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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]
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.
According to Mike et al.: [1]
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:
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]
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]
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]
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]
If the developed solution proves its feasibility and profitability during this phase, it will be implemented and operationalized. [2] [4]
Creative problem-solving (CPS) is the mental process of searching for an original and previously unknown solution to a problem. To qualify, the solution must be novel and reached independently. The creative problem-solving process was originally developed by Alex Osborn and Sid Parnes. Creative problem solving (CPS) is a way of using creativity to develop new ideas and solutions to problems. The process is based on separating divergent and convergent thinking styles, so that one can focus their mind on creating at the first stage, and then evaluating at the second stage.
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.
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.
In industry, product lifecycle management (PLM) is the process of managing the entire lifecycle of a product from its inception through the engineering, design and manufacture, as well as the service and disposal of manufactured products. PLM integrates people, data, processes, and business systems and provides a product information backbone for companies and their extended enterprises.
Product design is the process of creating new products for sale businesses to its customers. It involves the generation and development of ideas through a systematic process that leads to the creation of innovative products. Thus, it is a major aspect of new product development.
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.
Requirements management is the process of documenting, analyzing, tracing, prioritizing and agreeing on requirements and then controlling change and communicating to relevant stakeholders. It is a continuous process throughout a project. A requirement is a capability to which a project outcome should conform.
Business analysis is a professional discipline focused on identifying business needs and determining solutions to business problems. Solutions may include a software-systems development component, process improvements, or organizational changes, and may involve extensive analysis, strategic planning and policy development. A person dedicated to carrying out these tasks within an organization is called a business analyst or BA.
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.
Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.
Training Analysis is the process of identifying the gap in employee training and related training needs.
Decision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. Its application provides a framework for best practices in organizational decision-making and processes for applying computational technologies such as machine learning, natural language processing, reasoning, and semantics at scale. The basic idea is that decisions are based on our understanding of how actions lead to outcomes. Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains.
The Design of Business: Why Design Thinking is the Next Competitive Advantage is a 2009 book by Roger Martin, Dean of the University of Toronto's Rotman School of Management. In the book, Martin describes the concept of design thinking, and how companies can incorporate it into their organizational structure for long term innovation and results.
The term is used for two different things:
Systematic Inventive Thinking (SIT) is a thinking method developed in Israel in the mid-1990s. Derived from Genrich Altshuller's TRIZ engineering discipline, SIT is a practical approach to creativity, innovation and problem solving, which has become a well known methodology for innovation. At the heart of SIT's method is one core idea adopted from Genrich Altshuller's TRIZ which is also known as Theory of Inventive Problem Solving (TIPS): that inventive solutions share common patterns. Focusing not on what makes inventive solutions different – but on what they share in common – is core to SIT's approach.
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, scientific visualization, 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.
Design culture is an organizational culture focused on approaches that improve customer experiences through design. In every firm, the design culture is of significance as it allows the company to understand users and their needs. Integration of design culture in any organisation aims at creating experiences that add value to their respective users. In general, design culture entails undertaking design as the forefront of every operation in the organisation, from strategy formulation to execution. Every organisation is responsible for ensuring a healthy design culture through the application of numerous strategies. For instance, an organisation should provide a platform that allows every stakeholder to engage in design recesses. Consequently, employees across board need to incorporate design thinking, which is associated with innovation and critical thinking.
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