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. [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

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

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

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

Related Research Articles

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Public policy is an institutionalized proposal or a decided set of elements like laws, regulations, guidelines, and actions to solve or address relevant and real-world problems, guided by a conception and often implemented by programs. The implementation of public policy is known as public administration. Public policy can be considered to be the sum of a government's direct and indirect activities and has been conceptualized in a variety of ways.

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<span class="mw-page-title-main">Decision support system</span> Information systems supporting business or organizational decision-making activities

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<span class="mw-page-title-main">Requirements analysis</span> Engineering process

In systems engineering and software engineering, requirements analysis focuses on the tasks that determine the needs or conditions to meet the new or altered product or project, taking account of the possibly conflicting requirements of the various stakeholders, analyzing, documenting, validating and managing software or system requirements.

<span class="mw-page-title-main">Systems development life cycle</span> Systems engineering terms

In systems engineering, information systems and software engineering, the systems development life cycle (SDLC), also referred to as the application development life cycle, is a process for planning, creating, testing, and deploying an information system. The SDLC concept applies to a range of hardware and software configurations, as a system can be composed of hardware only, software only, or a combination of both. There are usually six stages in this cycle: requirement analysis, design, development and testing, implementation, documentation, and evaluation.

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<span class="mw-page-title-main">Data science</span> Interdisciplinary field of study on deriving knowledge and insights from data

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.

User research focuses on understanding user behaviors, needs and motivations through interviews, surveys, usability evaluations and other forms of feedback methodologies. It is used to understand how people interact with products and evaluate whether design solutions meet their needs. This field of research aims at improving the user experience (UX) of products, services, or processes by incorporating experimental and observational research methods to guide the design, development, and refinement of a product. User research is used to improve a multitude of products like websites, mobile phones, medical devices, banking, government services and many more. It is an iterative process that can be used at anytime during product development and is a core part of user-centered design.

References

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  11. 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)
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