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...

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...

We didn't call it a "data apocalypse" at the time. We called it "that weird spike," "the numbers are off again," and-my personal favorite-"let's just refresh and see if it fixes itself." Then came the day every metric disagreed with every other metric. Revenue looked down 22%. Signups looked up 40%. Paid CAC doubled. And the CEO asked the sentence that makes analytics teams age ten years instantly: "Which one is right?" The problem wasn't one bug. It was a chain reaction: a tracking change shipped late Friday, a backfill job that silently timed out, a dashboard built on top of a view built on top of a sheet someone "temporarily" edited. We were one Slack message away from deleting something important just to stop the bleeding. The Apocalypse: When Manual Analytics Collapses Here's what the failure looked like in practice: Multiple sources, one metric name: "Leads" meant "form submits" in on...