learning journal #1
on learning about data in product design
Hi friends,
Long time no see. Honestly I didn’t think I would come back to this blog writing something that’s very work-related, given the vibe of work in progress over the past year haha.
Life has been a bit hectic lately (in the best way possible). I’m still with my full-time job, still working out 3 to 5 times a week, meeting beloved friends regularly, learning things here and there. Other than that is the data-driven product design course that I’ve added to my plate. It’s about using data to inform better (more accurate, more intentional) product decisions.
Behind the why
Before we start, I generally am not a numbers person. I do enjoy looking at data and trying to make sense of it, but not in a way I’d like to become a data analyst some day. I still want data to be part of what shapes my decisions – planning for the future and also making sense of the past.
In life, we continuously collect data points about ourselves and our beloved ones to deepen inner and outer relationships, while aiming for more meaningful conversations and interactions.
In the product-making world, companies and product teams use data for various purposes, including but not limited to improving the product and getting closer to the 5-star ranking for their app on the app stores, and, at the end of the day, more people paying for what their products offer.
Working in tech means being in the loop of continuous improvement – or iteration in product language. Yet oftentimes, features and product enhancements are built based on pure intuition, or because “our competitors are doing this”. And product teams end up as the feature factory every now and then, churning out features after features without knowing the real impact placed on the end-user experience.
Coming from a product growth position in a tech company, I’ve always wanted to learn how to connect business goals and product metrics, clearly understand from “what happened” to “why it happened” and “what can we do to move forward with this” – knowing that it’s not enough if I just simply look at the numbers shown on the Google Analytics dashboard or the list of event-tracking requests the team has made to track product adoption and usage. I want to dive deeper on how product data tells the story behind every product iteration and how it helps shape current user growth.
Beginner’s mindset
Start from curiosity, not numbers.
It’s easy to quickly jump into dashboards, try to extract a narrative that feels impressive, or give meaning to the data received – to have something to show to your upper management in weekly reports. But sometimes I really need to pause for a few seconds and say “is this making sense?” in the middle of me reporting those data.
Starting a data course at this time means to unlearn what I’ve known (so little) about data – especially data in building products – while trying to figure out how to incorporate what I learn into the work that I actually do on a day-to-day basis. Knowing how to answer the instructor’s questions in a live session is not equivalent to having thought through everything when I implement my observations and implications towards the data I receive from our data analyst in our team.
Learning this properly means challenging myself to ask better questions about data, better questions when forming hypotheses related to new features being built, plus better questions that ensure the team and I align on mutual goals that help shape the business’ impact & outcome.
Instead of “What numbers do we have”, I learn to start asking “What do I want to understand?” “ or “What change do I want to see?”
Every design should answer the single question: “Em muốn điều gì xảy ra?” - quoted anh Thành, our instructor of the Data-driven Product Design Course.
We product builders easily come to the conclusion of what we should change – sometimes a button’s placement, a twist of UX hierarchy on the screen, or an adjustment of the user flow. Sometimes we jump into the belief that fewer steps means better UX – while that’s not entirely true without clear context and an understanding of the actual outcome given the chosen solution.
Learning about data-informed product design follows me from defining what success looks like to how I will actually measure it.
What data-driven really means
It’s not about having beautified dashboards and complex charts to showcase in your management meetings – it’s about decision clarity.
It’s not about having more data points, yet having data points that serve an intended purpose/goal of your product. That’s where data helps frame better questions and validate intuition.
To this journey of learning how to become more data-informed, I wish myself to keep staying curious, remember to align outcomes, while turning assumptions into meaningful experiments in building products.