Page 306 - The Design Thinking Playbook

The first phase is understand: We develop in common an understanding of the problem. It is important that data scientists and design thinkers
already collaborate here. Some facts can be determined through the analysis of social media data, for instance, which has a broader base than
data gathered from traditional user surveys.

The observe & data mining phase is dedicated to the collection of “deep insights” and “deep learnings.” “Deep insights” arise from our
traditional observations of customers, users, extreme users, and the like. To obtain “deep learnings,” data must be collected, described, and an-
alyzed, which allows us to identify initial patterns and visualize them. We recommend discussing the insights from both observations together
and reviewing the next steps.

In the define phase, we combine the “deep insights” and “deep learnings.” A more exact point of view can be defined this way. The PoV de-
scribes the need a specific customer has and on what insights the need is based. The combination of both sides helps to get a better picture of
the customer. The stumbling block again here is the definition of the PoV. We already talked about it in Chapter 1.6. The hybrid approach yields
more “insights” that confirm the PoV but can also result in even bigger contradictions.

The aim of the ideate phase is to continue to generate as many ideas as possible, which are then summarized and evaluated by us. Several
ideas are available at the end of this phase that are used in the next steps.

Then comes the prototype & modeling experiments phase. In this phase, we develop prototypes and carry out experiments with models. Proto-
types make ideas palpable and easy to understand. As we know, a prototype can take many different forms; an algorithm, for instance, is also a
simple prototype. The insights from the data experiments are best represented with models in the form of visualizations; in data science, this is
Combined the best solution to make something tangible.
solution
In a test & proof of value phase, the prototypes are tested together with the potential user in order to learn from the feedback and adapt the
solutions to the needs of the customer. This includes models, visualizations, and dashboards from data science, which constitute the basis for
the prototype.

In the final phase, realize, we transfer an idea into an innovation! This includes integrating the models in operations. While data solutions
usually evolve from data science projects and design thinking develops products or services, in the hybrid process, combined solutions from data
science and design thinking can emerge. This can refer to a service-plus business model that presents added value as a result of the aggrega-
tion of various data sources; an example would be changes in the behavior of drivers to avoid traffic congestion in combination with an app.

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