tylers-blogger-blog

Most "data strategies" are built like a museum: collect everything, label it nicely, and hope someone strolls through and discovers insight. The contrarian view is simpler-and more uncomfortable: your data strategy is not a storage strategy, not a dashboard strategy, and definitely not a "we should track more things" strategy. A better data strategy is a set of deliberate constraints: what you will measure, what you won't, which decisions you want to accelerate, and where you'll tolerate ambiguity. That sounds like less data, not more. And that's the point. The mistake: treating data like an asset you should hoard Here's the common pattern: 1) A leader says, "We need to be data-driven." 2) Teams instrument everything "just in case." 3) A warehouse fills up, a BI tool goes live, dashboards multiply. 4) People still argue in meetings-now with screenshots. The quiet reason this fails: hoarding data doesn't create clarity; it crea...

The 2 a.m. Moment We were minutes from pushing a release when support lit up: "Checkout failures spiking." Logs were noisy, metrics were contradictory, and everyone had a different theory. In the old days, we'd have argued for an hour while grepping files and refreshing random queries. That night, we did something better: we stopped talking and started looking. What Actually Saved Us We pulled up a dashboard that layered error rate, latency, and revenue impact on the same timeline. The story snapped into focus: the spike only hit one region, only after a config rollout, and only for a specific payment provider. A quick heatmap by endpoint pointed to a single route returning 502s. Then a simple filter showed the failures clustered around a new feature flag. Five minutes later, we rolled back the flag and watched the line drop in real time. If you're still choosing your stack, this breakdown of popular visualization tools mirrors the exact tradeoffs we ran into that ni...

If you've spent any time in modern data circles, you've heard the default advice: "Just stand up a warehouse." It's not bad advice-just over-prescribed. Building without a data warehouse can be a perfectly rational choice when your product is young, your data questions are narrow, and your team needs speed more than an immaculate semantic layer. This guide is not "never use a warehouse." It's: don't let a warehouse become your first reflex. 1) Start with the question, not the architecture A data warehouse is an answer to a specific set of problems: cross-domain analysis, consistent definitions, historical tracking, ad hoc slicing, and scaling read-heavy workloads. If you don't have those problems yet, you might be buying complexity early. A simple litmus test: if 80% of what you need is "show me what happened in the product yesterday" or "send a lifecycle email when X happens," you can often do that faster with operational...

Building a community-driven product is messy-in a good way. People post at odd hours, content spikes unexpectedly, and "success" can mean anything from more thoughtful replies to fewer mod escalations. When we started, the common advice was: "Set up a data warehouse early." We didn't. Instead, we asked a simpler question: what decisions do we actually need to make in the next 90 days, and what's the minimum data setup that supports those decisions? Why we skipped the warehouse (on purpose) We weren't anti-analytics. We were anti-premature analytics infrastructure. At our stage, a warehouse would have added (1) extra pipelines to babysit, (2) new failure modes, and (3) a lot of "maybe someday" data modeling. We had two engineers and a growing community-our bottleneck was shipping improvements and responding to users, not running cross-domain BI. So we defined a "decision list," not a "dashboard list." Examples: Are new member...