Data thinking

Last updated

Data thinking is a product design framework that combines data science with the design process. It integrates principles from computational thinking, statistical thinking, and domain-specific knowledge to steer the creation of data-driven solutions. Data thinking guides the exploration, design, development, and validation of data-driven solutions in product development. It merges data science with design thinking, [1] focusing on user experience and data analytics, including the collection and interpretation of data.

Contents

This framework aims to apply data literacy and inform decision-making through data-driven insights. By adopting data thinking, organizations can more closely align their products with user needs, derive evidence-based conclusions, and proactively address potential biases in their analyses. [2] [3] [4] [5]

Major components

According to “Computational thinking in the era of data science” [1]

Major phases

Strategic context and risk analysis

Analyzing the broader digital strategy and assessing risks and opportunities is necessary before starting projects. Techniques like coolhunting, trend analysis, and scenario planning can be used. [6]

Ideation and exploration

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 personas and customer journey mapping are applied. [7]

Prototyping

A proof of concept is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance. [8]

Implementation and monitoring

Solutions are operationalized and monitored for performance and continual improvement. [2] [4]

Implementing Data Thinking

Implementing Data Thinking enables organizations to systematically identify user-oriented use cases that create actual added value. It combines data science with the proven innovation method of Design Thinking, focusing on the creative development of user-oriented data science use cases with high business potential. [9]

For a comprehensive understanding of Data Thinking and its applications, you may refer to the following resources:

These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes.

See also

Related Research Articles

The rational unified process (RUP) is an iterative software development process framework created by the Rational Software Corporation, a division of IBM since 2003. RUP is not a single concrete prescriptive process, but rather an adaptable process framework, intended to be tailored by the development organizations and software project teams that will select the elements of the process that are appropriate for their needs. RUP is a specific implementation of the Unified Process.

<span class="mw-page-title-main">Usability</span> Capacity of a system for its users to perform tasks

Usability can be described as the capacity of a system to provide a condition for its users to perform the tasks safely, effectively, and efficiently while enjoying the experience. In software engineering, usability is the degree to which a software can be used by specified consumers to achieve quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use.

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

A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e., unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.

User-centered design (UCD) or user-driven development (UDD) is a framework of processes in which usability goals, user characteristics, environment, tasks and workflow of a product, service or process are given extensive attention at each stage of the design process. This attention includes testing which is conducted during each stage of design and development from the envisioned requirements, through pre-production models to post production. Testing is beneficial as it is often difficult for the designers of a product to understand the experiences of first-time users and each user's learning curve. UCD is based on the understanding of a user, their demands, priorities and experiences, and can lead to increased product usefulness and usability. UCD applies cognitive science principles to create intuitive, efficient products by understanding users' mental processes, behaviors, and needs.

<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.

Human-centered computing (HCC) studies the design, development, and deployment of mixed-initiative human-computer systems. It is emerged from the convergence of multiple disciplines that are concerned both with understanding human beings and with the design of computational artifacts. Human-centered computing is closely related to human-computer interaction and information science. Human-centered computing is usually concerned with systems and practices of technology use while human-computer interaction is more focused on ergonomics and the usability of computing artifacts and information science is focused on practices surrounding the collection, manipulation, and use of information.

Kansei engineering aims at the development or improvement of products and services by translating the customer's psychological feelings and needs into the domain of product design. It was founded by Mitsuo Nagamachi, professor emeritus of Hiroshima University. Kansei engineering parametrically links the customer's emotional responses to the properties and characteristics of a product or service. In consequence, products can be designed to bring forward the intended feeling.

Object-oriented analysis and design (OOAD) is a technical approach for analyzing and designing an application, system, or business by applying object-oriented programming, as well as using visual modeling throughout the software development process to guide stakeholder communication and product quality.

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

The engineering design process, also known as the engineering method, is a common series of steps that engineers use in creating functional products and processes. The process is highly iterative – parts of the process often need to be repeated many times before another can be entered – though the part(s) that get iterated and the number of such cycles in any given project may vary.

<span class="mw-page-title-main">Outline of thought</span> Overview of and topical guide to thought

The following outline is provided as an overview of and topical guide to thought (thinking):

Design science research (DSR) is a research paradigm focusing on the development and validation of prescriptive knowledge in information science. Herbert Simon distinguished the natural sciences, concerned with explaining how things are, from design sciences which are concerned with how things ought to be, that is, with devising artifacts to attain goals. Design science research methodology (DSRM) refers to the research methodologies associated with this paradigm. It spans the methodologies of several research disciplines, for example information technology, which offers specific guidelines for evaluation and iteration within research projects.

Work system has been used loosely in many areas. This article concerns its use in understanding IT-reliant systems in organizations. A notable use of the term occurred in 1977 in the first volume of MIS Quarterly in two articles by Bostrom and Heinen (1977). Later Sumner and Ryan (1994) used it to explain problems in the adoption of CASE. A number of socio-technical systems researchers such as Trist and Mumford also used the term occasionally, but seemed not to define it in detail. In contrast, the work system approach defines work system carefully and uses it as a basic analytical concept.

<span class="mw-page-title-main">Visual analytics</span>

Visual analytics is a multidisciplinary science and technology field that emerged from information visualization and scientific visualization. It focuses on how analytical reasoning can be facilitated by interactive visual interfaces.

In software engineering, a software development process or software development life cycle (SDLC) is a process of planning and managing software development. It typically involves dividing software development work into smaller, parallel, or sequential steps or sub-processes to improve design and/or product management. The methodology may include the pre-definition of specific deliverables and artifacts that are created and completed by a project team to develop or maintain an application.

Computational informatics is a subfield of informatics that emphasizes issues in the design of computing solutions rather than its underlying infrastructure. Computational informatics can also be interpreted as the use of computational methods in the information sciences.

In network theory, link analysis is a data-analysis technique used to evaluate relationships between nodes. Relationships may be identified among various types of nodes, including organizations, people and transactions. Link analysis has been used for investigation of criminal activity, computer security analysis, search engine optimization, market research, medical research, and art.

<span class="mw-page-title-main">Data science</span> Field of study to extract insights from data

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

Crowdsourcing software development or software crowdsourcing is an emerging area of software engineering. It is an open call for participation in any task of software development, including documentation, design, coding and testing. These tasks are normally conducted by either members of a software enterprise or people contracted by the enterprise. But in software crowdsourcing, all the tasks can be assigned to or are addressed by members of the general public. Individuals and teams may also participate in crowdsourcing contests.

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

  1. 1 2 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.
  2. 1 2 "Why do companies need Data Thinking?". 2020-07-02.
  3. "Data Thinking - Mit neuer Innovationsmethode zum datengetriebenen Unternehmen" [With new innovation methods to the data-driven company] (in German).
  4. 1 2 "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 AI innovation]. Handelskraft (in German).
  6. 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. S2CID   53096729.
  8. 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 .
  9. "Blog Article".
  10. "Blog article".
  11. "Blog article".
  12. "Paper Link".