Who This Helps
If you're a Team Lead trying to scale a repeatable analytics routine, this is for you. The Metrics & Dashboards Basics course shows you how to build a system that supports calm, weekly decisions instead of noisy data debates.
Mini Case
Maya's team was tracking over 20 different numbers. Every planning meeting turned into a 90-minute argument about which metric mattered most. She built a simple weekly scoreboard focused on their North Star and three supporting metrics. In 4 weeks, they cut meeting time in half and increased their experiment win rate from 25% to 40%. They stopped guessing and started knowing.
Do This Now (5 Steps)
- Pick your one North Star. From all the numbers you track, choose the single metric that best reflects your core value. Define it clearly so everyone agrees.
- Choose three supporting metrics. These are your guardrails. Pick one for growth, one for quality, and one for efficiency. Give each a realistic target.
- Build your weekly scoreboard. This is your main dashboard. It should show your North Star, your three supporting metrics, and their weekly trend. Keep it to one screen.
- Schedule a 20-minute weekly review. Every Monday, look at the scoreboard with your team. Did we go up, down, or stay flat? Why?
- Decide on one experiment. Based on the scoreboard, pick just one thing to test this week to move the needle. Write it down.
Avoid These Traps
- Don't try to track everything. More data leads to more confusion, not more clarity.
- Don't skip the weekly review. Consistency turns data into a habit.
- Don't let perfect be the enemy of good. A simple, ugly scoreboard you actually use is better than a beautiful one you ignore.
- Don't change your core metrics every month. Give them time to tell a story.
- Don't debate in meetings without the scoreboard open. Let the numbers do the talking.
Your Win by Friday
By this Friday, you'll have a draft of your weekly scoreboard. You'll walk into your next team sync knowing exactly what to discuss. You'll leave that meeting with one clear, high-impact experiment to run, and your team will know why it's the priority. Your data routine just became a decision engine. Nice work.