Data-Informed vs. Data-Driven Decision Making in Design
Digital experiences have increasing benefited from data collection and analysis from real users. Even user testing is a form of data driven design–data from customers is used to evaluate how successfully a design meets the user’s goals. Data can be incredibly useful for digital experiences, guiding teams toward making better decisions, such as deciding which features to prioritize, which trade-offs to make and driving empathy for how users behave.
The idea behind gathering data is that the more data there is, the better the design can be. But despite the benefits of data-driven design, there are still some pitfalls and times when it may be better to be data-informed rather than data-driven.
Designs that are purely reliant on data for any type of change may be harmed by bias in the type or amount of data collected, skewed analysis of the collected data, or simply choosing the wrong metric to measure your design.
The idea behind data-informed design is to for designers to use data as a supporting tool, or just one of the factors to consider as the design team drives major design changes.
When to be Data-Driven
Use a data-driven approach when you are trying to grow or optimize a specific area, such as signup percentages, increasing views or clicks, etc. Being data-driven makes more sense when you have a lot of traffic or users to collect the data, you can collect the data quickly, and there are clear metrics for what’s good versus bad in the current experience of your product.
When to be Data Informed
Use a data-informed approach when you are making design decisions that evaluate high level user motivations, expectations, perceptions, or emotions. Being data-informed makes sense in contexts where you are focusing on strategic decisions such as focusing on long term retention rates. It takes the vision of a design team looking farther ahead rather than using short term metrics to make the experience successful over time.
Relying on data alone makes it difficult to justify larger, more costly changes which may be necessary to innovate on a product or lead a higher level UX strategy. There is a time and a place for each type of decision making, so use data and the right research methods to support your designs. This will help you iterate toward the long term goal of your users having the most optimal experience.