Data Thinking is a framework that integrates
data science
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 from potentially noisy, stru ...
with the
design process
A design is the concept or proposal for an object, process, or system. The word ''design'' refers to something that is or has been intentionally created by a thinking agent, and is sometimes used to refer to the inherent nature of something ...
. It combines computational thinking, statistical thinking, and domain-specific knowledge to guide the development of data-driven solutions in product development. The framework is used to explore, design, develop, and validate solutions,
with a focus on
user experience
User experience (UX) is how a user interacts with and experiences a product, system or service. It includes a person's perceptions of utility, ease of use, and efficiency. Improving user experience is important to most companies, designers, a ...
and
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, which also falls under and directly relates to the umbrella term, data sci ...
, including data collection and interpretation
The framework aims to apply
data literacy and inform decision-making through data-driven insights.
Major components
According to "Computational thinking in the era of data science":
* Data thinking involves understanding that solutions require both data-driven and domain-knowledge-driven rules.
* Data thinking evaluates whether data accurately represents real-life scenarios and improves data collection where necessary.
* The framework highlights the importance of preserving domain-specific meaning during data analysis.
* Data thinking incorporates statistical and logical analysis to identify patterns and irregularities.
* Data thinking involves testing solutions in real-life contexts and iteratively improving models based on new data.
* The process requires evaluating problems from multiple abstraction levels and understanding the potential for biases in generalizations.
Major phases
Strategic context and risk analysis
Analyzing the broader digital strategy and assessing risks and opportunities is a common step before beginning a project. Techniques like
coolhunting
Coolhunting is a neologism coined in the early 1990s referring to a new kind of marketing where professionals make observations and predictions based on changes of new or existing "cool" cultural fads and trends. Coolhunting is also referred to ...
, trend analysis, and scenario planning can be used to assist with this.
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
persona
A persona (plural personae or personas) is a strategic mask of identity in public, the public image of one's personality, the social role that one adopts, or simply a fictional Character (arts), character. It is also considered "an intermediary ...
s and customer journey mapping, are applied.
Prototyping
A
proof of concept
A proof of concept (POC or PoC), also known as proof of principle, is an inchoate realization of a certain idea or method in order to demonstrate its feasibility or viability. A proof of concept is usually small and may or may not be complete ...
is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance.
Implementation and monitoring
Solutions are tested and monitored for performance and continual improvement.
Implementing Data Thinking
The following resources explain more about data thinking and its applications:
* "Data Thinking: Framework for data-based solutions" by StackFuel
* "What is Data Thinking? A modern approach to designing a data strategy" by Mantel Group
* "Data Science Thinking" by SpringerLink
These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes.
See also
*
Statistical thinking
Statistical thinking is a tool for process analysis of phenomena in relatively simple terms, while also providing a level of uncertainty surrounding it. It is worth nothing that "statistical thinking" is not the same as " quantitative literacy", a ...
*
Analytical thinking
*
Computational thinking
Computational thinking (CT) refers to the thought processes involved in formulating problems so their solutions can be represented as computational steps and algorithms. In education, CT is a set of problem-solving methods that involve expressin ...
References
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Data management
Product development
Applied data mining
Innovation