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What is data-driven design?

Many digital products fail to address the needs of their users. They become irrelevant soon after their launch. This happens because a lot of good ideas are not properly tested before they’re turned into a real-world solution. The best way to prevent irrelevance is to test and collect data.

Data-driven design enables teams and companies to better understand how users interact with their products. Data-based decisions give a clear framework to steer the development of products and parameters on which anyone can relate at any time.

This article helps you understand what data-driven design is and how you can apply it to your daily work.

What is data-driven design?

Simply phrased data-driven design is a way of designing backed up by findings from data. It’s the method of shaping your product road-map according to the evidence you collect directly from the users of your product.

Adopting a data-driven approach also means understanding the difference between intuition and facts. While our intuition is a great starting point to formulate hypotheses on how to improve the product, decisions need to be backed up by facts and supported by data.

It’s important to understand that the process of data-driven design is not supposed to hinder our creativity as designers, but rather to channel our creative energy into deliverables with a clear reason behind them.

Why data-driven design?

Data-driven design maximises the chances of success of products. It is the most effective way to build value for users. It also minimises the risks you’re taking with new products and it reduces the waste connected to implementing ineffective solutions.

A quote often used from Roger Pressman, a software engineer, is that “fixing a problem in development costs 10 times as much as fixing it in design, and 100 times as much if you’re trying to fix the problem in a product that’s already been released”.

So using data in the design phase has the triple effect of increasing your users’ satisfaction, increasing the ROI on the product and lowering the costs connected to waste.

What data should you collect?

A mistake often made is to collect too much data. An overflow of data is as unhelpful as no data at all. Not everything that can possibly be collected is equally important.

Be selective on what data are strictly needed to test your assumptions. This is easier when you start with the business goals you want to achieve. Once you have clearly defined your goals, then select the relevant metrics needed to measure success.

It is crucial to define the success indicators before starting to test. This avoids biased decisions, influenced by the early data you collect. When the test is over you should be able to clearly determine if the result confirms or rejects your working hypothesis.

Qualitative data and quantitative data

There is a common misconception which identifies data-centricity with crunching gargantuan amounts of data. On the contrary, especially at the beginning, you won’t have much data to analyse. What kind of data can you collect? The main difference is between quantitative data vs qualitative data.

Quantitative data are numerical and answer yes/no questions and who, what, when. How many users open your app on a certain hour of the day? What call-to-action receives the most clicks? Quantitative data give you numbers to answer these questions.

Qualitative data tell you why and how. Why do users perform an action instead of another? How do they perceive the value of your solution? Qualitative data provide you with context. They let you understand not only what happens, but also why it happens. They are perhaps the most valuable, but they also require interpretation from your side.

What data should you collect?

The data you collect are influenced by the kind of experiment you are conducting and by what is realistically available to you.

When choosing between qualitative data and quantitative data, there is no better or worse. Different methodologies respond to different needs and to create a user-centric product you must adopt a combination of both methods.

It is in general good to start by setting the scene with qualitative data to understand the bigger picture and then to dive deep into the what with quantitative data.

Don’t forget to document your processes

One of the advantages of a data-driven approach is to create a clear reference framework for decision-making.

If you work in a team or alone, it is equally important to log the experiments you run, the data you collect and the decisions you make. Doing this will allow you to have a process that you can control and verify.

Documenting doesn’t need to take hours! There are many great resources available to quickly document your experiments, like the experiment card from Strategyzer.

Crafting unique products by moving beyond best practices

Data-driven design is what enables the creation of unique products, by moving beyond best practices of UX and UI. While best practices are meant to be fairly universal and standard, your users have unique needs.

If you want your product to be unique, then you need to address your users needs like no other existing solution does. From this point of view data-centricity is the opposite of ‘best practices’. Only user-centric products can differentiate themselves from the competition and win their niche.

You can read more in this article on how good UX and UI influence the success of your product.

Data-driven design and Design Accelerator

Design Accelerator focuses on data-driven design to help you create unique, user-centric solutions.

Are you wondering how data-driven design can help you improve the experience of your users? Reach out for an orientation session.

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