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The Cockpit: Decision Intelligence for Leaders

You cannot manage what you do not measure. Why most dashboards are useless, and how to move from 'Data-Driven' (Looking back) to 'Decision-Intelligent' (Looking forward).

CD
Chloé D.
The Cockpit: Decision Intelligence for Leaders

“We are a data-driven company.” Everyone says this. But when you ask the CEO: “What was your Customer Acquisition Cost (CAC) yesterday?”, they say: “I’ll ask the agency.” When you ask the CMO: “What is the CLV (Customer Lifetime Value) of customers acquired in 2024?”, they say: “I think it’s around $150.” “I think” is not data. “I think” is a hallucination. Most executives fly a $10M jet (their company) with no instruments. They look out the window. If it is sunny (high sales), they are happy. If it is cloudy (low sales), they panic. This article is about building a Cockpit. Real-time, actionable intelligence that allows you to fly through the storm.

Why Maison Code Discusses This

We are developers. We write the SQL queries that power the dashboards. We see the backend of 50 Shopify Plus stores. We see the difference between:

  • Company A: Uses Excel. Updates once a month. Revenue is flat.
  • Company B: Uses a Live Dashboard (Tableau/Looker). Updates every hour. Revenue is growing 50%. We discuss this because Data Latency kills strategy. If you find out you are losing money 30 days later, you are already dead.

1. The 3 Levels of Analytics Maturity

Most companies are stuck at Level 1.

  • Level 1: Descriptive Analytics (What happened?)
    • “Sales dropped 10% yesterday.”
    • This is looking in the rear-view mirror. It is useful for accounting, useless for strategy.
  • Level 2: Diagnostic Analytics (Why did it happen?)
    • “Sales dropped because the ‘Add to Cart’ button broke on Safari.”
    • This is useful. It allows you to fix the problem.
  • Level 3: Predictive/Prescriptive Analytics (What will happen, and what should we do?)
    • “Sales will drop tomorrow because a competitor is launching a sale. We should increase ad spend by 20% to defend market share.”
    • This is Decision Intelligence. This is the goal.

2. The Single Source of Truth (The Warehouse)

Marketing says ROAS is 4.0 (Facebook data). Finance says ROAS is 2.5 (Bank data). Who is right? They are both “right” in their own silo. But the company is confused. You need a Data Warehouse (Snowflake / BigQuery). You pipe all raw data (Shopify, Meta, Google, Stripe) into one place. You define logic once in SQL. Revenue = Gross Sales - Returns - Discounts. Everyone looks at the same dashboard. There is no longer a debate about “Whose number is real?”. The debate shifts to “What do we do?“.

3. Leading vs Lagging Indicators

A Lagging Indicator tells you the result.

  • Revenue.
  • Churn Rate.
  • Net Profit. By the time you see these, it is too late to change them.

A Leading Indicator predicts the result.

  • Site Traffic: Predicts Revenue.
  • NPS (Net Promoter Score): Predicts Churn.
  • Ad Inventory available: Predicts CPM. Strategy: Obsess over Leading Indicators. If Traffic drops today, Revenue will drop tomorrow. Fix the Traffic today.

4. The North Star Metric (NSM)

If you give a team 10 metrics, they will focus on none. You need One Metric That Matters.

  • Spotify: Time Spent Listening (Not App Downloads).
  • Airbnb: Nights Booked (Not Searches).
  • Facebook: Daily Active Users (DAU). For E-commerce, the North Star is often Contribution Margin Dollars. (Revenue - COGS - Shipping - Ad Spend). This represents the actual cash generated to pay overhead. Align the entire company around the North Star.

5. The “Actionable” Test

Every chart on your dashboard must pass the “So What?” Test.

  • Chart: “500 visitors from Brazil yesterday.”
  • So What?: “We don’t ship to Brazil.”
  • Action: “Block Brazil traffic to save server costs? Or open shipping to Brazil?” If a chart does not lead to a decision, delete it. Data without context is noise. We have seen dashboards with 50 widgets. Nobody looks at them. A good dashboard has 5 numbers.
  1. Revenue (vs Target).
  2. CAC (vs Target).
  3. AOV.
  4. Conversion Rate.
  5. Inventory Days.

6. Real-Time vs Batch

“I get a report every Monday.” Monday is too late. Black Friday happens in hours. You need Real-Time Visibility.

  • If a product goes viral on TikTok at 2 PM, you need to know at 2:05 PM.
  • Why? So you can increase ad spend on that specific SKU instantly.
  • So you can call the warehouse to prep for overtime. Speed is the only competitive advantage that cannot be copied.

7. The Democratization of Data

Who has access to the dashboard?

  • Old School: Only the C-Suite.
  • New School: Everyone. Display the Revenue Dashboard on a TV screen in the office. Give the Customer Support agent access to LTV data.
  • “Oh, this customer has spent $5,000 with us? I will refund them instantly without asking a manager.” Data empowers autonomy. If people have the information, they can make the right decision without asking permission.

8. The Human Element (Intuition)

Data is not God. It is a tool. Data cannot tell you to launch a bold new product line. Data would have told Steve Jobs: “Nobody wants a phone without a keyboard.” Use data to Optimize, but use Intuition to Innovate. The dashboard is the cockpit instruments. But the Pilot still flies the plane.

10. The Executive Dashboard (What to Watch)

If you are a CEO, you need 5 numbers on your phone.

  1. Runway (Cash): How many months until we die?
  2. CAC (Acquisition): Performance of marketing.
  3. LTV (Retention): Performance of product.
  4. NPS (Satisfaction): Future growth potential.
  5. Employee eNPS: Happiness of the team. If any of these 5 lights turn red, you wake up. If they are green, you sleep.

11. The AI Analyst (The End of SQL)

In 2024, you needed a Data Analyst to write SQL queries. In 2026, you just ask the AI. “Hey Maison AI, show me sales in Germany vs France for last week, excluding refunds.” The AI writes the SQL, queries the database, and graphs the result. Democratization Level: 100%. Now, the skill is not “Writing SQL”. The skill is “Asking the Right Question”. This is the Golden Age of Decision Intelligence.

12. The Governance Layer (Who owns the definition?)

Marketing defines “Churn” as “Unsubscribed from Email”. Product defines “Churn” as “0 Purchases in 12 Months”. This causes chaos. You need a Data Governance Dictionary. A shared Notion doc that defines every metric.

  • Metric: CAC.
  • Definition: Total Ad Spend / Total New Customers (First Order).
  • Owner: CMO. Without governance, dashboards become “Art” (Open to interpretation). With governance, dashboards become “Law”.

12. The Quarterly Review (QBR)

Dashboards are for daily steering. The QBR is for course correction. Every 90 days, sit down with the 12 weeks of data. Look for macro-trends.

  • “TikTok CPMs increased by 40%.”
  • “Returning Customer Rate dropped by 5%.” This deep dive allows you to reset the targets. Strategy is not static. It evolves with the data. Use the QBR to kill what isn’t working and double down on what is.

13. Conclusion

We are drowning in data but starving for wisdom. Stop collecting more data points. Start building better decision frameworks. The goal is not to have the biggest database. The goal is to make the fastest, most accurate decisions. Build the Cockpit. Fly the plane.

Flying blind?

We build Custom Data Warehouses (BigQuery) and Executive Dashboards (Looker Studio) that define the Single Source of Truth.

Hire our Architects.