Newsletter Archives

Moving data from one system to another seems simple on paper. But when implemented poorly, your entire analytics operation can collapse. I’ve seen teams spend months rebuilding pipelines that should have taken days to fix. Here are 3 critical components every successful data pipeline needs: Intelligent Alerts When something breaks, you need to know before your stakeholders do. Set up monitoring for pipeline health Create meaningful alerts that explain what failed and why Establish escalation…

Your dashboard projects are failing silently. Here’s why: They focus on one-off problems (should’ve been a simple data pull) They try to do too much at once (executive ambition gone wild) They measure everything but see nothing (classic dashboard ADHD) They track metrics nobody actually uses (requirements failure) They lack the right detail level (context matters) Instead: Target a specific, recurring business problem Build in phased releases (not big bang deployments) Focus on 3-5 critical…

Data models save businesses millions. But no one asks for them. Here’s why that’s a problem… As analysts, we focus on delivering what the business asks for. But sometimes the most valuable deliverables are never requested. Data models are the perfect example. I’ve NEVER been asked to create a data model by non-technical leaders. Yet in almost every analytics project, they’re absolutely essential. The challenge? You’ll never get a dedicated week to build them. So how do successful analysts…

The biggest difference between senior and junior-level individual contributors is how they name things. Seniors name things intentionally and clearly. Juniors name things based on how they feel that day. All the Best, Tucker

You’re already a data manager. (Yes, you.)If you check reports, use software, or make decisions based on forecasts – congratulations, you’re practicing data management.The difference? Some do it intentionally. Most don’t.Unintentional data management looks like: • Unpredictable workdays • Constant firefighting • Frustrated customers • Teams working in silosThis might feel like the status quo. You might even enjoy the chaos.But I promise you this: once you start managing data intentionally,…

At a certain point nearly every company needs to modernize their tech stack. This includes how they manage their data. Because you don’t have time to track down data: When you are shipping thousands of orders a day… Talking to multiple clients at a time… Putting out fires every day… Modernizing your data management stack, and getting off of excel is a necessity not a luxury. All the Best, Tucker

“We do it in Excel” they said. I almost fell out of my chair. 😳 This 3PL was processing 1,000+ orders daily… Managing millions in revenue… And doing their invoicing manually. Here’s why this is dangerous: MANUAL INVOICING RISKS Missing billable activities costs you thousands in revenue Excel errors compound over time, destroying profitability Hours spent reconciling could be used growing your business Delayed invoicing hurts your cash flow THE REALITY For small operations, Excel works…

A bad data strategy costs money. A great one makes it.The question isn’t whether you need one—it’s whether you’re building it right.Here are 5 key ingredients to building a great data strategy…👇1. VISION This is your North Star. Write it down. Make it real. Hold yourself accountable.2. GUIDING PRINCIPLES Healthcare? Privacy first. Logistics? Speed wins. Your principles shape everything.3. BUSINESS-ALIGNED GOALS Think big. Make them ambitious enough to transform your business. Keep them…

A COO told me his company was making about 20 million in revenue without a data team. That’s impressive for a newly founded company. He acknowledged, though, that…. Their billing was done manually There was little to no reporting The owner had concerns about growing Here’s the thing. You can make a lot of money and not spare a second to think about your data. But if you did spare that second, I can guarantee you’ll sleep better at night, spend less time doing mundane tasks, and increase…

Most data projects that fail don’t: Have a long-term plan Have an executive sponsor Deliver a good enough MVP They’re too reactive and not planned out enough. The opposite is also true. You could get stuck in planning session upon planning session. You could have too much executive oversight. You could spend too much time on your MVP. Most of the time, though, the error is having to little rather than having too much. All the Best, Tucker