The importance of data-driven Design

Introduction — Why a product that only looks pretty will not work?

A common myth that Digital Product Designers are often trying to take down, is that Design is only used as an aesthetic touch to a Digital product and that Designers only focus on making a product look good.
The truth is, Product Design -or UX Design-, aims to make a product work more than just make it look pretty. UX Design is both an art and a science, and unlike art, the product that we build shouldn’t only be aesthetically and emotionally appealing but also be easy to use.

The goal of a Product Designer is to define and solve problems in the most efficient way, using a variety of tools beyond colors and fonts.
In order to create a user-friendly product, we must understand what works and what doesn’t, and a feature that looks good in our imagination will most likely end up being confusing and not suitable to be used by the user, at least in the long run.

Our decisions shall thus not only be influenced by aesthetics but also by data and information, and this is why we need to talk about data-driven design.

What does data-driven design mean?

Data-driven design refers to a design that is endorsed by the findings you get from data. It illustrates the process of building or improving a product -or a feature-, based on criteria that can be measured.

Why is data-driven design important?

Data-driven design backs up your research and assumptions and helps you to stay on the right track. It gives you more precise user-based data, pain points, and opportunities and adds more objectivity to your future prototypes.

One of the biggest mantras of UX Design, that you probably heard of if you work as a Product Designer is “You are not your user”.
Indeed, assumptions are dangerous when building a product, since you cannot possibly know what users want without doing any kind of user research. The Norman Nielsen Group explains really well why something they call “The false-consensus effect” can totally jeopardize your work.

Basically, when you try to design without having data to back up your decisions, you are trying to play darts blindfolded. You will end up failing at creating a good design for the people who use your products and waste money later on, changing your initial design choices which failed.

Using data efficiently in design has been proven to improve business results directly. A study by McKinsey that interprets the Business value of Design through their own Index (McKinsey Design Index) showed that companies that made it into the top quartile of MDI scorers, were able to double their revenue growth and shareholder returns over those of their counterparts within the same industry.
Besides, another research by MIT’s Center for Digital Business stated that “companies in the top third of their industry in the use of data-driven decision making were, on average 5% more productive and 6% more profitable than their competitors.”

How do we proceed?

One really good thing is that at any stage of your design process, data can help you identify problems, back up your decisions, and find solutions. However, you do need to know which data you need and how to use them properly when you design, since data-driven Design doesn’t mean gathering as much data as you can but thoroughly choosing data that can help you get real insights about user behavior.
It’s important that you come prepared with user needs and business goals prior to gathering your data, and for that, you will need to put a real effort into User Research and select a set of metrics that match your business goals.

Another common myth is that that data only stands for numbers. Of course, quantitative data is the foundation of data-driven design, but it is not enough to make proper decisions. When opting for data-driven Design, it is better to combine both quantitative and qualitative methods, because quantitative data will give you a hint about the actions users take when using your product, but qualitative data will help you understand why they take these actions and most importantly, how they feel when taking these actions.

Many tools exist to collect both qualitative and quantitative data from your users:

  • Analytics

How do I Analyze Data?

After you have collected data, you have covered one aspect of the data-driven design process. Quantitative data, such as design and analytics, is vital, but qualitative data that you collect from user interviews, is also crucial.

Quantitative data gives designers an idea of what is happening on a website or an app, but qualitative data is needed to clarify why users do what they do. Understanding why users behave in certain ways is a large part of UX theory and the psychology of design.

Once both qualitative and quantitative data have been collected, Designers will analyze trends in the data as well as anomalies. The latter often alerts a product team about potential problems that users might face while using the product, which could become a trend when traffic increases.

The easiest way to have a clear overview, especially with big datasets, is to create data visualizations. Simple charts and graphs can do the job and will help you to analyze the available data. Google Analytics is a good example of how analytics programs visualize data with charts and graphs instead of raw data. It will also be easier to present it to your team this way.

As a Product Designer, you (or a dedicated person from your team) should analyze data on a regular basis, as new data is collected. All the changes going on within the product (design, content, SEO algorithms, and other developments can affect UX and user behavior. It’s very important that Designers analyze this data to improve constantly the product and make new iterations to match the evolution of the product.

Presenting Data to Stakeholders

You will often have to convince your team and your hierarchy about the improvements you want to do within your product, especially if you work in a large corporation. Again, displaying data in a visual way is crucial to present information.

Your safest bet is to build a slide deck with charts and graphs. This will make the difference when you try to get your manager (or a client) to approve a project instead of facing resistance during your work process.

Conclusion

Embracing the data-driven design process is a mandatory skill for Product Designers. Going deep into the user research and user testing process, and learning how to use data analytics, will give Product Designers solid additional materials to back up their ideas, and will help them to build the best product possible confidently.

I am curious to know about your best practices to integrate data into your working process on a daily basis!

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