- What is Data?
- What is Design?
- What is Design Thinking?
- Importance of Design Thinking in today’s world
- What is Data Science?
- Importance of Data Science in today’s world
- Common Areas
- Stages of Design thinking process
- Stages of Data Science process
- Overlap of the both processes.
What is Data?
Data is a collection of numbers and information about an object, people or group of people, which is collected through observation. It can be a combination of numbers, words, measurements, observations or just descriptions of things. Data can be quantitative or qualitative, and can reveal a lot about a subject. It can tell us whether a particular project is successful, moving in the right direction, whether going ahead with it is feasible or not.
What is Design?
According to designer and blogger Koos Looijesteijn, “A design is a plan to make something new for people that they perceive as beneficial.” Merriam-Webster Dictionary calls design as an activity “to create, fashion, execute, or construct according to plan”. So, designing is not just about how something looks. It means a plan to make something, and the execution of this plan. In other words, it includes processes of identifying objectives and finding ways of achieving them.
What is Design Thinking?
Design Thinking, on the other hand. Is a mechanism to deal with sticky problems innovatively by utilizing a designer’s tools. These sticky problems are often not defined well, and hence it is all the more important to find innovative solutions. According to Tim Brown, the CEO of IDEO, a global design and innovation company, 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.”
Importance of Design Thinking in Today’s World
In the complex, ever-changing world of today, innovation and creativity are essential tools to solve business problems. The earlier ‘one size fits all’ strategy is not going to work anymore. Owing to the digital revolution, the customers are aware of their choices. Countless brands are struggling to grab their attention. The one that solves their problem in the most customised and innovative fashion will hit the jackpot. An article in Harvard Business Review mentioned a seven-year study which looked at 50 projects from a number of sectors, including business, health care, and social services. It was seen that design thinking could potentially do for innovation what Total Quality Management (TQM) did for manufacturing. It could release people’s creative energies, improve processes, and get them more committed.
Over the past few years, Design Thinking has been adopted by leading brands like Apple, Google and Samsung, while it is now being taught at some well-known universities of the world. The launch of Apple’s iPhone and Macbook Air, for instance, are both great examples of excellent design thinking. Steve Jobs in his famous keynote spoke about how they arrived at a new device that was both a phone, an email device and an internet communicator. This came from a deep understanding of the challenges customers were facing back then, and finding innovative solutions to it. The Macbook Air came at a time when laptops were thick, heavy machines which were difficult to carry and use. Apple solved it with the Macbook Air, which became the thinnest laptop in the world that offered true portability.
What is Data Science?
Data Science is a field of expertise that utilises a number of skills like programming knowledge, domain expertise and mathematical proficiency to get meaningful insights out of data.
The objective of data science is to use these disciplines to extract actionable insights from data. Data science applies machine learning algorithms on data to produce Artificial Intelligence (AI) to perform tasks that require human intelligence.
Importance of Data Science in Today’s World
Increasingly, companies are realising the importance of data science, AI and machine learning. In the age of big data, companies need to equip themselves with data science capabilities in order to stay relevant. For example, sophisticated modern email services constantly use machine learning to execute spam filtering. As the user tags some emails as spam, the machine learns from the patterns and starts filtering the email by tagging spam emails in large numbers.
Data science and design thinking have some common areas. When they come together to solve a problem, it can be a potent combination. In fact, for the data-driven, complex world of today, this integration may be a necessity. But the focus must be on deriving and driving new sources of value and actionable outcomes. To make this potent combo of data science and design thinking more actionable, one must begin with an end in mind.
Stages of the design thinking process
- Empathise – This stage is about a close observation of the consumer. As the old saying goes, it’s important to step into the shoes of the customer and walk around in it. While observing the consumer, the designer needs to develop empathy so that they could understand the deeper issues that sometimes they themselves couldn’t express
- Define – The observations of the consumer and their needs are summarised in a definition of the problem. It is important to define clearly the problem that is being solved. If there is a proper understanding of what challenges the customer is facing and their struggles, this shouldn’t be too difficult.
- Ideate – The next step is to brainstorm ideas as to how to solve the problem. This could be done with a team or even by oneself. One can throw around different ideas and evaluate whether or not they will be able to solve the problem. This process can output some ideas that could be considered for this process.
- Prototype – At the prototype stage, one comes with a model of the idea that is being considered for solving the problem. The idea is to come up with a version of the solution, to see how it is accepted by the consumer or whether they are actually solving the problem.
- Test – Once the prototypes are handed to the consumer, the job is to observe how the users react to it. This is the stage where one collects feedback on the work/ solution.
This flow of stages in design thinking is not a linear process. After undergoing the final stage of testing, it is possible that the user night go back to one of the earlier stages.
Data science process
- Ask – The first stage is to ask for the goal statement. What is the goal of the particular activity? What are the opportunities? Once the goal is designated and opportunities identified, one can create hypotheses to be tested, on that basis.
- Collect – This stage is about identifying, collecting and preparing the data. Once the preliminary stage is taken care of, data collection becomes significant. Once the data has been found, it has to be prepared for further study and analyses.
- Process – The collected data needs to be explored, validated and processed. At this stage, the data is validated and tested. If it requires improving, that requires to be taken into consideration.
- Analysis – A further level of optimisation happens at this level. Data analysis and observations are monitored closely at all levels.
- Insights – Finally, after processing and observing, the final insights are ready to be delivered. This is the last stage of the data science process, aimed at delivering insights from the data.
Overlap of both the process
1. Empathy – While Design Thinking requires one to understand and observe the consumer and their struggles, Data science is enriched because of the insights offered by the process. As the quantitative research is informed by qualitative understanding, this exercise ensures that no bias creeps in the process.
2. Define – Design Thinking defines the problem. Data science evaluates the quality of the hypothesis. If the data acquisition and analysis has been systematic, the hypothesis can be tested against quantitative evidence.
3. Ideate – As per design thinking, this stage focuses on brainstorming and analysing different ideas. Data science uses quantitative understanding to smartly cluster ideas, and can help make a decision when there are competing opinions about which approach would work better.
4. Prototype – This stage is another overlap between design thinking and data science. It is important to have data science inform the process of prototyping. It is important that the prototyped solution solves the problems, and in the correct order. Data science can also help reduce redundancies.
5. Test – At this stage, design thinking requires one to observe how the user reacts to the prototype. Data science allows the mechanism to compare the designs in an unbiased way. From the micro level to the macro, this can be done at different levels of design fidelity.
FAQs For Data Science and Design Thinking
What is meant by Design Thinking?
Design thinking is a method that is used, to try and eradicate user-centric problems that prove to be sticky problems. Design thinking leads to innovation that helps make user journeys and their experience better as well as business success.
Why is Design Thinking so Important?
Design thinking helps businesses to constantly improve and innovate their products, services, and processes. By using design thinking, businesses can come up with new and improved solutions to problems that they may be facing. Additionally, design thinking can help businesses to better understand their customers and what they want or need. This understanding can then be used to create products and services that are more likely to be successful.
How do Companies use Design Thinking?
Design thinking helps companies innovate as well as focus on the users’ views. By doing, so each step is organized to help deliver good experiences and to establish good engagement between customers and the platforms.