Data-driven design can be defined as a decision-making approach to the design process that heavily relies on collected data about users’ behavior and attitude. It’s a process of developing or improving a product based on activities that can be measured.
Why data-driven design?
There are quite a few success stories on how data-driven UX methods significantly contribute to the growth of a business.
Data-driven design is not only critical during the user research or user testing phase but while performing various design activities or tasks in the design projects.
Designers can validate their instinctive choices with evidence. Also, PMs can better understand their teams’ needs and motivations from the data collected and adjust the design function accordingly.
Data-Driven Designs in Cubyts -
1. Understand design function and business goals
Cubyts helps to find insights about design function through various frameworks and use this knowledge to make design processes better. These insights help PMs get a better understanding of various design processes and design systems. A set of metrics will be derived from those insights to match the business goals.
2. Maturity assessment at Project level and Org level
Cubyts, as a design maturity platform, will continuously guide & recommend insights within or across the organization to make better design decisions through various UX integrations such as A/B testing & multivariate testing and various survey frameworks.
In addition, Cubyts also generates usage and performance data based on activities performed on Cubyts and artifacts created. This is how Cubyts assess the design maturity at the project level and organizational level.
3. Data-Driven Recommendations
Cubyts measures every task or action by designers, adding to the organizational wisdom that can be analyzed and made available to designers/PMs to make design-driven decisions. Cubyts eventually starts recommending organizations as per the data collected about the key insights and what systems or processes yield better results than others. Every activity, task, unit action gets captured and uses as insights to build a guided recommender system.
With digital ecosystems growing in complexity and the overwhelming opportunity to define and refine how these ecosystems are experienced by customers and teams alike, the need to design better is greater than ever, and what better way to make designs effective than using Cubyts to build such ecosystems through data-driven designs.