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...

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...

We didn't set out to "build a data team ." We set out to stop arguing in meetings. At the time, every big decision sounded like this: * Marketing: "Leads are up, so the campaign worked." * Sales: "Pipeline is up, but close rates are down." * Product: "Activation is fine-people love the new onboarding." * Finance: "Revenue isn't tracking. What's going on?" All of these could be true. Or none of them. The real problem wasn't people-it was that we didn't have shared definitions, a reliable source of truth , or a repeatable way to learn. This is the story of how we went from gut-feel debates to a data-driven dream team-without turning everyone into analysts, and without drowning in dashboards. Step 1: We stopped chasing "more data" and agreed on the questions The first breakthrough wasn't a tool. It was a whiteboard session. We gathered stakeholders from Product, Marketing, Sales, and Customer Success and a...

You've heard it: 'Run AI locally for privacy!' You install that sleek offline chatbot for your business, convinced you're avoiding big tech's data grabs. You're safe, right? Wrong. That 'local' AI is quietly building a fortress around your data-and it's making your business slower, less insightful, and even riskier. Think of it like having a locked filing cabinet in every department: HR has employee wellness data, sales has customer preferences, but no one can share or see the big picture. Your local AI might be private, but it's also useless for real decisions. I've seen a healthcare startup waste months because their 'private' patient symptom tracker couldn't connect to the local lab results AI-leading to duplicate tests and frustrated patients. That's the hidden cost : privacy sold at the price of functionality. You're not just hoarding data; you're creating a maze where insights get lost. The real privacy win isn...