statistically, using visualizations and statistical methods to find patterns as well as irregular phenomena.
- Data thinking is understanding that problem abstraction is domain-depended, and generalization is subject to biases and variance in the data.
- Data thinking is understanding that lab testing is not enough, and that real-life implementation will always encounter unexpected data and situations, and so improving the models and the solution for a given problem is a continuous process that includes, among other activities, constant and iterative monitoring and data collection.
- Data thinking is the creative process of understanding the problem from different levels of abstraction, which always involves being stack.
- Data thinking involves understanding that the analysis of data could have multiple meanings and that it requires proper thinking to have valid representations.
- Data thinking is a process of creating the most suitable way to analyse the data, input it, and make conclusions about it.
- Data thinking is the creative way to assess the problem, analysing all available data using modern technology and get the wanted solution 5 times quicker, than it was before.
- Data thinking is the process of processing available data in a meaningful way while also not excluding the impact of the missing data.
Major Phases of Data Thinking
[edit]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
[edit]During this phase, the broader context of digital strategy is analysed. 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
[edit]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][8]
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.[9]
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.[10]
Prototyping / Proof of Concept
[edit]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.[11] 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
[edit]Solution feasibility and profitability are proven during the data thinking process. Cost benefits analysis and business case calculation are commonly applied during this step.[12]
Implementation and Improvement
[edit]If the developed solution proves its feasibility and profitability during this phase, it will be implemented and operationalized.[2][4]
See also
[edit]References
[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.
- ^ a b "Data Thinking - Mit neuer Innovationsmethode zum datengetriebenen Unternehmen" [With new innovation methods to the data-driven company] (in German).
- ^ a b c "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 KI-innovation]. Handelskraft (in German).
- ^ a b 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. ISSN 0169-023X. S2CID 53096729.
- ^ "design-thinking-process-and-principles".
- ^ Fomenko, Elena; Mattgey, Annette (2020-05-12). "Was macht eigentlich ... ein Data Thinker?". W & V. German.
- ^ 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.
- ^ 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.
- ^ "Data-Thinking – das Potenzial von Daten richtig nutzen". t3n Magazin (in German). 2018-09-08. Retrieved 2020-07-08.