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Design Thinking meets Data Science


The ethos of design thinking is the human-centered approach to innovation. The design thinking process takes a designer's perspective that focuses on the user desirability incorporating feasibility and viability elements.


By the early 2000s, due to the escalating growth of internet access and computing power, many organizations had been made possible to collect and analyze a large amount of structured and unstructured data. Furthermore, more advanced and complex analytic techniques like predictive analytics with machine learning were within reach.


Data science requires programming skills to collect, manipulate, and analyze digital data, and the statistics skills to select the type of analysis and algorithms needed to address business or user questions and gain meaningful insights.


Design thinking is the mindset that requires skills to empathize with people's emotional needs to gain deep insights into their problems that need solutions, generate lots of ideas, and create prototype solutions, test, learn and repeat.


Design thinking and data science might not seem like a natural connection; one is associated with unquantifiable and complex human behaviors while the other is a rigorous, quantitative discipline.


I am fortunate to have the opportunity to learn data science and lead a data science team during my short stint with Azendian Solutions. I could see how both disciplines contrast, and yet where they overlap and how they can work together.


The principles of design thinking provide data science with a structured approach to analyze data generated from ambiguous and complex challenges. A human-centered approach can also help ensure the resulting insights are actionable and valuable for target users.


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