Data Science and Design: An Intimate Connection in Tech-driven Market
Know about the blooming connection between data science and design
Apparently, data science and design seem to be poles apart in terms of their nature and purposes. But if the very recent trends are to be followed, both are coming closer. Thus, taking the global crisis head-on, IDEO, a global design consultancy firm, advocates the “power and potential of design in a global crisis”. In such effort, the tools, actions and inspiration for navigating a changing world are sourced from exploring and calibrating design in newer ways. In a related development, data scientists are reflecting on incorporating the power and potential of design in developing human-centred data science.
The major thrust in such development is that rather than calling for techniques for easy-read data the idea is to look for intense visualization of design-based data to improve the existing situations and performance. ‘How to do it’ is the major issue in such development, rather than an urge to go for simplification of data. In an important conference last year, Rise 2020, the point was explicated by designers Takashi Wickes and Lisa Nash of IDEO. They came up with a four step-design process marked by inspiration, research and synthesis, prototyping, and communication.
Inspiration: As the first stage of the process it concerns the exploration of places not usually considered likely ones. Most important, it avoids the usual mainstream practice of standard-based competitor analysis or market study and calls for studying the scenarios in firms from other industries and other sectors, which are indulging in problem-solving. Data science comes to the aid of this process by permitting designers to venture into analogous spaces and blindspots. Be it the client’s own data or data publicly available, as in social media, are a great resource to kick off. It helps to find out a number of previously thought issues like some unexpected ways of using a product by people or different perceptions from that of brand positioning.
Research and Synthesis: With the human-centred approach at the base of the overall process research relates to finding design opportunity by understanding what the potential user say, think and feel. It requires much greater involvement on the part of researchers as they are to exert empathy with the users. In a major way, this stage tends to reveal gaps between people’s stated intentions and their actual behaviour. At this stage data science helps researchers to acquire an objective view of qualitative data by lending statistical analysis to rank variables. This leads to a much-needed synthesis for data scientists.
Prototyping: This stage becomes important as a follow-up of adequate research on understanding the target groups’ pain points and potential solutions. As the third stage, it requires designing a tangible product, its field testing and also iteration in the face of user feedback. Interestingly, the IDEO designers go for a low-tech approach here, even using preschool classroom materials like pipe cleaners, modeling clay, paste and so forth. Also, much importance is given to visuals, such as graphs and charts, as the ideal medium to communicate ideas.
Communication: This stage involves giving a final shape to the concept and accordingly tuning the presentation of the idea to the client. True to the human-centered approach at this stage communication does not only refer to the use of charts, reports and PowerPoint presentations. It claims to make efforts to communicate the design to make the stakeholders ‘feel’ it, and not merely understand it. The measures may range from using high-tech apps to playing the role of a particular user. Data science has a very important role for data scientists in this regard because its techniques enable designers to make use of real and/or simulated data for devising real-life prototypes. Simulated data enhance the ‘feel’ that is being emphasized in the process.
So be it in the retail sector or be it in the medical sector or be it elsewhere, the bond between data science and design is emerging with a lot of force for data scientists— with the human factor at the centre.