Data science is a genre of science that has taken up a lot of space in the global industry; with widespread outreach into global sectors and businesses. It is an interdisciplinary field with a seamless amalgamation of mathematics, computer science, statistics, and beyond. this governs the data culture that we experience today. Talking of data science technology; this has got a lot to do with how businesses run and function today to reap better returns for the future.
Not-so-latest addition is the term Design thinking! Coined by David Kelley, a Stanford professor and founder of the design agency IDEO, in the 1990s. Let us explore the world of design thinking and how it impacts the data science industry.
About Design Thinking
Design thinking is a problem-solving process that can be used in data science to help create innovative solutions that meet users’ needs. It is a non-linear iterative process that helps teams understand users, redefine problems, and create solutions. It is a human-centered approach that encourages creativity and collaboration. Design thinking can help control risk in data analytics projects by getting users involved in the design process. This can save time and money, and help ensure users’ needs are met.
Design Thinking-Mechanism
Data-enhanced design thinking includes qualitative activities that support insightful and impactful design solutions. These activities may vary depending on the nature of the projects. However, the core activities include:
- Empathize
This involves conducting ethnographic interviews with relatively small groups of users to gain a deep understanding of the user journey, and the pain points, motivations, and consequent behaviors which are relevant to our design problem.
- Define
This process synthesizes all the information we have learned so far by identifying patterns that either confirm or confound our expectations.
- Ideate
This stage involves brainstorming potential solutions to the user’s pain points. Guided by the principle of ‘no idea is a bad idea’; this encourages uninhibited ideation and generates solutions that may not be immediately obvious or typical given the problems we have identified.
- Prototype
This is when the concepts come to life. visual designers sketch mockup screens and product features. Interaction designers build user journeys, hero flows, and interactive prototypes. These prototypes undergo extensive iteration to ensure that they have fully addressed the pain points uncovered during research.
- Test
This stage involves getting feedback from real users about how the design solutions address their needs. This stage may uncover mistakes or areas of opportunities to evolve further.
Design Thinking in Data Science
Utilizing machine learning in design thinking and data science strategies yields results that can be listed below:
- Prioritization
Ensures alignment with strategic business initiatives to secure organizational buy-in and focus.
- Early Stakeholder Engagement
Understanding stakeholder needs yields more relevant and actionable use cases.
- Informed Decision making
Uses well-defined KPIs and metrics to improve decision-making effectiveness.
- Optimized outcomes
Provides prescriptive recommendations to optimize business processes and outcomes.
- Predictive insights
AI models yield predictive insights to optimize high-priority use cases.
- Continuous learning
A learning-based user experience incorporating feedback from AI models and human users.
- Ethical foundation
Ensures that AI models are used responsibly and ethically.
Dual Benefits for Business Growth
From understanding the business initiatives and desired outcomes; these help to build a robust set of Key Performance Indicators (KPIs) and metrics that will eventually be integrated into the AI utility function that guides the operations and performance of the AI models. Further on, stakeholder involvement is critical for adoption. By being involved in the definition and design phase, stakeholders are more likely to adopt the analytics results; leading to improved decision-making, and achieving the business initiatives. There are core benefits that businesses can reap from design thinking and data analytics:
- Customer-centric innovation
- Improved decision-making
- Enhanced user experience
Data science and design thinking bring together a combination of the analytical power of data science with the human-centered approach of design thinking, businesses can gain a deeper understanding of customer needs and develop innovative solutions that effectively drive growth and market success. Personalized marketing, website optimization, and product development are some of the popular applications that this duo can bring out.
Design thinking is streamed to humanize data science. The sheer ability to leverage new powerful technologies such as the Internet of Things, Human-Machine Interfaces, Cyber-physical systems, and Artificial Intelligence; requires organizational connections to align, adopt, and monetize them. Organizations are required to master the disciplines of data science technology, design thinking, and economics to identify and capture new sources of customer and market value creation. This shall help in re-engineering existing business models based upon these new sources of customer and market value creation.
