Data thinking

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, focusing on user experience and data analytics, including the collection and interpretation of data.

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.

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.
• It evaluates whether data accurately represent 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.
• It includes the importance of 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 necessary before starting projects. Techniques like coolhunting, trend analysis, and scenario planning can be used.

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.

Prototyping

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

Implementation and monitoring

Solutions are operationalized and monitored for performance and continual improvement.

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.

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

• "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
• Analytical thinking
• Computational thinking