What is data-driven vs. data-informed design?
Overview
A huge amount of data is generated daily, from different spheres of life, by different means and in different forms. Analyzing this data has given us an edge in making better decisions and building business models which meet goals, profits, and also solve the users’ needs.
The need for data in UX design
In design, to solve users’ problems, it is essential to work with data and not just assumptions. Data provides information and insight about the users’ behavior, attitude, pain points, and goals. Deeper analysis of this data can help in making a concrete decision, building a usable product, and leaving users with great experience on the product. Hence, the need for data-driven design and data-informed design.
Types of design approaches
Data-driven design and data-informed design are the two approaches in which UX design has been greatly improved as it employs data to solve users’ needs, provide a great user experience, and make decisions for a good business model. However, there is a difference between the two design approaches.
Data-driven design
Data-driven design is a design that is fully backed up by data and helps the UX designer understand the target users better, based on the data provided. This approach makes use of both quantitative and qualitative sources of data. These include the UX research method: surveys, usability testing, behavior flows, tracking analytics on web or mobile app, competitor analysis, etc. Thus, it answers the question “What” and “How”.
Data-informed design
In data-informed design, the focus is mainly on qualitative data, which informs the decision of the designer or the product owner. In this approach, data is used as a reference when a design decision is to be made. It answers the question “Why”.
The data approach to be used for a particular project depends on the nature of the project. For instance, a data-informed design approach is best employed when introducing new changes that will solve users’ problems. It will allow you to measure the impact of your changes on your users as well as on the product, and also discover the problems the users are facing in using your product. The data-driven design approach is best for carrying out performance optimization and quantitative metrics of your product. For example, when a product team wants to reduce the bounce rate on a landing page, quantitative metrics such as average time on a page or time-to-load can help them understand when their users face roadblocks.
What is data-driven design?
The data-driven design approach uses data as its focal point. This is to say that data dictates the whole move of the design rather than the designer. The risk of letting data dictate the whole move is that it stifles great design and can sometimes lead to a fragmented mess of the user experience. In a data-driven design approach, the work could be made easier with less stress since the data handles the workload of the designer in making design decisions.
This approach creates an environment where designers are continually challenged to prove every design decision, even the microscopic ones with BIG data. When the design decision cannot be backed up by data, it could slow down the design process greatly. Also, the data-driven approach focuses on metrics, which are a mere reflection of the product strategy that have already been put in place. A consequence of this approach is that most times, the design team unwittingly becomes beholden to the data and they also do not consider the wider product vision, since data has no notion of design cohesion.
A/B testing, multivariate testing, and analytics play a significant role in achieving a data-driven design.
A/B testing
The A/B testing technique involves showing two or more variants of a design to users at random to find out which variant performs better. In other words, it compares two versions of a webpage or app against each other to determine which one performs better. The difference in the two versions is typically a change in a specific element such as change in the color of a call-to-action button. A/B testing can be performed by splitting the users into two groups, say a 50-50 split when the user base is small or taking a sample size from a larger user base and have the different groups use the different versions of the product.
Running an A/B test that directly compares a variation against a current experience lets you ask focused questions about changes to your website or app and then collect data about the impact of that change.
Multivariate testing
Analytics
Generally, analytics provides quantitative metrics such as unique page/ screen view, how many pages viewed per single session, average time on a page, and bounce rate for websites. Hence, this helps to measure the user’s activity in a product. It also points out what part of the product is more valuable to the users and how exactly they use the product.
Analytics helps understand what user behavior is driving the metric, thus could be a first step to creating the big picture view of the user journey/ user flow for your product.
What is data-informed design?
Being data-informed means acknowledging the fact that you only have a small subset of the information that you need to build a successful product.
As earlier stated, this approach answers the question “Why”. For instance, “why do users do what they do?” Thus, this approach reveals users’ behavior patterns and figures out why the users behave in certain ways. It also helps to meet the users’ needs by making strategic, fundamental design decisions, and creating innovative products.
Hence, the data-informed design approach enhances an environment where designers can be innovative and creative. Qualitative user research plays a significant role in data-informed approach and can be achieved through contextual inquiries and user flow analysis.
Contextual inquiry
In this research method, the UX researcher observes how a user interacts with a product in their environment, e.g. the user workspace.The UX researcher may ask the user questions like, “Why did you tap on this button?” Therefore the UX researcher gets to understand human behavior, habits, and expectations better.
User flow analysis
User flow refers to the path the users take from the entrance point of the application/website, which could be a sign up page or homepage to the exit point of the product, which could be a check out page. Generally, UX designers have a specific flow they map out for the users but this may greatly differ from what the users expects. Therefore to map out user flows, it is best to ask users to complete a specific task, observe and analyze how they complete the task.
Conclusion
It is important to leverage data the right way, be it for strategic or tactical issues. Thinking of data as something that supports your design decisions, rather than thinking of it as numbers, helps to build a good product. A balance between data-driven and data-informed approaches in UX design results to a good product and achieving business goals. It is best to have a vision of what to achieve, then use the data to validate and navigate-through to the vision, thus helping you to make decisions and executions in a data-informed way.
