Prototyping contributes to every aspect of our design process - from strategy to research, idea generation, testing and trials. We begin building prototypes at the very start of each project - and continually iterate throughout.
We build them to speculate on future data capabilities for ourselves and our clients. We are encouraged to expose ideas, take risks and explore new territories. Our prototypes help us find positive and productive uses for data - but also help us understand its personal, social and environmental impact.
We see our work manifest in the future; new products and services get launched, built with features and insights from these prototypes. But we are largely free from the nitty-gritty spec or production build. Instead, we focus on ideation, speculative thinking and data exploration to arrive at future service ideas. Our clients use our work in multiple ways - as inspiration, as tools and as a platform supporting research trials.
The benefits of prototyping in an R&D capacity at Normally:
- Our clients give us freedom and trust to experiment with their data. They don’t expect a product - they expect knowledge and capabilities - we’re all here to learn together.
- Prototyping is decoupled from launch deadlines. We’re usually exploring a space rather than racing towards a release. This enables us to follow our instincts and spend the appropriate time focusing on each different aspect we uncover.
- We’re not tied to supporting live products. Instead, our long-term relationships are used to build skills, knowledge and capabilities together.
We also build some of our own products, like Cabin - Privacy-first, carbon-conscious web analytics and Toaster API.
We provide our clients with full-stack, end-to-end prototypes built to withstand trials with thousands of real participants: real data, real infrastructure and high-fidelity interfaces. The only caveat is that they are built to explore and learn.
Prototyping at Normally is a blend of data engineering, data science, back-end development, and front-end UI design - with an understanding of how each piece fits together. We manage large volumes of data - spinning up databases, building APIs, training ML models and uncovering insights.
We focus on what is important to communicate an idea. This can alternate from a wide macro business lens down to a specific interaction or an emotional response to the UI.
We often say that we have no process, which raises some eyebrows. In reality, we are experienced enough to understand where to use the best bits from multiple processes and apply them when needed - a form of just-in-time process on the fly. Normally is the platform that gives us the freedom to switch it up at any given moment.
We always design and build in parallel. We make our interfaces look and feel great, but we only use design to communicate our ideas. We don’t spend days comparing fonts or colours. We use our judgement and push forward.
We work in small, autonomous, multi-disciplinary teams - each team member contributes to every stage of the process.
There are also some common interests that we apply to our work.
- Privacy - How can we ensure the data safety of our trial users? How can we make this private by default? What are the personal and social impacts of our work?
- The Environment - How do our data handling and engineering decisions affect the environment? What are the carbon implications? How can we make our work benefit people and the planet?
We love big, thorny problems. It’s what makes us tick. We think prototypers are great at breaking big problems into small solvable challenges. We’re always looking for people who work and think this way. Creative technologists, designers, designer/developers, data scientists and data engineers who can solve big problems using prototyping.
We'd love to hear from you if you enjoy using ANY of these technologies:
Job openings at Normally: