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Your UX needs a data strategy

How to extract properly value from your product’s data

How many hours did you spend with charts without context for understanding the result of a change in your design? How many times did you squeeze your brain creating new user flows which outcome didn’t resonate with your colleagues? If the answer is a lot, perhaps your product needs a data strategy.

UX/UI design needs always a data strategy

As I explained in a previous Medium post, sometimes it is better to stop adding features or new designs to your product and start studying the behavior of your users

No matter if you are a UX/UI designer 🎨, a researcher 🔬or a data scientist 📊, when you work with a product’s data, it is easy to get lost between different sources, charts, tools and KPIs to measure. You will spend a lot of time studying your data and at the end of that, you will present your insights and the outcome will be, at least, ok.

If you don’t have a data strategy, you won’t generate value from your product’s data.

In this article I will write down the data strategy that we follow in BravoStudio. As Benjamin Franklin said, if you fail to plan you are planning to fail 🖋.

When you want to generate proper value from data, the very first step is to design a data strategy 💡.

A data strategy is a plan with a structure that goes through choosing the proper data sources to the correct communication of the information.

It seems easier that it is at first, but it’s critical to have a strategic data direction before handling the data. Having a strategic data direction has multiple benefits such as:

  • Goals and efforts alignment through teams.
  • Increase your confidence once you have to explain your insights to the rest of the team.
  • Remove misleading metrics and noise.

So how to know if your strategic data direction is bullet-proof? You must to have a clear answer to the following questions:

  • What’s the goal/s that we are aiming as a company or team?
  • What are the main hypotheses that I will test and how are they correlated to the goal/s?
  • Why will the information be useful to have? What am I trying to find?
  • Do we have the proper data for answering those questions?
  • How much effort do we need to put into answering those questions? Quick-wins vs long-term outcomes.

Spending some time answering these questions will provide you a clear direction from all the data mess to the concrete value that you want to extract.

When the data hides you the forest

Once the strategy data direction has been correctly defined, it’s time to handle some data.

The lack of a data strategy leads us to spend x5 or even x10 efforts on each task.

It is critical to follow the data strategy while you are working with your product’s data. It is not as easy as might sound though as we can get easily distracted by:

  • Spending unnecessary time processing data that it’s not related to the hypothesis that you want to study.
  • Trying to automate processes that you will run once or twice.
  • Generating an excessive number of KPIs or metrics that provides noise to the results.

That being said, the main issue that I found at this point is that, most of the time, people generate an unnecessary number of ramifications because they forget or don’t have hypotheses that they want to test and this is a big mistake.

Instead of looking at the big picture, they over-focus on wrong metrics or have too many results to analyze that aren’t related to the goal. This is the case when the data hides you the forest.

The “less is o more” is a principle that most of the time you have to follow in the field of data.

When the data resonates

If you follow the data strategy though, your data will resonate. As a quick example, let’s say that we want to increase the number of trials that leads the paywall changing the user’s experience of the paywall’s flow because one of the goals as a company is to increase the MRR.

If we defined correctly the hypothesis and what/how to measure, the data will resonate as we will see a direct correlation between our defined KPIs or models and the goal that we are aiming for.

The data will resonate if there is a direct relationship between the data strategy and the goal that you are aiming for.

In order to make this easier it is important to choose wisely what and how to measure things related to our experiment and of course, how to present the results to the rest of the team.

The follow up

Finally, a proper data strategy needs a follow up. In other words, every insight extracted needs a course of action with an owner in order to get the real value of your data.

If the data is not actionable, your data doesn’t have any value.

Wrapping up

  • Product development needs a data strategy for generating proper value.
  • Spending some time defining a data strategy will provide you a clear direction from all the data mess to the concrete value that you want to extract.
  • The lack of a data strategy leads us to spend x5 or even x10 efforts on each task.
  • The data will resonate if there is a direct relationship between the data strategy and the goal that you are aiming for.

Last but not least:

  • Every insight extracted needs attached a course of action.

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Juan

Juan

Lead Data Scientist 📊 at Bravo Studio. Start-up growth advisor 🚀 He/Him — 🏳️‍⚧️🏳️‍🌈 ally. Opinions are my own