tylers-blogger-blog

A visualization strategy isn't "make prettier charts." It's a shared system for turning data into decisions-consistently, quickly, and with fewer meetings where everyone argues about what a metric means. We learned this the hard way. Our early dashboards were a patchwork: different definitions, different chart styles, and a lot of "wait, why doesn't this match Finance's number?" Eventually, we decided to stop shipping one-off dashboards and build a real visualization strategy from scratch . Below is the exact approach we used, including the artifacts we created and the decisions that saved us the most time. 1) Start with decisions, not charts Our first mistake was treating dashboard requests like design tickets: "Add a funnel chart," "Make a cohort view," "Show performance by region." That's backwards. The only reason to visualize data is to support a decision. So we ran a simple workshop (45-60 minutes) with each m...

Data visualization used to feel like a finished product: you picked a chart type, wired up a dataset, and prayed nobody asked for "just one more interaction." We wanted the opposite. We wanted a place where developers could experiment, remix, and extend visuals the same way they do with code-fast feedback, clear interfaces, and zero ceremony. So we rebuilt our visualization stack as a playground : everything scriptable, composable, and safe to break. We treated every chart like a small app (not an image) The first shift was mindset: a chart isn't the output-it's a runtime. That means it needs state, events, and predictable inputs/outputs. We standardized a tiny "viz contract" that every component follows: Inputs : `data`, `schema`, `theme`, `options` Outputs : emitted events like `point:click`, `range:change`, `legend:toggle` Lifecycle : `mount()`, `update()`, `destroy()` Example: instead of hard-coding a tooltip, we exposed an event stream: js viz.on('...