While Data scientists and analysts have learnt to put data to work for them by understanding patterns, behaviours etc through various analytics tools, designers are yet to fully embrace this habit by data scientists. In this Invision post,Roger Huang describes and prescribes the steps that can tie data and design together. Here’s the excerpt that we found compelling the most:


By embracing the metrics your business cares about and contextualizing why good UX design matters to those metrics, you not only make it easier for business leaders to justify putting more resources into UX, you also start prioritizing what initiatives make the most sense. Speaking to how better user engagement ties to company goals help frame your work in a context that’s familiar to non-designers.


How you can apply this to your day-to-day: Ask which metrics matter to your company or client. Deeply examine how your design goals will help improve those metrics and the company’s bottom line. Then reconcile your work with the impact it drives on the bottom line.


Key resources: UX designers working in data-driven organizations should start by reading Dave McClure’s ARRRR Startup Metrics framework to understand how companies measure growth at various stages. The book Lean Analytics is a must-read to understand how good use of data works to track impact.


Experiment, measure and repeat


You’ll often find yourself debating between many design treatments. How do you make a decision?


Good news: You don’t always have to decide—you can let users tell you. And to do that, you’ll want to take a page from the data scientist’s approach of experimentation.


Data scientists tend to think in terms of experiments—they’re usually pretty structured about stating their hypothesis for an experiment, what they wish to measure and learn, and how they want to run the experiment.


This mentality might already be part of your workflow, but you should employ some tricks data scientists use to make sure they’re on track with their experiments. State and record your hypotheses. For instance, “I believe that version 2 will lead to 20% more users making a purchase than version one.”


Next, build the minimum viable version of each design.


Then put your design in front of users and track their behavior to learn if your hypothesis is true. Make sure to consider the concept of statistical significance (whether you have enough observations to make a statistically accurate prediction).


How you can apply this to your day-to-day


Get familiar with deciding when to incorporate data in your decisions, and when data doesn’t matter. Learn how to use tools like Optimizely, Mixpanel, or Google Analytics that can easily help you see how your UX choices impact user behavior.


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