Design Thinking Process in Data Science
By: Sandhya Reddy
Tags: Design Thinking, Data Visualization, Data, Data Science, Analytics

Design Thinking along with Analytics and Data Science, can make companies open for new chances of Business Growth, and strengthen long-term transformation abilities.

Data and business go hand in hand together, for business growth. But let us see how the design thinking process is useful in data science.

The Data Science process is done by following steps, Framing the problem, retrieving the data, exploring the data, performing deep analysis and delivering the results.

While Framing the problem, we often get stuck with what data is there in mind, based on which we can frame calculated questions. For exploring the data, one should have sound knowledge of the testing questions, and creativity by which we can get desired results. For more Additional info at Data Science Online Training.

In the data science process research, there are few challenges like composing and asking the correct questions of the data and delivering executable outcomes, for the related users.

Therefore, these challenges in Data Science can be resolved with the help of the Design Thinking process.

Design Thinking

It is a repetitive process, by which we try to understand the client, challenges that they are facing and rethink the issues. So that we can identify other methods and solutions that might not be clear immediately at the first level of understanding.

Design Thinking process gives a result-oriented method, for solving problems. It is a method of thinking and functioning, as well as a group of direct methods.

How Does Design Thinking Process help?

Data Science uses the technical ways to data, so as to get important confirmations that can be applied to create fresh ideas. Usually, data scientists create a concept, examine it, and save the outcomes.

These outcomes lead to the latest one and the procedure is repeated. Designers use different ways and use processes that form the issues and improve learning about users to enhance deep results, and create a solution to examine and learn more.

Observe the similarities between both, Design Thinking and Data Science. Design Thinking process makes sure to design results, that are practical and workable.

In the Design Thinking process, user and their requirements are the basic point of improving the new product and results and ask queries like, what is the issue the user has, for whom we are creating, how to increase the production and gain flexibility.

This along with Data Science from the starting phases can lead to the correct mixture of understanding, technical practicality, business executable, and user requirements.

Phases in Design Thinking:

The Design Thinking process maintains a direct, client-oriented method to solve issues that lead to new ideas, and new ideas can lead to distinguish and competitive use.

The Design Thinking process follows the flow of understanding, exploring and make them happen.

There are six phases in the Design Thinking Process. They are

Empathize — Research to understand your users like who are your users, what are their issues, behaviors and what they want to achieve at an emotional level apart from your product. Define — Know users’ hidden needs and problems. Specify where the problem is or what we have to solve, which we found in the Empathize phase. This results in the Challenge declaration. Ideate — Think about various ways to solve, the issues. Plan innovative ideas that can solve the user requirements, and problems that are declared in the Define phase.

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Prototype — With the ideas, create models. Compare both effects or results and adaptability of our ideas, with the help of responses on our models. Test — Confirm the prototype with clients. Ask users questions like, does the solutions meet user’s needs and how users feel and perform their works? Execute — Finally, convert the vision into the results. Make sure that your result has taken place, and reaches the lives of your clients.

Even though the process seems to be straight, but every phase can be cyclic and repetitive. Usually, it is required to work in a phase different times to get the desired result.

The wide implementation of Design thinking, integrated with the increasing abilities of Data Science and analytics leads to business growth, to improve everlasting creative abilities and production.

There is no doubt, this approach is very much useful for Data Science. In this article, I have explained the significance of the Design Thinking process in Data Science. Follow my articles to get more updates on Data Science.