Optimize vs Monitor

# Optimize vs Monitor A leading indicator changes *before* the outcome you care about. A lagging indicator changes *after*. You optimize leading indicators; you monitor lagging ones. ## Rolling time-series monitoring in Python ```python import pandas as pd import numpy as np # Daily time series tracking leading activation vs lagging ARR dates = pd.date_range(start="2026-01-01", periods=60, freq="D") np.random.seed(42) daily_activations = np.random.poisson(lam=120, size=60) + np.linspace(0, 40, 60) # ARR lags activations by ~14 days daily_arr_gain = np.roll(daily_activations * 50, 14) + np.random.normal(0, 200, 60) df_metrics = pd.DataFrame({"date": dates, "activations": daily_activations, "arr_gain": daily_arr_gain}) df_metrics["activations_7d_ma"] = df_metrics["activations"].rolling(7).mean() df_metrics["arr_gain_7d_ma"] = df_metrics["arr_gain"].rolling(7).mean() print("Latest 5 days of Leading vs Lagging Monitoring:") print(df_metrics[["date", "activations_7d_ma", "arr_gain_7d_ma"]].tail(5).to_string(index=False)) ``` ## Takeaway Place leading indicators at the top of your weekly operating dashboard; place lagging financial metrics at the bottom.