Data thinking is a

product design Product design as a verb is to create a new product to be sold by a business to its customers. A very broad coefficient and effective generation and development of ideas through a process that leads to new products. Thus, it is a major aspect of n ...

framework with a particular emphasis on

data science Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a br ...

. It integrates elements of

computational thinking Computational thinking (CT) is the mental skill to apply concepts, methods, problem solving techniques, and logic reasoning, derived from computing and computer science, to solve problems in all areas, including our daily lives. In education, CT ...

statistical thinking Statistical thinking is one of the tools for process analysis. Statistical thinking relates processes and statistics, and is based on the following principles: * All work occurs in a system of interconnected processes. * Variation exists in all p ...

, and domain thinking. In the context of product development, data thinking is a framework to explore, design, develop and validate data-driven solutions. Data thinking combines

with

design thinking Design thinking refers to the set of Cognition, cognitive, strategic and practical procedures used by designers in the process of Design, designing, and to the body of knowledge that has been developed about how people reason when engaging with des ...

and therefore, the focus of this approach includes user experience as well as data analytics and data collection. The term was created by Mario Faria and Rogerio Panigassi in 2013 when in a book about data science,

data analytics Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It ...

data management Data management comprises all disciplines related to handling data as a valuable resource. Concept The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to r ...

, and how data practitioners were able to achieve their goals.

Major Components of Data Thinking

According to Mike et al.: * Data thinking is the understanding that a solution to a real-life problem should not be based only on data and

algorithms In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing c ...

, 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 In computer science, a data structure is a data organization, management, and storage format that is usually chosen for efficient access to data. More precisely, a data structure is a collection of data values, the relationships among them, a ...

, 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 analyzes 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.

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 Trend analysis is the widespread practice of collecting information and attempting to spot a pattern. In some fields of study, the term has more formally defined meanings. Although trend analysis is often used to predict future events, it could be ...

technology forecasting Technology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Researchers create technology forecasts based on past experience and current technological developments. Like other ...

and scenario planning/analysis as well as internal data capability assessments are the major techniques that are typically applied at this stage.

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. 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,

persona A persona (plural personae or personas), depending on the context, is the public image of one's personality, the social role that one adopts, or simply a fictional Character (arts), character. The word derives from Latin, where it originally ref ...

customer journey Customer experience (CX) is a totality of cognitive, affective, sensory, and behavioral consumer responses during all stages of the consumption process including pre-purchase, consumption, and post-purchase stages. Pine and Gilmore described the ...

) are broadly applied at this stage. 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. To scope data and the technological foundation of the solution, practices from cross-industry standard processes for data mining (

CRISP-DM Cross-industry standard process for data mining, known as CRISP-DM,Shearer C., ''The CRISP-DM model: the new blueprint for data mining'', J Data Warehousing (2000); 5:13—22. is an open standard process model that describes common approaches use ...

) are typically used at this stage.

Prototyping / Proof of Concept

During the previous stages, the major concept of the data solution was developed. Now, a

proof of concept Proof of concept (POC or PoC), also known as proof of principle, is a realization of a certain method or idea in order to demonstrate its feasibility, or a demonstration in principle with the aim of verifying that some concept or theory has prac ...

is conducted to check the solution's feasibility. This stage also includes testing, evaluation, iteration, and refinement. Prototyping design principles are also combined during this phase with process models that are applied in data science projects (e.g. CRISP-DM).

Measuring business impact

Solution feasibility and profitability are proven during the data thinking process. Cost benefits analysis and

business case A business case captures the reasoning for initiating a project or task. It is often presented in a well-structured written document, but may also come in the form of a short verbal agreement or presentation. The logic of the business case is that, ...

calculation are commonly applied during this step.

Implementation and Improvement

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

References

{{reflist Data management Product development Applied data mining Innovation