Design Thinking and Machine Learning

Design thinking is defined as a human centric process that builds upon the deep understanding of the end users such as behaviours or tendencies to generate ideas or prototypes and to put out your innovative solution to the world. In future, every business should integrate design thinking and machine learning in order to take it to the next level.

Machine learning is a method of data analysis. Machine learning is a machine centred approach. In machine learning, there are algorithms we can use to make machine learn through data iteratively and make it able to find hidden insights from the data. So that, machine can itself take decisions without being explicitly programmed what to do in each case.

Design thinking is a process for creative problem solving and as AI is getting more and more advance , future business leaders should must know about how to use design thinking with machine learning in order to create better and effective solutions. Machines are better at processing data.

“Design thinking is a human-centred approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.”
— Tim Brown, CEO of IDEO

Phases of Design Thinking:

Phases of Machine Learning:

2. Synthesize

3. Ideate

4. Tuning

5. Validate

Now, I am going to explain each of these phases in detail..

Step 1. Empathize and Analyze

The objective of ‘Empathize and Analyze is to understand your users. Who is my user? What matters to this person? In design thinking, it refers to understand and capture the user’s pain points, and trying to know as much as possible about user. In machine learning, it refers to capture the user’s key decisions and to find out the variables and metrics that might be useful for making better predictions.

Step 2. Define and Synthesize

In design thinking, it refers to creation of a Point Of View(POV) based on user’s needs and insights. What are their needs? What is the working environment of user? It basically refers to design, document and validate your understanding of user’s needs. In machine learning, it refers to the combination of separate elements in order to create a whole new.

Step 3. Ideate and Ideate

This step is all about ideation. In design thinking, it refers to brainstorm as many solutions as possible and then prioritize those solutions on the basis of business feasibility and customer values and implementation. In machine learning, it refers to playing with small sample data by applying various analytical models and algorithms in order to see what types of insights are there in the data.

Step 4. Prototype and Tune

In design thinking, it involves providing the working prototype to the user. This steps involves the study of user’s interactions with the product to see what works and where the users are facing problems. Identify what additional designs can be added in product in order to enhance user experience. In machine learning, identify where analytics insights are needed and what additional data you can capture. You have to explore opportunities to provide real time insights to user.

Step 5. Test and Validate

In design thinking, it refers to the monitoring of usage to determine the effectiveness of the product or solution. There should be a continuous improvement of the product where feedback can be quickly used to improve users experience. In machine learning, try to avoid overfitting your model. This step is also the start of the continuous improvement process from the user experience and analytic model tuning perspectives.

Now I hope that you understand why it is important for a business leader to understand both design thinking and machine learning together.

Let me know your thoughts regarding it in comments section.

Thank you…

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